WELCOME, EVERYONE.
WE'RE CELEBRATING TODAY.
I GUESS WE'RE GETTING USED TO
BEING BACK UP PERSON TODAY.
WE WANT TO THANK YOU ALL FOR
COMING AND BATTLING THE
TEMPERATURES AND PLANES AND
SUMMER ACTIVITIES AND COMING.
IT'S AMAZING YOU'RE COMING ON
YOUR OWN.
WE'RE NOT EVEN FORCING YOU.
MY NAME IS GRACE PENG.
ONE OF THE CO-CHAIRS OF THE
INTERAGENCY MODELING AND
ANALYSIS GROUP AND WE'RE
CELEBRATING THE GROUP CALLED
IMAG TODAY.
HERE AT THE TABLE WE HAVE MY
TRUSTED CO-CHAIRS, REED AND LIZ.
YOU WANT TO SAY HELLO.
>> HELLO.
WELCOME.
>> NICE TO SEE YOU ALL.
>> AND WE HAVE THE WONDERFUL
ORGANIZERS.
FIRST WAS LIZ AND REED.
THANK YOU.
OKAY.
SO WE HAVE OUR TRUSTED
CO-CHAIRS.
I AM SO HAPPY TO HAVE LIZ AND
REED CO-CHAIRING IMAG WITH ME
NOW.
AND THEY REALLY ARE THE ONES
THAT CAME IN AND MADE THIS WHOLE
THING HAPPEN WITH ME.
SO WE ARE ALSO VERY HAPPY TO
HAVE THE PLANNERS, THE MAIN
ORGANIZERS, HERBERT, DO YOU WANT
TO GO DOWN THE LINE, HERBERT,
DENISE, JASON AND BRUCE.
>> HELLO.
IT'S WONDERFUL TO BE HERE TODAY.
EVEN THOUGH I GOT UP AT 3:00 IN
THE MORNING I'M EXCITED TO BE
HERE AND TIMELY COVID IS OVER
AND WE CAN START MEETING EACH
OTHER AGAIN.
>> DENISE KIRSCHNER.
I'M HAPPY TO BE HERE.
I REALIZE COMING TO NATCHER FOR
A VERY LONG TIME.
IT FELT GOOD AFTER SO MANY YEARS
OF COVID, ETCETERA, TO BE BACK.
I'M REALLY EXCITED ABOUT THE
MEETING AND SEEING EVERYBODY.
>> I'M JASON HAUGH.
HAPPY TO BE HERE.
[OFF-MIC]
I USE THE HASH TAG IMAG PAST
FUTURE WHEN POSTING ON MEDIA.
>> IT SOUNDS LIKE THE MEDIA CAST
IS ON.
WELCOME TO THE REST OF THE
WORLD.
THANK YOU FOR JOINING US.
WE STARTED A BIT LATE TODAY
BECAUSE OF SOME TECHNICAL
DIFFICULTIES BUT WE'RE GOING
FULL FORCE NOW.
THIS MEETING WAS PLANNED BY A
LOT OF PEOPLE OVER THE PAST YEAR
AND WE HAVE BEEN ON MANY MANY
MANY ZOOM CALLS AND I TRIED TO
CAPTURE THAT ABOVE US INCLUDING
MANY OF YOU IN IMAGO AND MSM AND
ALL OF YOU SAID WE REALLY WANT
TO MEET AGAIN IN PERSON.
WE DON'T WANT TO MEET ON ZOOM.
SO THIS IS WHAT WE HAVE AND BY
BRUCE'S ADVICE WE SAID WE'RE
GOING TO MEET IN JUNE JUST TO
MAKE SURE THE PANDEMIC IS OVER.
BECAUSE PANDEMIC MODELERS DON'T
WANT US TO COME IF THERE'S A
PANDEMIC.
MANY THANKS TO THE CO-CHAIRS,
THE SESSION CHAIRS LISTED HERE
AND THE IMAG ORGANIZERS.
AS WELL AS THE WONDERFUL FOLKS
WHO HELPED US REJUVENATE THIS
WIKI AND OUR WONDERFUL MEETING
SUPPORT CONTRACTORS AND SPECIAL
THANKS TO THE NIBIB FOR
SUPPORTING THIS MEETING AS WELL
AS THE CENTER FOR COMPLEMENTARY
AND INTEGRATIVE HEALTH, NICCH
AND REED IS FROM THE NATIONAL
INSTITUTE OF ALLERGY AND
INFECTIOUS DISEASES.
AND MANY THANKS TO NSF FOR
PRINTING OUT THE POSTERS AND
THANK YOU TO THE SOCIETY FOR
MATHEMATICAL BIOLOGY.
IF IT WEREN'T FOR THEM YOU
WOULDN'T HAVE NO FOOD AT ALL IN
THE BUILDING SO THANK YOU.
HOUSE RULES, THERE'S GOING TO BE
FOOD OUTSIDE.
BEVERAGES OUTSIDE.
YOU CANNOT HAVE FOOD OR DRINK IN
HERE.
AND IN THE AFTERNOON YOU'LL HAVE
REFRESHMENTS UPSTAIRS IN THE
ATRIUM.
YOU'LL PICK UP THE LUNCH BOX
OUTSIDE OF THE AUDITORIUM.
IT WILL BE LAID OUT ON THE
TABLES.
TODAY YOU CAN HAVE LUNCH
ANYWHERE EXCEPT THE AUDITORIUM
EXCEPT IT'S CODE ORANGE.
THERE ARE PICNIC TABLES OUTSIDE.
TOMORROW WE'LL HAVE A WORKING
LUNCH WITH A BREAKOUT SESSION.
I WANT YOU ALL TO LOG ON TO THE
IMAG WIKI, EVEN THOSE ONLINE.
USE THE QR CODE AND LOG INTO THE
WIKI.
WE'D LIKE YOU TO FOLLOW THE
INTERACTIVE AGENDA CLOSELY.
CLICK ON TO THE SESSION PAGES.
YOU CAN THEN ENTER IN YOUR
COMMENTS.
SO IF YOU DID EVERYTHING RIGHT,
YOU LOGGED IN, CLICK ON CURRENT
IMAG MEETING TAG, YOU'LL GET TO
THIS PAGE AND ON THIS PAGE YOU
WILL SEE THE AGENDA.
CLICK HERE AND YOU'LL SEE THE
SESSION LINKS.
IF YOU ARE AN EARLY CAREER
INVESTIGATOR, WE ARE
HIGHLIGHTING YOU BECAUSE YOU ARE
OUR FUTURE.
AND WE WANT YOU TO USE THESE
PAGES TO HIGHLIGHT YOUR WORK AND
FIND YOUR LAST NAME AND CLICK ON
IT AND ENTER IN YOUR
PUBLICATIONS, YOUR DREAMS, YOUR
INTERESTS.
CLICK ON THE POSTER LINK TO FIND
ALL THE POSTERS.
YOU CAN DOWNLOAD THE ABSTRACTS
AS WELL AS THE FULL POSTERS.
YOU CAN DO YOUR HOME WORK BEFORE
YOU GO TO THE POSTERS SO YOU
KNOW WHAT QUESTIONS TO ASK.
YOU CAN CLICK ON THE ATTENDEE
LISTS AND TYPE IN YOUR NAME AND
SEARCH FOR YOUR MENTOR OR YOUR
EARLY-CAREER INVESTIGATOR YOU'RE
MENTORING.
ON THE LOGISTICS PAGE WE HAVE
INFORMATION ON THE INFORMAL
CHATS ON SLACK AND TWITTER.
THANK YOU FOR MONITORING THE
SLACK.
FINALLY, IT'S NOT JUST LATE IF
YOU HAVEN'T SUBMITTED YOUR
MATERIALS YET, CLICK ON THE
SUBMIT MATERIALS AND SUBMIT YOUR
PDFs SO PEOPLE CAN KEEP IT AND
IF YOU LOOK ON THE WIKI, WE HAVE
ALL OUR MEETINGS SAVED LIKE THIS
SINCE 2006.
PLEASE DO THAT.
YOU'LL LEAVE A LEGACY STARTING
NOW.
OKAY, I'LL START WITH THE BELL.
REMEMBER THE BELL?
WE DON'T HAVE THE FLASHING TIMER
ON THE PODIUM BECAUSE IT'S NOT
WORKING.
SO IF YOU HEAR ONE BELL RING
THAT MEANS ONE MINUTE LEFT.
IF IT'S TWO BELLS -- IT MEANS
PLEASE STOP.
AND IN THE PAST PEOPLE ARE
RACING AND USUALLY WE DON'T GET
TO TWO BELLS.
I WANTED TO LET YOU KNOW.
I'M HERE TO TALK BRIEFLY ABOUT
THE HISTORY IN THE 20 YEARS.
AND SOMEBODY TIME ME -- REED,
YOU CAN -- YOU CAN DO THE BELL
ON ME.
HOW DID THIS ALL START?
I'LL WALK US DOWN MEMORY LANE A
LITTLE BIT.
IMAG WAS CREATED IN DECEMBER
2002 I CAME TO THE NIH AND SAID
WHAT ARE WE GOING IN MODELLING
AT THE NIH?
I PRESENTED IT TO A GROUP AND
THEY SAID, WELL, LET'S GET
TOGETHER A GROUP OF PEOPLE AND
TALK ABOUT IT.
THAT'S WHAT NIH PEOPLE DO IS
THEY CREATE GROUPS.
IN APRIL 8, 2003 WE FORMED
GROUP.
THE NSF FOLKS, STEPHANIE, GEORGE
AND OTHERS IN THE ROOM WERE THE
FIRST TO APPROACH US AND SAID
LET'S DO MULTISCALE MODELLING
AND THERE'S A LOT OF NIH AND NSF
FOLKS AND WE CAME TOGETHER AND
FORMED IMAG.
WE DECIDED IT WOULD BE A
GRASSROOTS MOVEMENT AND WE WOULD
NOT FORCE ANYONE TO JOIN AND
PEOPLE WOULD STAY IF THEY WANTED
TO STAY AND DO MODELLING ABOVE
AND BELOW THE SCALES OF
INTEREST.
THIS HAPPENED BECAUSE AT THE
SAME TIME MANY THINGS WERE
HAPPENING.
1999 THERE WAS A REPORT AND
THERE WAS A BSTI SYMPOSIUM.
MY OWN INSTITUTE WAS FORMED IN
2000.
THAT'S WHY I JOINED NIH.
WE FORMED IMAG IN 2003 AND
BECAUSE OF OUR INTEREST IN IMAG
WE QUICKLY FORMED OUR FIRST
FUNDING OPPORTUNITY IN 2004 AND
FUNDED IN 2005 AND IN 2007 WE
HAD ANOTHER INITIATIVE.
SO THAT'S THE START OF OUR
HISTORY.
OF COURSE PETER HUNTER AND HIS
PAPER IN 2003 CAME OUT WITH HOW
THE SCALES OF THE HUMAN BODY
SHOULD BE THOUGHT OF IN TERMS OF
THE SPATIAL AND TEMPORAL SCALES
FROM GENES TO PROTEINS TO CELLS,
TISSUES, ORGANS AND CLINICAL
MEDICINE.
PETER TALKED AT THE INAUGURAL
BSTI MEETING TOO.
SINCE THEN WE'VE BEEN MOVING
THINGS TO THE POPULATION LEVEL
AS WELL.
IF ALL OF YOU REMEMBER, WE HAD
RON WHITE WITH US IN IMAGO AND
BACK THEN HE CREATED THIS VISION
WHERE RON WHITE HAD WHERE WE
WOULD HAVE A FOUNDATIONAL
DEVELOPMENT HERE AND THESE
PILLARS WOULD BE THE
APPLICATIONS OF THE FOUNDATIONAL
MODELS.
THE FOUNDATIONAL INFRASTRUCTURE
NEEDED TO ULTIMATELY CREATE A
DIGITAL HUMAN.
THIS WAS HIS VISION BACK IN
2005.
EVENTUALLY ADDRESS THE NEEDS OF
SOCIETY.
THINK ABOUT THIS AND SEE WHERE
WE'RE AT NOW.
THIS WAS WAY BACK.
SO, IMAG IS THE GOVERNMENT GROUP
OF FUNDING AGENTS AND MULTISCALE
MODELLING ARE THE RESEARCHERS.
TOGETHER IMAGO AND THE
MULTISCALE CONSORTIUM ARE THE
PARTS AND WE LIVE ON WIKI.
AFTER WE FORMED THE CONSORTIUM
WE HAD SEVERAL MEETINGS ALL THE
WAY UP TO NOW AND SO THIS SLIDE
SORT OF DESCRIBES THE EVOLUTION
OF WHERE THE MULTISCALE
MODELLING CONSORTIUM WAS
THINKING.
YOU GUYS LOVED MEETING IN THE
BEGINNING AND WERE AMBITIOUS
READY TO PLAN THE FUTURE.
WE HAD A MEETING OFF THE BAT IN
2009.
AFTER THAT WE DISCUSSED
DISPARATE MODELS AND TALKED
ABOUT MODULEARTY AND GLOBAL
EFFORTS WERE STARTING IN EUROPE
AND JAPAN AND STARTED PARTNERING
WITH THE NATIONAL INSTITUTE OF
HEART, LUNG AND BLOOD AND THEY
HAD THEIR SYSTEMS BIOLOGY FOLKS
COMBINE WITH US AND WE TALKED
ABOUT SIMPLE AND COMPLEX SYSTEMS
AND VARIOUS BIOLOGICAL DOMAINS.
IN 2012 THE PRECISION MEDICINE
REPORT CAME OUT AND WE SPENT THE
ENTIRE MEETING COMBING THROUGH
THE REPORT AND DISSECTING IT IN
EVERY ONE OF OUR SESSION.
ALL THESE ARE ON THE WIKI, BY
THE WAY.
MANY THANKS TO ANDREW McCULLOUGH
FOR COMPILING THIS MASSIVE
REPORT IN 2012 FROM OUR
CONSORTIUM DISCUSSIONS.
IN 2013 WE PLANNED A WONDERFUL
MEETING HAVING PEOPLE IN THE
CONSORTIUM HOST DIFFERENT THEMES
AND THEN THE GOVERNMENT WAS SHUT
DOWN.
SO IF YOU REMEMBER, SOME OF YOU
EVEN FLEW HERE AND WE COULDN'T
TALK TO YOU.
THEN WE REPLICATED EXACTLY THE
SAME MEETING AND JUST HELD IT IN
2014.
THEN IN 2015 WE STARTED TALKING
ABOUT SUCCESS STORIES AGAIN.
WE RECOGNIZE WE NEED TO STILL
CONVINCE THE COMMUNITY ABOUT THE
IMPORTANCE OF MODELLING AND THE
WAY TO DO IT IS THROUGH SUCCESS
STORIES AND WE ALSO THOUGHT THE
ONLY WAY TO THE FUTURE IS
THROUGH NEW AWARDEES AND WANTED
TO START ABOUT NEW AWARDEES AND
TALKING ABOUT MEDICAL SIMULATORS
AND GOT IN THE GRANT PROCESS HOW
PEER REVIEW IS DONE AND WE
CELEBRATED AN ANNIVERSARY AND WE
ADDED MEDICAL DEVICES IN THE
MIX.
HERE ARE THE DIFFERENT POSTERS
YOU CAN SEE OUTSIDE AS WELL.
THIS ONE POSTER YOU CAN SEE DOWN
HERE AND WE'RE GOING TO TALK
ABOUT IT A LITTLE BIT IN THE
REMEMBRANCE SESSION.
THIS IS WHERE WE PARTNERED WITH
THE SYSTEMS BIOLOGY FOLKS AND
THIS IS OUR 10th ANNIVERSARY
POSTER OF THE MULTISCALE
MODELLING CONSORTIUM.
DON'T FORGET TO SIGN OUR POSTER
THIS YEAR TO LEAVE YOUR PARK ON
THE POSTERS FOR THIS YEAR.
IN 2018 WE ACTUALLY HAD ANOTHER
FUTURES MEETING.
AND THAT'S WHERE I TOOK THIS
WONDERFUL PICTURE OF DENISE AND
JENNIFER LINDERMAN AND SILVIA
AND PEARCE AND THEY DID AN
AMAZING TALK HOW THEY IMPLEMENT
CREDIBLE PRACTICES IN MODELLING.
THEN IN THEN IN 2019, BRUCE, ON
THE STAGE HERE AND JEFF HOLMES
HOST THE 2019 MEETING WHERE WE
REALLY FOCUSSED ON TRANSLATING
MODELS TO DIFFERENT DOMAIN AREAS
AND IT WAS AN AMAZING MEETING
AND MY OWN DIRECTOR IT WAS HIS
VERY FIRST MEETING AND HIS
INTRODUCTION TO OUR GROUP.
WE HAD THAT MEETING IN THE
SPRING.
AT THAT MEETING TWO PEOPLE SAID
WE REALLY NEED TO START TALKING
ABOUT MECHANISTIC MODELLING AND
MACHINE LEARNING.
THEY WENT ACROSS THE HALL TO THE
SCHEDULERS AND SAID CAN WE HAVE
THE MEETING IN THE FALL.
FROM SPRING TO FALL THEY PLANNED
THE NEXT MEETING AND IT WAS
BASICALLY A CONTINUE OF OF --
TSUNAMI OF IDEAS AND WE HELD THE
MEETING AND THE PAPER WAS
PUBLISHED A MONTH LATER.
THAT WAS AMAZING.
THIS MEETING WAS FOCUSSED ON
PATIENT SAFETY AND DIGITAL TWINS
AND THEN THE PANDEMIC HIT.
SHANE AND SILVIA, HOWEVER, WERE
PLANNING AN AMAZING MEETING FOR
THE NEXT YEAR.
A 2020 MEETING.
LITERATELY THE WEEK THE PANDEMIC
HIT.
UNFORTUNATELY THE PANDEMIC HIT
AND WE HAD TO TRUNCATE THE
MEETING TO VIRTUAL AND WE
ACTUALLY HELD PRIZE COMPETITIONS
FOR MODELLING TRAINEES AND WE
HAD SPONSORSHIPS FROM THE FALL G
ELLEN FRONTIERS GROUP WHO GAVE
PRIZES AND IT WAS STILL AMAZING.
THEY PLANNED OUT THE ENTIRE
MEETING.
WE THOUGHT THE PANDEMIC WILL BE
OVER THE FOLLOWING YEAR AND
WE'LL HOLD THE MEETING THEY HELD
IN 2021 BUT IT NEVER HAPPENED.
THE PLANNING COMMITTEE WAS
LIKE -- THEY WERE HAVING THE
VERY GOOD FORESIGHT OF PLANNING
A MEETING TO FOCUS ON DIVERSITY.
THIS WAS IN 2019.
AND IF YOU GUYS KNOW WHAT WE'RE
NOW, EVERYTHING AT NIH IS ABOUT
DIVERSITY AND THEY WERE WAY
AHEAD OF THE GAME.
SO EVERYTHING THEY THOUGHT ABOUT
BACK THEN IN TERMS OF DIVERSITY
OF PEOPLE AND DIVERSITY OF
METHODS AND PEDAGOGY IS ALL
BEING TALKED ABOUT NOW.
THOUGH THEY DIDN'T HOLD A
MEETING SOMEHOW IT MOTIVATED
PEOPLE.
AND THEN I JUST WANTED TO TALK
ABOUT A FEW SUCCESS STORIES THAT
I TELL OTHER PEOPLE ABOUT WHICH
IS WHY WE HAVE A SESSION ON
CREDIBLE PRACTICES IS THAT THE
10 SIMPLE RULES THAT COME OUT OF
THE MSM IS A SUCCESS STORY.
DURING THE PANDEMIC, THE WALL
STREET JOURNAL CALLED US AND CDC
AND SAID WHAT'S GOING ON WITH
THESE MODELS?
NOBODY BELIEVES THEM.
WHAT SHOULD WE DO?
JERRY MYERS WHO UNFORTUNATELY
CAN'T BE IN PERSON HERE WILL BE
IN THE SESSION WHERE HE HAD
INTRODUCED HOW NASA AND LA LA
MALAGETTA START THE CONVERSATION
INTRODUCING HOW NASA IMPLEMENTED
THEIR CREDIBILITY PRACTICES
AFTER THE CHALLENGER TRAGEDY AND
SO THAT'S WHERE OUR SIMPLE RULES
GOT STARTED.
WHEN WE STARTED TALKING WITH
DARPA AND STARTED TRYING TO
APPLY THE RULES TO THE COVID-19
MODELS.
THEN THE VIRAL PANDEMIC WORKING
GROUP WAS FORMED.
IF YOU'RE FOLLOWING THE WIKI AND
JAMES GLASER AND JIM SLUKA AND
TOM HELKERS EFFORTS ARE MEETING
EVERY WEEK AND STILL MEETING
EVERY WEEK TALKING ABOUT VIRAL
PANDEMICS.
IT'S PRETTY AMAZING THAT THEY
HAD AN AMAZING AMOUNT OF PEOPLE
COMING TO THEIR WORKING GROUP
DURING THE PANDEMIC MEETING AND
HAVING I THINK 30 SUBGROUPS OR
SOMETHING REED FOLLOWED CLOSELY
FROM NIAID.
THIS IS OUR CONSORTIUM NOW.
WE HAVE IMAGO AND THE MSM.
YOU'RE NOT RINGING THE BELL --
AND SO WE'VE MOVED ON TO SOCIAL
ECOLOGICAL SYSTEMS AS WELL.
THE QUESTION I WANT TO LEAVE YOU
ALL WITH IS WHERE DO WE GO FROM
HERE?
I USUALLY TOUT OUR CONSORTIUM AS
THE MECHANISTIC MULTISCALE
MODELLING CONSORTIUM ESPECIALLY
NOWADAYS WITH DATA-DRIVEN MODELS
EVERY.
WHERE DO WE STAND?
I LIKE TO POSE THESE QUESTIONS
FOR YOU GUYS TO ANSWER IN THE
BREAKOUT SESSIONS.
ARE WE READY FOR A BIG PROJECT?
EXCUSE ME.
CAN WE HARMONIZE MSM TOOLS AND
BENCHMARK DATA?
DO WE HAVE A FUTURE CO-CHAIR?
POST ON THE WIKI WHERE YOU WANT
TO GO.
TODAY AS THE DAY.
WE'RE PITCHING IDEAS FOR THE
BREAKOUT SESSIONS.
THANK YOU, DENISE.
[COUGHING]
TODAY IS QUESTIONS AND LESSONS
AND FUTURE DIRECTION AND I'LL
NOW INTRODUCE DENNIS KIRSCHNER
AND PULL UP HER TALK.
DENISE WE CHOSE AS A KEY NOTE
BECAUSE WE THOUGHT SHE'D DO A
WONDERFUL JOB SHOWING US WHERE
WE WERE AND WHERE WE WANT TO GO
PUTTING SOME MEAT ON TO WHAT I
SAID.
DENISE IS ONE OF THE ORIGINAL
MSM AWARDEES TOO.
>> I'LL TELL YOU A STORY WHILE
THEY BRING UP MY TALK.
WE WERE GETTING CALLS ABOUT THE
MEETING IN GENERAL.
AND WE WERE HAVING WEEKLY
MEETINGS FOR SIX MONTHS WITH
THIS TEAM AND I MISSED ONE
BECAUSE I WAS AT A MEETING IN
LONDON AND WHEN I CAME BACK THEY
SAID OH, BY THE WAY YOU'RE THE
PLENARY AND WOULD BE THE BEST
PERSON TO REPRESENT US SO DON'T
EVER MISS A MEETING BECAUSE YOU
GET NOMINATED.
SO THE TITLE OF MY TALK IS
MATHEMATICAL MULTISCALE
MODELLING IS LIFE.
FOR THE PAST 20 YEARS IT HAS
BEEN MY LIFE AND IF YOU KNOW TED
LASSO, THIS IS PARAPHRASED FROM
DANNY ROJAS WHO OFTEN SAYS
FUTBOL IS LIFE.
FOR ME AND MY COLLEAGUE JENNA
LINENMAN A LOT OF MY TALK IS
JENNIFER AND MYSELF AND THE WORK
WE'VE DONE IN THE PAST 20 YEARS
AS PART OF THE MULTISCALE
MODELLING AND YOU CAN GO DOWN TO
PROTONS AND DRILL NORTH AND THEN
I LIKE TO TALK ABOUT EAST-WEST
WHICH IS OTHER ORGANS OR
COMPARTMENTS ON THE SAME SCALE.
AND THIS IS WHERE WE LIVE
WESTBOUND -- WITHIN THE BODY
AND POPULATION.
THERE'S OF FEEDBACK.
SORRY.
I DECIDED TO TALK IN GENERAL
ABOUT THINGS WE LEARNED IN THE
PAST 20 YEARS AND COME IN
QUESTIONS AS WE TRY TO ADDRESS,
HOW DO WE BUILT THE SCALES AND
CALIBRATE MULTISCALE MODELS AN
SIMULATE THEM AND ENSURE THEY'RE
VALIDATE AND USEFUL AND WHERE DO
WE GO NEXT?
THIS IS THE WAY WE TALKED TODAY
AND YOU WON'T HEAR ABOUT
GRANULOMAS FOR THE FIRST TIME
TODAY JUST HIGH LEVEL.
SO HOW DO WE BUILD MODELS?
REMEMBER, THIS IS THE WORLD
ACCORDING TO US.
OTHERS MAY FEEL DIFFERENTLY BUT
THESE ARE THE LESSONS WE HAVE
LEARNED OVER THE PAST 20 YEARS.
THERE'S A COUPLE WAYS PEOPLE
THINK ABOUT THIS.
YOU CAN BUILD MODELS FROM THE
TOP-DOWN OR GO AT THE LOWEST
SCALE AND BUILD UP.
ONE THING WE HAVE DONE IS DONE
WHAT WE CALL MIDDLE OUT OR MESO
SCALE WE START AT THE SCALE OF
MOST INTEREST WHICH FOR US IS
THE GRANULOMA THAT DEVELOPS
DURING TUBERCULOSIS DISEASE AND
BUILD THE SCALE BECAUSE THAT'S
WHAT WE'RE MOST INTERESTED IN.
WHAT MODELLING APPROACH IS MOST
USEFUL?
WELL, I THINK IT DEPENDS ON THE
BIOLOGICAL SCALE OF INTEREST.
IF YOU START WITH A MODEL ON THE
GENETIC SCALE, THEN YOU HAVE
TIME AND LENGTH SCALES THAT HAVE
VERY SHORT AND SHORT TIME SCALES
AND FAST TIME SCALES.
MAY NEED SOMETHING DETERMINISTIC
OR CONTINUOUS OR MAY NEED A
STATISTICAL MODEL AND SO ON.
WE DID A STUDY AND ASKED
OURSELVES WHAT'S THE MOST
APPROPRIATE METHOD TO USE THE
GRANULOMA STRUCTURES AND USED
ODEs COMPARTMENTS.
WE TRIED A PDE TO SEE IF WE CAN
UNDERSTAND THE GROWTH.
WE LOOKED AT A METAPOPULATION
MODEL WHICH IS BEFORE THERE WAS
SUCH A THING AS AGENT-BASED
MODELLING AND THEN MOVED TO
AGENT-BASED MODELLING ONCE
OBJECT ORIENTED PROGRAMMING WAS
DEVELOPED.
WE WROTE A PAPER IN 2005 WHERE
WE ACTUALLY COMPARED THE
DIFFERENT APPROACHES AND ASKED
WHAT CAN WE LEARN FROM EACH AND
WHICH MIGHT BE THE MOST
APPROPRIATE FOR THE QUESTIONS
WE'RE ASKING.
SO ONCE YOU BUILD MODELLING, THE
QUESTION IS IF YOU ARE GOING TO
EXPAND TO MULTI-SCALE HOW LINK
THEM AND EASY TO BUILD A MODEL
OF THE POPULATION AND MAYBE A
GRANULOMA BUT HOW DO YOU CONNECT
THE MODELS.
IT'S NOT SUCH A TRIVIAL
QUESTION.
THERE'S LOTS OF ISSUES ABOUT
LINKING.
THERE ARE NO STANDARD WAYS TO
LINK MODELS.
THAT'S THE FIRST THING.
THE SECOND THING IS IT MAY BE
IMPORTANT THAT YOU'RE TRYING TO
CAPTURE DIFFERENT SPATIAL
GRANULARITIES IF YOU'RE PASSING
INFORMATION BETWEEN A MOLECULE
AND A CELLULAR SCALE OR TISSUE
SCALE.
YOU ALSO MAY BE PASSING FROM
ODEs TO AGENT-BASED MODELLING
WHICH MAY BE A REASON YOU HAVE
TROUBLE LINKING.
YOU CAN PASS PARAMETERS IS
PROBABLY THE SIMPLEST WAY.
PASS IT FORWARD.
HAVE VOLUMETRIC SCALING BETWEEN
COMPARTMENTS.
SOME GROUPS HAVE THOUGHT DEEPLY
AND JAMES GLASSER IS HERE AND
HE'S AN EXPERT WITH COMPUCELL
AND YOU CAN ASK JAMES IF YOU
HAVE MORE QUESTIONS ABOUT THAT.
SO HOW DO WE CALIBRATE MODELS?
WE CAN FIT THEM AND MAY LOOK
GOOD AND HAVE SOME REGRESSION
AND CLOSE TO THE DATE AND I'M
HAPPY WITH THAT BUT WE'RE BEYOND
THAT NOW.
MOST THE BIO LODDGE --
BIOLOGICAL SYMPTOMS BEYOND AND
THEY'RE COMPLEX AND HOW DO WE
COME TO PRACTICE WITH TRYING TO
CALIBRATE MODELS TO A VARIETY OF
DIFFERENT DATA.
THIS IS IMPORTANT,
SIMULTANEOUSLY.
SIMULTANEOUSLY YOU WANT TO BE
ABLE TO CALIBRATE TO ALL THIS
DATA.
FOR EXAMPLE, IN OUR SYSTEM WE
HAVE LOTS OF METADATA THAT COMES
FROM OUR NON HUMAN PRIMATE
MODELS AND CELLULAR LEVELS AND
MICROENVIRONMENT LEVEL AND
SPATIAL DATA AND TRYING TO PUT
THIS ALL INTO A MODEL.
SO ONE THING WE'VE DONE IN THE
PAST, MANY KNOW OUR WORK ON
CERTAINTY ANNOUNCEMENTS WHERE WE
STRATIFY THE PARAMETER SPACE AND
CHOOSE RANDOMLY WITHOUT REPLACE
MANY AND CREATE A MATRIX AND RUN
THE MODEL THAT MANY TIMES AND
SEE WHAT THE OUTPUTS LOOK LIKE
AND YOU CAN DEFINE DISTRIBUTION.
USUALLY WE DON'T.
WE SAY IT'S UNIFORM.
AND THEN WE CAN SAY, OKAY, WE
FIT THAT TO THE DATA.
WE'RE LOOKING PRETTY GOOD.
WE FEEL GOOD ABOUT THAT.
LIKE I SAID, I THINK IT'S TIME
TO MOVE BEYOND THAT.
SO WE HAVE KIND OF GOT THIS
GAUNTLET WE ASK OURSELVES, WHAT
ARE YOU CALIBRATING TO?
IF YOU HAVE A SINGLE TREND
OUTCOME USE REGRESSION.
IF YOU HAVE COMPLEX DATA AND
WANT TO DO MORE YOU CAN ASK
YOURSELF DO YOU FEEL COMFORTABLE
ASSIGNING A DISTRIBUTION TO THE
DATA OR PARAMETER SPACE.
IF SO THERE'S BAYESIAN
CALCULATIONS YOU CAN USE AND WE
CREATED A METHOD CALLED CALI
PRO.
I'LL TELL YOU ABOUT IT.
IT USE THE UNCERTAINTY AANALYSIS
AND HYPER CUBE SAMPLING AND SO
BALL SEQUENCES, IT RUNS THE SAME
WAY.
YOU RUN IT AND IT PASSES OR
FAILS AFTER IT RUNS THROUGH THE
GAUNTLET OF THE CRITERIA YOU'RE
TRYING TO SATISFY AND ITERATE
AGAIN AND RUN IT AGAIN AND YOU
WANT ALL THE RUNS TO FALL WITHIN
A CERTAIN WINDOW.
THAT'S A HIGH LEVEL OF HOW WE DO
THAT.
FOR EXAMPLE, WE CAN MATCH TO
BOTH SPATIAL DATA AND TIME
SERIES DATA, MULTIPLE TIME
SERIES DATA AND WHAT ABOUT
BAYESIAN APPROACHES?
YOU'RE NOT CONSIDERING OTHER
KINDS OF DISTRIBUTIONS AND
THINGS AND A POSTDOC IN MY GROUP
HAS A POSTER HERE YOU SHOULD
VISIT AND HAVE A PAPER COMPARING
THE BAYESIAN APPROACHES WITH
CALI PRO AND WE'RE WORKING ON A
TISSUE TIME MACHINE AND YOU CAN
SEE ABOUT THAT AND YOU CAN SEE
HIS POSTER.
I WON'T SAY MORE ABOUT THAT.
WE BUILT OUR MODEL AND
CALIBRATED IT AND HAVE TO
SIMULATE IT.
HOW DO WE DO THAT?
THEY'RE USUALLY HYBRID.
YOU HAVE THINGS THAT ARE
DIFFUSING AND THINGS MOVING
AROUND AND RUNNING CONTINUOUSLY
AND DISCREET.
YOU'RE LINKING THINGS LIKE
MODELS WITH ODE AND PDEs AND TRY
TO RUN ALL THESE SIMULATIONS.
ONE OF THE WAYS WE DO IT IS HAVE
DIFFERENT SINKING POINTS BETWEEN
OUR RUNS.
MOLECULAR TIME STEPS ARE VERY
FAST COMPARED TO A CELL TIME
SCHLEP WHICH COULD BE SLOWER.
YOU RUN IT AND FIND SINKING
POINTS AND THOSE ARE GOOD
LINKING POINTS IF YOU'RE TRYING
TO CONNECT MOLTS AS WELL.
THAT'S JUST ONE EXAMPLE HOW WE
DID THAT AND WE HAVE A PAPER WE
PUBLISHED 10 YEARS OR MORE OR SO
ON THE TOPIC IF YOU WANT TO SEE
MORE ON NAP -- ON THAT.
ONE OTHER ANECDOTE, DIFFUSION IS
ALWAYS THE RATE-LIMITING STEP IN
OUR MODELLING AND DID A STUDY
WHICH TOOK US A YEAR AND WON'T
BE PUBLISHED BECAUSE NOBODY
CARES AND AFTER COMPARING
IMPLICIT AND EXPLICIT AND
DIFFERENT TIME STEPS IT TOOK A
YEAR TO FIGURE OUT ALTERNATING
DIRECTION IMPLICIT WITH THE TIME
STEP OF 60 SECONDS IS THE MOST
EFFICIENT FOR OUR SYSTEM.
IT TOOK A YEAR TO GET THAT
ANSWER.
NOT INTERESTING BUT IMPORTANT
FOR US TO RUN OUR MODELLING
EFFICIENTLY IN A GOOD AMOUNT OF
TIME.
ANOTHER THING YOU CAN DO TO HELP
SPEED UP YOUR MODELS IS TO
DECIDE WHERE TO FIND GRAIN IN A
PRUDENT WAY AND COURSE GRAIN.
DO YOU ALWAYS NEED THE GRAIN
SCALE RUNNING ALL THE TIMES IN
YOUR MODELS?
WE THINK NOT BUT YOU WANT TO BE
SMART WHEN YOU COURSE GRAIN.
HERE'S OUR COURSE-GRAIN MODEL
AND OUR FINE-GRAIN MODEL.
NEITHER ONE COMPLETELY COVERS
THE BIOLOGICAL SYSTEM AT HAND
BUT THE COURSE GRAIN'S DOING
WORSE JOB THAN THE FINE GRAIN.
BUT CAN WE MOVE TO A PLACE WHERE
THE COURSE GRAIN MODEL MATCHES A
LITTLE BIT BETTER WITH THE FINE
GRAIN STORY?
JENNIFER AND I CAME UP WITH
TUNEABLE RESOLUTION AND THE BEST
WAY TO FINE GRAIN AND COARSE
GRAIN THE MODEL AND WE DECIDED
TO COARSE GRAIN THAT PORTION AND
THEN -- SORRY, FINE GRAIN IT.
WHEN YOU RUN IT ALL THE TIME YOU
MAY WANT TO COARSE GRAIN IT AND
MY BALLOON TURNED ORANGE.
IT'S NOT THE SAME COLOR AS THE
WHITE BECAUSE IT'S SMARTER NOW.
I LEARNED A LOT OF INFORMATION
BY DOING ULTRA FINE GRAIN I PUT
IN MY NEW COARSE GRAIN VERSION.
MY PINK IS BETTER THAN MY WHITE
ONE WAS.
YOU CAN DO THE SAME THING WITH
THE AREA EAST-WEST OUTSIDE THE
MODEL LIKE THE COMPARTMENTS THAT
ARE INFILTRATING WHETHER YOU
FINE OR COARSE GRAIN THOSE AS
WELL.
HOW'S IT WORK?
HERE'S AN AGENT-BASED MODEL WITH
GRIDS AND TIME SCALES.
YOU CAN SEE THERE'S MULTIPLE
COMPARTMENTS FEEDING INTO THIS.
THE COMPARTMENT ON THE LEFT IS A
MOLECULAR SCALE MODEL OF TNF
RECEPTOR DYNAMICS AND KAPPA B
SIGNALLING DYNAMICS AND RAN THE
FINE AND COARSE GRAIN SO WE
DON'T NEED TO RUN ALL THE DETAIL
ALL THE TIME AND WE HAVE A LYMPH
NODE FEEDING INTO THE MODEL AND
CAN FINE OR COARSE GRAIN WHEN WE
NEED THE INFORMATION.
WE HAVE ONE SITUATION WHERE THE
PROGRAM DOES IT AUTOMATED.
IT PAYS ATTENTION TO WHAT'S
GOING ON AND WILL TURN ON THE
COARSE GRAIN MODEL OR WILL SHUT
IT OFF AND USE THE FINE GRAIN.
YOU CAN EVEN GET THE PROGRAM TO
DO THAT THINKING FOR YOU.
.
HOW DO WE ENSURE MULTI-SCALE
MODELS ARE ROBUST AND CREDIBLE?
THERE'S A SESSION TODAY ON THIS
TOPIC BUT AS GRACE POINTED OUT,
WE CREATED A TEAM, JENNIFER AND
MYSELF AND SILVIA AND SHANE
WHERE WE DECIDED TO BE A
CREDIBILITY TEAM FOR OUR TWO
GROUPS AND REALLY HOLD EACH
OTHER ACCOUNTABLE TO THESE
CREDIBILITY CRITERIA.
FOR EXAMPLE, WE POSTED OUR
WEBSITE WITH OUR GRANULOMA MODEL
ON IT AND ASKED THEM TO USE IT
AND COULD THEY PLAY AND COULD
THEY UNDERSTAND THE RULES?
WERE THEY ABLE TO RUN IT?
ALL THE DETAILS.
THEY HAD THE WHOLE LAB DO THAT
AND THIS IS THEIR CHALK BOARD
AND HAD A MEETING WHERE THEY
WALKED US THROUGH ALL THE ISSUES
AND QUESTIONS.
IT WAS HELPFUL.
WE WERE ABLE TO UPDATE OUR
WEBSITE AND DO A GOOD JOB.
IT'S IMPORTANT TO GET THESE
FRIENDLY REVIEWS.
SO AS GRACE POINTED OUT, WE THEN
TALKED ABOUT WHAT WE'VE DONE FOR
THAT YEAR OF CREDIBILITY
PRACTICES AND HOW WE PUT ALL 10
IN PLACE TOGETHER AS A TEAM.
I WILL TELL YOU WE CONTINUED
THIS COLLABORATION EVEN THROUGH
THE PANDEMIC AND HAVE HAD ONLINE
ZOOM MEETINGS WHERE WE TAUGHT
EACH OTHER THINGS, REVIEWED EACH
OTHER'S PAPERS AND WRITTEN
PAPERS TOGETHER AS WELL.
IT'S BEEN AN INCREDIBLE
OPPORTUNITY MSM AFFORDED US.
OKAY SO HOW DO WE ANALYZE THE
MULTISCALE MODELS?
WE BUILT THEM AND PARAMETERIZED
THEM AND NOW WANT TO UNDERSTAND
THEM.
MOST PEOPLE ARE NOT CREATING
PURE EQUATION-BASED MODELS,
RIGHT.
THAT'S RARE EVERYTHING IS
EQUATION BASED WHERE YOU CAN
TYPICALLY HAVE THE TRADITIONAL
ANALYSES TOOLS AVAILABLE TO YOU
I'VE LISTED HERE.
WHAT WE'VE DONE IS APPLIED THIS
UNCERTAINTY TOGETHER WITH THE
SENSITIVITY ANALYSIS THAT ALLOWS
US TO IDENTIFY THROUGH A PARTIAL
CORRELATION A NON-LINEAR
CORRELATION WHICH PARAMETERS ARE
DRIVING ALL THE BEHAVIOR.
WE ALSO BELIEVE AND I SAID MANY
TIMES TO PEOPLE THESE ARE
IDENTIFYING BIFURCATION
PARAMETERS IN CRITICAL AND HUF
AND I'VE TESTED IT IN A FEW
SITUATIONS.
THE IMPORTANT THING IS IT
QUANTIFIES THINGS OVER TIME.
WHAT THAT TELLS YOU IS WHAT'S
GOING ON EARLY IN THE STUDY MAY
BE DIFFERENT THAN LATER AND WILL
ALLOW TO YOU IDENTIFY PARAMETERS
DRIVING DIFFERENT BEHAVIOR
DURING THOSE DIFFERENT TIMES.
THIS PAPER'S PUBLISHED.
IT'S ONE OF THE MOST HIGHLY
DOWNLOADED AND CITED PAPERS OF
THE JOURNAL OF THEORETICAL
BIOLOGY AND HAVE THIS AVAILABLE
ON OUR WEBSITE IF ANYBODY WANTS
TO USE THEM.
WHAT DOES IT DO?
HERE'S AN EXAMPLE.
THESE ARE PARTIAL REG
CORRELATIONS.
YOU CAN SEE SOME PARAMETERS SHOW
UP IMPORTANT AND NEVER SHOW UP
AGAIN AND OTHER ONES SHOW UP
AFTER 50 DAYS AND STAY
SIGNIFICANT THE WHOLE TIME.
THIS IS AN IDEA OF HOW THE KIND
OF INFORMATION YOU CAN GET FROM
THESE ANALYSES.
NOW WE WANT TO SAY YOU LEARNED
ALL THIS STUFF, CAN YOUR MODELS
BE VALIDATED?
SO VALIDATION IS PART OF THE
CREDIBILITY PROCESS WHICH WE'LL
HEAR ABOUT IN THE NEXT SESSIONS.
ALSO I WANT TO POINT OUT IT TIES
IMPORTANTLY TO THE TYPES OF DATA
YOU ARE STUDYING.
FOR EXAMPLE, WE HAVE SPATIAL
DATA WE TRY TO MATCH TO AS WELL
AS TEMPORAL DATA AND IT BECOMES
EXTREMELY IMPORTANT TO FIGURE
OUT HOW TO VALIDATE YOUR MODELS
TO BOTH TYPES OF DATA.
ONE THING THAT BECOMES EXTREMELY
IMPORTANT HERE IS THE IDEA OF
2-D AND 3-D.
MOST THING IN BIOLOGY ARE THREE
DIMENSIONS BUT A LOT OF US MODEL
IN TWO DIMENSIONS BECAUSE IT'S
EASIER TO MODEL IN 2-D
ESPECIALLY NUMERICALLY.
WE CREATED GRANULOMAS BUT HARD
TO DO ANALYSIS THAT TAKES A WEEK
TO RUN.
WE DID STUDIES WHERE WE TRIED TO
ASK HOW LONG IS IT TO MODEL IN
2-D?
IF YOU'RE DOING SOMETHING IN TWO
DIMENSION WHEN IS IT SAFE TO USE
A 2-D MODEL?
WHAT IF YOU HAVE A 2-D MODEL BUT
THE DATA IS FROM A 3-D SYSTEM?
HOW DO YOU COMPARE TO THE DATA
THE NUMERICAL TO SIMULATED DATA?
WE WROTE TWO PAPERS ON THE
TOPIC.
ONE IS WITH SILVIA AND SHANE AND
JENNIFER THROUGH THE CONSORTIUM
WE IDENTIFIED THESE WERE
IMPORTANT ISSUES THAT WERE
NEEDED TO BE ADDRESSED.
REALLY I'LL TELL YOU THE BOTTOM
LINE, IF YOU THINK OF HOW THINGS
MOVE IN SPACE YOU NEED 3-D.
THE NUMBER OF DIRECTIONS YOU CAN
MOVE IS DIFFERENT AND YOU LOSE
ALL OF THAT BUT IF YOUR
QUESTIONS AREN'T RELATED TO
MOVEMENT OR HOW THINGS FIND EACH
OTHER MOST THE TIME 2-D WAS OKAY
IN THE SYSTEMS WE LOOKED AT.
I WANT TO WRAP UP BY SAYING HOW
CAN MULTISCALE MODELS BE USEFUL?
BY OUR WORK WE LOOKED AT TB
IMMUNE RESPONSE IN THE LUNGS THE
INNER WORKINGS OF THE LYMPH
NODES AND TB DRUG REGIMENTS,
EPIDEMIOLOGY OF TB AND WE DID A
SPIN OFF AND WE DID GRANULOMAS
AND DID TUMOR MODELLING IN
SASHA'S GROUP AND CREATED CALI
PRO AND OUR UNCERTAINALITY
APPROACH AND WE'RE LOOKING AT
VIRTUAL CITY HOPEFULLY YOU'LL
SEE A PAPER ON SOON AND WHO
KNOWS WHAT OTHER AREAS WE'LL GET
TO.
IT'S IMPORTANT TO EMPHASIZE THE
IMPORTANT OF PARING THE YIN AND
YANG OFFI IFI IFING OF MODELLI
HAVING A CLINICAL PERSON
STUDYING WITH YOU IS IMPORTANT.
THESE ARE ITERATIVE PROCESSES
BUT WORK SIMILARLY ACTUALLY AND
EACH CAN INFORM THE OTHER.
THEY INTERSECT TO GENERATE
HYPOTHESES, POSE NEW QUESTIONS
AND ITERATE.
WHERE DO WE GO FROM HERE ONCE WE
UNDERSTAND THE MODELS FROM NUTS
TO BOLTS?
CROSS-TALK WITH PEOPLE AND SHARE
MODELLING IDEAS.
DO WE CREATE A GIANT DIGITAL
TWIN STORY AS A MATH PERSON, IS
MATH STILL NEEDED DO WE NEED
THIS OR IS IT JUST COMPUTATION?
WHAT ABOUT MORE ADVANCED
APPLICATIONS?
CONTINUE WITH COVID AND CANCER
AND CLIMATE JUSTICE AND
RESISTANCE AND THERE'S MANY
AREAS OUR MODELS CAN BE USED FOR
AND THIS IS MY MECHANISTIC
MODELLING SOAPBOX IS A.I. THE
ANSWER?
I WANT TO END BY CYNTHIA RUDIN'S
PAPER THAT SAID STOP EXPLAINING
BLACK BOX MACHINE LEARNING MELDS
FOR HIGH STEAK DECISIONS AND USE
MECHANISTIC MODELS AND WHOO-HOO.
I THINK NO, BUT I THINK THERE'LL
BE A LOT OF DISCUSSIONS ABOUT
THAT IN THE NEXT TWO DAYS.
I'M EXCITED TO HEAR WHAT PEOPLE
HAVE TO SAY.
THANK YOU TO MY GROUP.
AS I SAID, MOST THIS WORK IS
DONE WITH JENNIFER LINDERMAN AND
OUR FANTASTIC COLLABORATORS AND
FABULOUS FUNDING.
THANK YOU SO MUCH FOR YOUR TIME.
WE DON'T HAVE QUESTION TIME BUT
THE WIKI IS A PLACE TO PUT
QUESTIONS IN AND WANT TO TALK TO
WHOEVER HAS COMMENTS OR
CRITICISMS OR WHATEVER ABOUT
WHAT I SHARED.
WE ARE NOW GOING TO HEAR THE
NEXT SESSION WHICH IS AHMET
ERDEMIR WHO IS UNFORTUNATELY
STUCK IN CLEVELAND AND HIS
SESSION IS THE JOURNEYS OF THE
EARLY FUNDED MULTISCALE
MODELLING P.I.s.
A SUB GROUP OF THE ORIGINAL
CREW.
WE'LL ASK YOU ALL TO COME UP AS
AHMET INTRODUCES THE SESSION.
AHMET, DO YOU WANT ME TO ADVANCE
THE SLIDES FOR YOU?
AHMET, CAN YOU SAY A FEW WORDS?
I CAN HEAR YOU, YOU CANNOT HEAR
ME.
THAT IS CORRECT.
SEAN, WOULD YOU LIKE ME TO HAVE
AHMET SPEAK THROUGH MY COMPUTER?
>> OKAY.
AMAZING.
IT WORKS.
ALL RIGHT, SO I'M GREGOR AND I'M
A MEMBER OF ONE OF THE FIRST
TEAMS THAT WAS FUNDED BY IMAG
MSM.
WE WERE MULTISCALE MODELING OF
THE PRIMARY VISUAL CORTEX.
THERE WERE THREE GUYS FROM NYU,
DAVID GUI WAS THE P.I. AND MIKE
McLAUGHLIN AND MYSELF.
SO WHEN I SAY WE, I MEAN MOSTLY
DAVID KUI AND A WONDERFUL
POSTDOC ANDY RANGGAN AT NYU.
THE ACCOMPLISHMENTS WAS A VERY
DETAILED MODELLING OF ABOUT 100
HYPER COLUMNS WITH BOTH SHORT
RANGE ISOTROPIC AND LONG RANGE
AND ISOTROPIC ORIENTATION
DEPENDENT CONNECTIONS.
AND IT WAS A HUGE MODEL MILLIONS
OF NEURONS RANDY RANGAN PUT
TOGETHER WITH NUMERICAL METHODS
AND TRICKERY AND IT REPRODUCED
EXPERIMENTS AT THAT TIME VOLTAGE
PATTERNS HOW THEY MIGRATE FROM
REGION TO REGION.
IT REPRODUCED THE TRACES OF THE
HIMOSAKA ILLUSION.
THAT'S A COOL ILLUSION WHERE YOU
CAN GOOGLE IT.
YOU PRESENT A LITTLE DOT AND
THEN THE LINE.
WHAT YOU SEE IS THE LINE GROWING
OUT OF THE DOT.
AND HOW DO YOU SIMULATE THE V1
ACTIVITY THAT SHOWED HOW THIS
GROWTH OCCURS?
SO THAT WAS ONE THING THERE WAS
A KINETIC THEORY OF THE NEURONAL
NETWORK DYNAMICS IN GENERAL NOT
JUST V1.
WHAT WAS THE ADVANCEMENT.
THE KINETIC THEORY WAS THERE
BEFORE AND WAS PROBABILISTIC IN
TERMS OF VOLTAGE WHICH MEANT
DIMENSIONALITY.
DAVID KUI DERIVED A CLOSURE IN
TERMS OF MOMENTS OF THE
CONDUCTANCES SO IN THE END WE
HAD A CLEAN PDE IN TWO
DIMENSIONS, TIME AND VOLTAGE.
SO THAT'S WHAT WE DID.
THANK YOU.
>> I THINK WE'LL I HAVE TO HAVE
AHMET SPEAK THROUGH THE AUDIO.
>> CAN YOU HEAR ME NOW?
>> YES, BUT WE'RE GETTING A
LITTLE BIT OF FEEDBACK.
CONTINUE WHILE WE WORK ON THE
TECHNICAL.
JENNIFER, PLEASE.
>> WELCOME, EVERYBODY.
SO DENISE AND I AND OUR
BIOLOGICAL COLLABORATORS
ESPECIALLY JOANNE FLIN AT
PITTSBURGH WHO DOES THE MONKEY
WORK AND DENISE TALKED ABOUT THE
SCIENCE SO I THOUGHT I'D TALK
ABOUT THE OTHER THING.
COMMUNITY.
MAYBE BECAUSE WE'RE POST
PANDEMIC AND ALL THOUGHT ABOUT
COMMUNITY.
A COUPLE THINGS THAT SHAPED MY
EXPERIENCE.
ONE, I'M IN MECHANICAL
ENGINEERING DEPARTMENT AND
NOBODY IN MY DEPARTMENT CARES
ABOUT TUBERCULOSIS AT ALL OR
INFECTIOUS DISEASES IT WAS GREAT
TO BE HERE AND BEING WITH A
COMMUNITY THAT CARED AND NOBODY
WAS CARING ABOUT MULTISCALE
MODELING AND I DIDN'T HAVE
COLLEAGUES ON THE GROUND THERE
AND TO COME HERE AND TALK ABOUT
SCALES AND LINKING AND METHODS
AND ALL THAT WAS PRETTY COOL.
I THINK THAT WAS A BIG BONUS.
I REMEMBER ONE OF MY EARLY GRANT
PROPOSAL REVIEWS, ONE PERSON
LIKED IT.
I STILL REMEMBER THAT BUT THE
OTHER PERSON SAID YOU BETTER GET
AN EXPERIMENTAL PROGRAM IN FAST
OR YOU'LL NEVER MAKE IT AND I
DIDN'T BELIEVE IT AND NONE OF
YOU BELIEVED THAT.
IT WAS NICE TO HAVE A COMMUNITY
LIKE THAT.
THE OTHER THING I'D SAY ABOUT
COMMUNITY IS MORE TROUBLING
THOUGH WHICH IS I'VE BEEN COMING
TO THIS MEETING FOR A LONG TIME
AND IT'S NOT A VERY DIVERSE
COMMUNITY.
I THINK THAT'S PRETTY OBVIOUS TO
ALL OF US.
IT'S MUCH LESS DIVERSE THAN
BIOMEDICAL ENGINEERING AND EVEN
CHEMICAL ENGINEERING.
THERE'S REASONS FOR THAT.
IN THE LAST FEW YEARS WE'VE BEEN
ABLE TO LOOK AROUND AND SAY THIS
ISN'T THE COMMUNITY WE WANT.
WE'D LIKE A MORE DIVERSE
COMMUNITY WITH NEW INVESTIGATORS
THAT REPRESENT ALL KINDS OF
IDENTITIES.
I KNOW MANY OF US HAVE BEEN
WORKING ON THAT AND HOPE WE SEE
THE FRUITS OF THAT.
IT'S ALSO SHAPED MY CAREER IN
THE SENSE THAT I'VE BEEN SINCE
2016 I'VE BEEN THE DIRECTOR OF A
CAMPUS PROGRAM AROUND FACULTY
DIVERSITY AND LOOKING AT
RECRUITMENT AND RETENTION AND
LEADERSHIP AND THINGS ANY
ORGANIZATION SHOULD BE LOOKING
AT.
I THINK THAT'S BEEN A BIG PART
OF THE COMMUNITY LESSON I'VE
LEARNED TOO IS THE WORK WE HAVE
TO DO TO SHAPE THE COMMUNITY WE
WANT.
>> I CAME TO THE MSM FROM NSF
BIO COMPLEXITY INSTITUTE GRANT
THAT GOT US STARTED.
INITIALLY WERE WORKING
MULTISCALE MODELING DEVELOPMENT
AND WE WORKED WITH MARK ALBERG,
CHARLIE LITTLE AND A NUMBER OF
INTERNATIONAL COLLABORATORS.
ONE THING THAT WAS IMPORTANT
ABOUT THE NIH FUNDING WAS IT
COULD INCLUDE AN INTERNATIONAL
COMPONENT.
THAT WAS CRITICAL.
WE WORKED WITH CHARLIE LITTLE IN
KANSAS AND CLAUDIA STERNE IN
LONDON.
THE ABILITY TO NOT ONLY
COLLABORATE WITH
EXPERIMENTALISTS AN
MATHEMATICIANS LIKE MARK AND
OTHERS AND TO COLLABORATE ACROSS
THE OCEAN WAS IMPORTANT TO US.
WE WORKED ON A VARIETY OF
DEVELOPMENT PROBLEMS.
FOUND OUT BIOLOGY IS NEVER AS
SIMPLE AS YOU MIGHT WANT IT TO
BE.
THAT LED TO INTERESTING RESULTS.
A LOT OF MY WORK HAS BEEN ON
OPEN-SOURCE SOFTWARE DEVELOPMENT
AND COMMUNITY DEVELOPMENT, MODEL
SHARING AND STANDARDS.
AND ENCOURAGE SHARABILITY AND
STANDARDS-BASED WORK AND WE'LL
BE TALKING ABOUT THIS AND THAT'S
SOMETHING WE SPENT A LOT OF TIME
ON OVER THE YEARS AND WORKED
HARD ON.
THE PANDEMIC WAS ACTUALLY A
GREAT OPPORTUNITY FOR US IN A
VARIETY OF WAYS.
WE HAD THIS WORKING GROUP WITH
THE AWKWARD NAME OF MULTISCALE
MODELING IN VIRAL PANDEMICS
WHICH STILL MEETS.
WE HAD AN ENORMOUS PEOPLE
PRESENT TO THAT GROUP.
PEOPLE ARE TIRED OF VIRTUAL
SEMINARS NOW BUT IT WORKED FOR A
WHILE AND BELIEVED THAT WAS
SOMETHING VALUABLE.
GOING ONLINE ALSO MEANS WE CAN
TEACH MORE.
I DO A LOT OF TEACHING OF
MULTISCALE MODELING AND WE HAVE
BEEN VERY PLEASED THAT'S ALLOWED
PEOPLE WHO WERE IN UNDER SERVED
COMMUNITIES TO PARTICIPATE IN
THE SCIENTIFIC PROCESS IN A WAY
THAT WAS NOT SO EASY WHEN
EVERYTHING WAS IN PERSON.
[NO AUDIO]
>> THE PROJECT WAS TRYING TO
ESTABLISH STANDARDS FOR
MODELLING SO THE DEVELOPMENT FOR
MODELS AND MODEL REPOSITORIES
BASED AND THE DEVELOPMENT OF
OPEN SOURCE SOFTWARE WHICH
IMPLEMENTED THE ABILITY TO SOLVE
THE MORALS IN THE CORE.
YOU TIE IT IN WITH THE IMAG
EFFORTS.
AND AND DOING MULTISCALE
MODELING IN A REPRODUCIBLE WAY
IS HARD.
MOST MODELS IT'S VERY DIFFICULT
TO PUBLISH A MODEL THAT DOESN'T
HAVE MISTAKES ON IT.
OUR PUBLICATIONS HAVE MISTAKES.
IT'S SO DIFFICULT TO PUBLISH IN
A WAY THAT AVOIDS TYPO GRAPHICAL
ERRORS AND PUTTING MODELS ON TO
WEBSITES OFTEN DOESN'T HELP
BECAUSE ALL THAT HAPPENS IS THE
CODE ON THE WEBSITE DOESN'T
MATCH THE RESULTS IN THE PAPER.
THAT'S ONE OF THE EARLY LESSONS.
THE IMPORTANCE OF TRYING TO GET
BETTER APPROACHES TO HANDLING
REPRODUCIBILITY WHICH IS
OBVIOUSLY CRUCIAL FOR SCIENCE.
AND WE APPROACH THE TRULY
THOUSANDS OF PEOPLE AROUND THE
WORLD AS WE HELPED GET THE
MODELS INTO THE PHYSIO MODEL
REPOSITORY AND THERE'S ABOUT
1500 MODELS ON THE REPOSITORY
NOW.
AND MANY ARE WILLING TO BE
INVOLVED AN GET THEIR MODELS
REPRODUCIBLE AND IT WITH A BIT
THEY WERE TRYING TO BE OBSCURE
OR OBTUSE IN THEIR MODELS THEY
WANTED TO PRODUCE REPRODUCEDABLE
MODELS.
WE WERE GREATLY ENCOURAGED BY
THE WILLINGNESS OF PEOPLE TO PUT
THE EFFORT IN TO THEN PUBLISH
MODELS THAT WERE THROUGH THE
PHYSIO MODEL REPOSITORY WERE
REPRODUCIBLE AND LET US TO THE
PHYSIO JOURNAL INTENDED AS A
SECONDARY JOURNAL TO ALLOW
PEOPLE TO PUBLISH IN THE NORMAL
JOURNALS AND IN A LOW OPEN
ACCESS COST TO BE ABLE TO HAND
ADD CURATETION AND ANNOTATION TO
MAKE SURE A MODEL WAS
REPRODUCIBLE.
THAT I THINK IS GOING TO BE
ANOTHER WAY OF GIVING PEOPLE THE
INCENTIVE TO MAKE SURE THEY'RE
MAKING THEIR WORK AVAILABLE TO
THE BROADER COMMUNITY IN A
REPRODUCIBLE FASHION.
THANKS.
>> JAMES.
>> I'M PLEASED TO BE HERE.
I ALWAYS ENJOY THE MEETING.
THERE'S TOPICS I ONLY SEE AT
THIS MEETING, NOT AT OTHER
CONFERENCES I GO TO AND PEOPLE I
ONLY SEE AT THIS MEETING AND
APPRECIATIVE OF ALL YOU BEING
HERE.
THIS CAME ABOUT AT A GOOD TIME.
IN 2002 I JUST MOVED TO TEXAS
A&M AND LOOKED AT THE SYSTEM AND
WAS INVOLVED IN THE TOP SO 10
CAUSES OF DEATH.
I CAN TELL YOU WHY IT BELONGS AT
EACH INSTITUTE AND WHY IT
DOESN'T FOR EACH OF THE
INSTITUTE.
THERE'S STILL NO STUDY SECTION.
THERE'S STILL NO LYMPHATIC
SPECIFIC FUNDING CALLS.
WE END UP APPLYING WHATEVER AND
GETTING REVIEWED BY PEOPLE WHO
CAN'T SPELL LYMPHATIC.
YOU CANNOT MEASURE PRESSURE FLOW
OR DIAMETER IN ANY PART OF THE
BODY MINIMALLY INVASIVELY,
NON-INVASIVELY, FORGET IT.
THIS WAS A GOOD WAY TO LOOK AT
LYMPHATIC PUMPING AND MASS
TRANSPORT AND TO THIS DAY IT'S
THE FUNDING POT THAT'S HAD THE
MOST IMPACT ON MY CAREER FOR
SURE.
I'M VERY THANKFUL FOR IT AND WE
CONTINUE TO WORK ON THE
QUESTIQUEST
QUESTIONS THAT GOT STARTED
DURING THE FUNDING PERIOD SO
THANKS FOR INVITING ME TO BE
PART OF THIS.
>> THANK YOU, PANEL FOR SHARING
YOUR IMPACTS AND YOUR TAKEAWAYS.
AND YOU CAB PUT QUESTIONS ON THE
WIKI AND AND I'LL INTRODUCE
MYSELF, I'M STEPHANIE GEORGE.
I'M A PROGRAM DIRECTOR AT THE
NATIONAL SCIENCE FOUNDATION AND
I AM REPRESENTING IMAG AS ONE OF
THE MODERATORS.
MY FIRST QUESTION TO THE PANEL
IS WHAT WERE SOME OF THE
TECHNICAL AND SOCIETAL
CHALLENGES THAT YOU FACED AS YOU
HAD AND HOW DID YOU OVERCOME
THEM AND WHOEVER WOULD LIKE TO
START.
TECHNICAL AND SOCIETAL
CHALLENGES.
>> REPRODUCIBILITY WAS IMPORTANT
WAS A SOCIETAL CHALLENGE.
THE INCENTIVE FOR DOING THAT
THROUGH THE NORMAL ACADEMIC
PROMOTION PROCESS ARE NOT GREAT.
IF YOU PUBLISH PAPERS NO ONE'S
THEN COMMENTING ON WHETHER OR
NOT THE PAPER IS USEFUL TO OTHER
PEOPLE BY BEING REPRODUCIBLE.
THAT WAS A SOCIETAL CHALLENGE IN
A WAY.
YOU ALMOST NEED A CARROT AND
STICK APPROACH.
THERE'S INCENTIVE TO PUBLISH
REPRODUCIBLE IN AN OPEN-ACCESS
REPRODUCIBLE FORM BECAUSE YOU
GET CITED MORE FREQUENTLY IF
PEOPLE CAN REUSE WHAT YOU'VE
DONE.
AND I THINK AND HAVE TO PLAY A
ROLE BECAUSE WHY FUND IF IT'S
NOT REPRODUCIBLE.
IT REQUIRES THE RIGHT INCENTIVES
FOR PEOPLE TO EXHIBIT THE RIGHT
BEHAVIOR THAT IS NEEDED FOR THE
FUTURE OF COMPUTATIONAL SCIENCE
I THINK.
>> A COUPLE SOCIETAL S --
CHALLENGES.
THE MODELLING DOESN'T PAY OFF
RIGHT AWAY.
IT'S NOT ENTIRELY CLEAR WHERE
THE WORK AROUND SENSITIVITY
ANALYSIS OR LINKING MODELS OR
OPTIMIZING THINGS WHERE IT'S ALL
GO TO GO.
THAT'S ONE WE'RE ALL FIGHTING.
AND AROUND TB IT KILLED THE SAME
NUMBER OF PEOPLE EVERY YEAR THAT
COVID KILLED IN ITS TWO BAD
YEARS.
SO PEOPLE ARE DYING OF TB.
AT THAT RATE EVERY SINGLE YEAR
AND YET MOST PEOPLE DON'T KNOW
THAT.
SO IT WAS GREAT TO GET FUNDING
TO WORK ON SUCH AN IMPORTANT
DISEASE BUT I THINK IT DOES SORT
OF BRING TO LIGHT REALLY
INTERESTING QUESTIONS ABOUT WHAT
DO WE FUND AS A SOCIETY AND WHY.
>> I'LL COME BACK TO WHAT I SAID
EARLIER ABOUT NOT BEING ABLE TO
MEASURE VERY MUCH IN THE
LYMPHATIC SYSTEM.
MOST PEOPLE THAT DO LYMPHATIC
RESEARCH ARE BIOLOGIST AND
PHYSIOLOGISTS.
YOU MAY NEED TWO HANDS TO COUNT
THEM AROUND THE WORLD BUT MAYBE
ONE.
YOU GET DINGED A LOT FOR YOU
CAN'T VALIDATE THE MODEL
OUTCOMES AND WE'VE SEEN THAT IN
BOTH FUNDING APPLICATIONS AND
PUBLICATIONS WHEN WE SUBMIT THEM
AND YOU CAN'T MEASURE THESE
THINGS.
WE GET A BIT STUCK SOMETIMES.
WE'VE HAD TO OVERCOME THAT OR
TRY TO OVERCOME THAT THE OTHER
THINK IS I'VE SEEN POOR ATTEMPTS
TO QUANTIFY THE OUTCOME OR COME
TO QUANTITATIVE CONCLUSIONS
BASED ON THE EXPERIMENTS THEY'VE
DONE AND THEY'RE BLATANTLY
WRONG.
I'D LIKE TO SEE VALIDATION GO
THE OTHER WAY.
IS SOMEONE WHEN AN IN VIVO
EXPERIMENT OR PHYSIOLOGY, IS
THERE SOMEONE CHECKING THAT IN A
QUANTITATIVE WAY?
I'D LIKE TO SEE THAT
QUANTIFICATION GO BACK THE OTHER
WAY.
>> THERE WAS GREAT
EQUATION-BASED MODELS.
DESPITE NIH BEING GENEROUS IN
FUNDING WORKSHOPS WE STILL DON'T
HAVE THE ABILITY TO DO THAT KIND
OF STANDARDIZATION IN THE AREA
OF AGENT-BASED MODELLING.
THAT'S THE PROBLEM WITH
MULTISCALE MODELING BECAUSE MANY
INVOLVE AGENTS.
IT'S HARD.
HOW DO YOU COMPARE STOCHASTIC
IMAGES AND CLASSIFY THEM AND
WHEN YOU DO HOMOGENIZATION YOU
HAVE TO MAKE DIFFERENT CHOICES.
THAT'S SOMETHING WE TALKED ABOUT
MANY YEARS AND STILL GRAPPLING
WITH AND IS A HARDER PROBLEM
THAN WE APPRECIATED WHEN WE
STARTED.
OUR GROUP HAD SOCIETAL AND
TECHNICAL CHALLENGES.
THE SOCIETAL CHALLENGE WAS WE
WERE IN SOME SENSE INTERLOPERS.
WE WEREN'T MATH BIOLOGISTS.
WE STARTED DOING THAT BIOLOGY
ABOUT FIVE YEARS BEFORE.
THIS IS A WAY TO GAIN CRITICAL
MASS OF FUNDING TO PUT OUT GOOD
AND IMPORTANT WORK AND IT WAS IN
SOME SENSE THE PINNACLE OF THE
WORK OF THE GROUP.
THE TECHNICAL CHALLENGES WERE
PRETTY BIG.
IMAGINE IN THOSE DAYS, THIS IS
20 YEARS AGO, SIMULATING A
MILLION NEURONS INTERACTING AND
THIS BEING ACHIEVED OUR POSTDOCS
MANAGED TO DO SO THAT WAS QUITE
AN ACHIEVEMENT AT THE TIME AND
THE TECHNICAL CHALLENGES WERE
GREAT.
I STILL DON'T SEE A LOT OF
PEOPLE USING HIS METHODS.
A LOT OF PEOPLE USE METHODS SO
IT WOULD BE GOOD IF MORE PEOPLE
READ HIS PAPER AND USED HIS
METHODS BECAUSE THEY'RE VERY
POWERFUL.
>> THIS QUESTION BUILDS ON
CHALLENGES.
AN IMPORTANT CHALLENGE IS
MODIFYING REUSING COMBINING
MODELS FOR THE
OPERATIONALIZATION FOR SEAMLESS
DECISION MAKING.
SO WHAT CHALLENGES DO THE
PANELISTS SEE IN THIS REGARD
OPERATIONALIZING TOWARDS
TRANSLATION AND HOW CAN THE
COMMUNITY TACKLE THIS TOGETHER.
SO THIS IS REGARDING
OPERATIONALIZATION OF METHODS
TOWARDS TRANSLATION.
SO AN IMPORTANT CHALLENGE IS
MODIFYING, REUSING, COMBINING
MODELS FOR THEIR
OPERATIONALIZATION TO ENABLE
SEAMLESS DECISION MAKING AND SO
WHAT ARE SOME OF THE CHALLENGES
THAT YOU ALL SEE IN DOING THIS
THAT THE COMMUNITY COULD TACKLE?
>> WHEN WE FRAME THINGS IN TERMS
OF DIGITAL TWINS THAT CLARIFIES
SOME ISSUES YOU RAISED.
IF WE THINK WHAT IT TAKES TO
BUILD A FUNCTIONAL MEDICAL TWIN
THAT INCORPORATED WHAT WE DO NOW
YOU HAVE TO BEGIN TO ADDRESS ALL
THESE ISSUES AND OF COURSE THIS
WORKSHOP IS GOING TO BE
FOCUSSING ON VALIDATION AND
CREDIBILITY AND INTEROPERABILITY
WE TALKED A LITTLE BIT ABOUT AND
DENISE SAID THESE MODELS ARE
SLOW.
IF YOU'RE GOING TO TRY TO MAKE
REAL TIME DECISIONS IT'S NOT
OBVIOUS YOU'LL BE ABLE TO GET
AWAY WITH LARGE AGENT-BASED
MODELS AS A COMPONENT.
ONE CONCEPT IS THE IDEA OF
ONLINE DIGITAL TWIN WHERE YOU
HAVE TO DO PREDICTION IN REAL
TIME VERSUS OFFLINE DIGITAL TWIN
WHERE YOU'RE DOING SURGICAL
EVALUATION AND I THINK THAT'S A
PLACE ELUDED TO THE ISSUE OF
MACHINE LEARNING AS A CONFLICT.
I THINK THE FUTURE FOR DOING
THIS IS GOING TO BE THE
HARMONIZATION OF THE MACHINE
LEARNING MODELS AND USE THEM TO
EVALUATE VALIDITY.
I THINK WE ARE JUST BEGINNING TO
GRAPPLE WITH THOSE ISSUES.
>> ALL RIGHT.
WE'LL MOVE TO ANOTHER QUESTION.
THIS IS REGARDING INTRODUCING
INTERDISCIPLINARY WORK.
IF YOU CAN COMMENT ON THE VALUE
AND FUTURE OF INTERDISCIPLINARY
WORK AND COMMUNICATION ACROSS
DOMAINS FOR MULTISCALE MODELING.
>> THIS CLOSER YOU GET TO
CLINICAL APPLICATION OF
MODELLING WORK AND THE USE OF
MODELS AND CLINICAL WORK FLOWS
THE CLOSER YOU GET TO THE
CLINICAL END POINT THAT WORK THE
MORE DISCIPLINES YOU NEED TO BE
EFFECTIVE.
IT BECOMES EXTREMELY -- THERE'S
A VERY DIVERSE GROUP OF PEOPLE
THAT NEED TO BE INVOLVED IN THAT
FINAL CLINICAL WORK FLOW BECAUSE
YOU NEED TO PEOPLE TO UNDERSTAND
THE PSYCHOLOGY OF TREATMENT
STRATEGY.
AND PEOPLE AROUND COMPLIANCE
WITH ADVICE FROM THERAPEUTIC
MODEL-BASED THERAPIES.
I THINK -- THE SINGLE POINT IS
THE CLOSER YOU GET TO
APPLICATION THE MORE
INTERDISCIPLINARY THE CHALLENGES
ARE AND THE MORE PEOPLE YOU NEED
TO COME TOGETHER TO COLLABORATE
TO MAKE SURE THINGS DO WORK.
>> TEACHING MULTISCALE MODELING
FOR 18 YEARS.
WE HAD HIGH SCHOOL STUDENTS FROM
EMERITUS PROFESSORS AND SURGEONS
TO MATHEMATICIANS AND DEVELOPING
AN APPROACH TO BRING THOSE
PEOPLE TOGETHER AND COMMUNICATE
AND HELP BUILD TEAMS I THINK HAS
BEEN ONE OF THE BIGGEST
CHALLENGES AND ALSO ONE OF THE
BIGGEST SATISFACTIONS.
WE LOVE HACKATHONS BECAUSE YOU
CAN ASSEMBLE A TEAM OF PEOPLE
WITH DIVERSE BACKGROUNDS.
THAT'S DIFFERENT FROM BUILDING A
TEAM THAT WILL SOLVE A MAJOR
CLINICAL PROBLEM IN A MEDICAL
CONTEXT WHICH IS A DIFFERENT
THING.
BUT THE SMALL SCALE THINGS LIKE
A HACKATHON CAN BEGIN TO
NUCLEATE TO COMMUNICATE AND
BUILD A GENERATION OF PEOPLE WHO
HAVE THE ABILITY TO FEEL
NATURALLY COMFORTABLE ACROSS
DISCIPLINES.
>> CAN I ADD TO THAT BECAUSE I
SAW ANOTHER QUESTION COMING UP
ABOUT CAREER OPPORTUNITIES.
I WANT TO PIGGY BACK ON WHAT
YOU'RE SAYING, HAVING PEOPLE WHO
CAN SPEAK ACROSS DISCIPLINES WHO
DON'T JUST KNOW THE WAY THINGS
ARE DESCRIBED IN MEDICINE OR IN
COMPUTER SCIENCE OR WHATEVER,
THAT'S INCREDIBLY VALUABLE AND
THOSE FOLKS CAN FIND JOBS
EVERYWHERE.
THEY'RE RECOGNIZED.
MY STUDENTS ARE RECOGNIZED AS
FOLKS WHO CAN GO INTO LOTS OF
DIFFERENT CONTEXTS AND SPEAK TO
LOTS OF PEOPLE AND BRING PEOPLE
TOGETHER TO WORK ON A PROBLEM.
>> INTERDISCIPLINARY PEOPLE HAVE
MORE OPPORTUNITIES AND WE SHOULD
START TRAINING THEM MORE OR LESS
FROM THE GET GO.
FOR ME EVEN TALKING AS A MAT
MATHEMATICIAN TALKING TO PURE
MATHEMATICIANS IS HARD AND
SOONER GRADUATE STUDENT START
DOING IT THE BETTER.
>> ARE THERE DIVERSE CAREER
TRAJECTORIES BEYOND ACADEMIA
THAT CAN SUPPORT THIS MODELLING
ECO SYSTEM?
>> I SUPPOSE EVERYONE IN THE
ROOM HAS HAD TROUBLE RECRUITING
POSTDOCS BECAUSE INDUSTRIAL
DEMAND HAS BEEN SO HIGH AND
EVERY POSTDOC HAD OFFERS FROM
START UPS AND INDUSTRIAL
COMPANIES.
AND I THINK THE ABILITY TO THINK
ABOUT PROBLEMS IN A QUANTITATIVE
WAY AND ANALYZE COMPLEX PROBLEMS
HAS A SET OF SKILLS THAT GO WELL
WELL BEYOND THE BASIC SCIENTIFIC
RESEARCH SOME OF US STARTED
WITH.
I THINK THAT'S REALLY A CRITICAL
OPPORTUNITY ONE CAN GO TO HEART
FLOW IN THE UNITED STATES.
ONE COULD POINT TO NOVA
DISCOVERY IN FRANCE THAT ARE
TAKING THESE IDEAS AND USING
THEM IN A TRANSFORMATIONAL AND
TRANSLATIONAL WAY.
IT'S A GREAT OPPORTUNITY FOR US
TO BUILD TECHNOLOGY AND
TRANSLATION.
>> OTHERS FOR CAREER PATHWAYS?
WE'LL MOVE TO THE NEXT QUESTION,
THIS IS ADVOCATING FOR
MECHANISTIC MODELLING.
MANY DOMAIN SCIENTISTS ARE BEING
INFLUENCED BY BIG DATA AND
MACHINE LEARNING IN THEIR
FIELDS.
HOW DO WE ADVERTISE FOR MEC
MECHANISTIC MODELLING WHEN MANY
USE IN THE FIELD IS TOWARDS A.I.
AND MACHINE LEARNING?
>> BIG TOPIC BUT I'LL TRY.
THE TEMPTATION FOR FUNDING AND
TO BE VISIBLE IN THE FIELD IS TO
FOLLOW FASHION TRENDS.
I UNDERSTAND THAT TEMPTATION
WITH MACHINE LEARNING IT'S
POWERFUL AND THINGS IT'S GOOD AT
AND THINGS IT'S NOT GOOD AT.
THAT'S THE THING TO LOOK OUT FOR
IN ALL FASHION TRENDS IS WHERE
IS THE LOW-HANGING FRUIT?
WHERE ARE THE THINGS THEY CAN
REALLY DO WELL AND HOW DO YOU
IDENTIFY THOSE THINGS EARLY ON
RATHER THAN JUST THROWING IT AT
EVERYTHING YOU CAN THINK OF?
THAT'S WHY I SEE MACHINE
LEARNING IT'S BEING THROWN AT
EVERYTHING INSTEAD OF SOMEONE
WITH INSIGHT SAYING HANG ON,
LET'S THINK THOUGHTFULLY WHERE
THIS OUGHT TO BE APPLIED.
>> I THINK MACHINE LEARNING IS
JUST ANOTHER TOOL WE USE IT IN
DEVELOPING SURROGATE MODELS WITH
REPRODUCIBLE MODELS TO CREATE
THE DATA SETS MACHINE LEARNING
CAN LEARN FROM BUT IT'S
IMPORTANT JUST TO SEE IT AS YET
ANOTHER TOOL.
A VERY POWERFUL TOOL IN
COMBINATION WITH CHAT GPT WE'RE
SEEING HOW POWERFUL THAT CAN BE.
NEVERTHELESS IT'S HARD TO BEAT
THE INSIGHTS YOU GET BY
UNDERSTANDING THE MECHANISTIC
INSIGHTS FROM HUNDREDS OF YEARS
OF STUDYING AND UNDERSTANDING
THE PHYSICS BEHIND MECHANISMS.
WE HAVE TO USE BOTH.
I THINK THE KEY IS GOING TO BE
THE A.I. MORE PHYSICALLY
CONSTRAINED YOU'RE USING A.I. IN
A PHYSIC WAYS AND YOU CAN'T
IGNORE WHAT WE LEARNED FROM THE
WAY NATURE WORKS WITH MECHANISMS
BUT IT'S A VALUABLE TOOL AND
NEEDS TO BE USED WITHIN THE
APPROPRIATE CONTEXT AND
HOPEFULLY INCREASINGLY COMBINED
WITH MECHANISTIC MODELLING SO
THE TWO ARE WORKING TOGETHER.
>> I WANT TO ECHO THAT.
IT'S A TOOL, ONE OF MANY.
IT'S A GREAT TOOL.
WE SHOULD HAVE LOTS OF TOOLS IN
OUR TOOLBOX.
WE NEED TO LEARN MORE ABOUT IT
THAN WE WANT TO OR MAYBE WE WANT
TO.
ONE OF MY EARLY PAPERS I SENT
SOMEWHERE AND IT WAS REJECTED
BECAUSE WE DON'T DO MODELLING.
I THOUGHT WOULD A JOURNAL SAY
SORRY, WE DON'T BELIEVE IN FLOW
CYTOMETRY.
IT'S A TOOL.
PUT IT IN THE TOOLBOX.
>> I WOULD AGAIN ECHO BOTH OF
YOU BEFORE.
IT IS A TOOL.
IT'S A GOOD TOOL.
IN FACT, I HAVE I FLOW FROM
FRIENDS THEY'RE GOING TO USE IT
TO PROVE THE BLOWUP OF THE DOSE
EQUATIONS WHICH IS A BIG DEAL IN
MATHEMATICS.
BUT IT IS A TOOL TO NEED TO
UNDERSTAND WHAT'S GOING ON.
AND IT'S VERY USEFUL BUT, YES,
IT'S LIKE CRANKING UP SOME
COMPUTER AND COMPUTING
SOMETHING.
YOU STILL HAVE TO UNDERSTAND
WHAT YOU'RE COMPUTING AND
MECHANISTIC MODELLING GIVES YOU
THAT.
>> GARY AND TAMARA WILL GET TO
TALK ABOUT IT BUT ONE THING WITH
DATA-BASED METHOD IS COUNTER
FACTUALS ARE NOT IN YOUR DATA
SET AND BASED ON EXPERIMENT, IF
NOTHING ELSE MECHANISTIC MODELS
WILL ALWAYS BE NEEDED TO
GENERATE THE TRAINING DATA SETS.
I BELIEVE THERE'S MUCH MORE THAN
THAT THAT MEC NIFT MODELLING IS
IMPORTANT FOR.
I'D LIKE TO COME BACK TO
SOMETHING JENNIFER TALKED ABOUT
THAT I THINK THINGS LIKE CHAT
GPT WILL BE TREMENDOUSLY
POWERFUL FOR MAKING MODELLING
MORE POWERFUL.
I USE CHAT GPT FOR CODING.
I'LL ASK IT TO GENERATE CODE
FOR.
IT ACTUALLY KNOWS COMPUCELL
SOFTWARE THAT WILL GENERATE THEM
WHICH IS AMAZING BECAUSE NOT
MANY OF US USE IT BUT YOU CAN
RUN IT AND SEE IF IT WORKS.
THERE'S A POSSIBILITY BY MAKING
THE BARRIER TO ENTRY MUCH LOWER
WE CAN PULL IN TO THE
MECHANISTIC MODELLING COMMUNITY
A MUCH BIGGER GROUP OF PEOPLE
THAN WERE AVAILABLE BEFORE.
BUILDING NETWORK MODELS IS
ANOTHER PLACE WHERE I THINK
THESE LLMs MAY BE VERY VALUABLE.
WE DON'T WANT TO THINK OF EACH
OTHER AS OPPOSED.
I THINK WE HAVE TO WORK TOGETHER
AND IDENTIFY THE BEST PLACES TO
WORK TOGETHER.
>> I'LL ADD THAT TOMORROW DURING
BREAKOUT SESSION 2, GEORGE AND
MAURICIO AND ALANA WILL HOST THE
MECHANISTIC BREAKOUT SESSION
TALKING ABOUT USING PHYSICS AND
FORM NEURAL NETWORKS USING CHAT
GPT AS WELL.
>> ALL RIGHT.
THE NEXT QUESTION IS REGARDING
NEW MATH FOR BIOLOGY.
THEY'RE INTERESTED IN HEARING
ABOUT EFFORTS IN NEW MATH OR
APPROACHES.
DO YOU HAVE THOUGHTS OR
DIRECTION FOR NEW MATH?
THIS IS SPECIFICALLY RELATED TO
MOMENTUM OF QUANTUM SENSING
APPLICATION AND QUANTUM-DRIVEN
BEHAVIORS IN BIOLOGY.
>> THERE'S NOT THAT MANY
APPLICATIONS AT THE MOMENT WHERE
IT'S OBVIOUS HOW QUANTUM THINKS
COMES INTO PHYSIOLOGY.
THE TRANSPORT CHAIN IS ONE AND
THERE ARE EXAMPLES BUT IT'S
WORTH THINKING ABOUT THE
LINKAGE.
I'M FINDING IT INTERESTING IN
THINKING ABOUT THE WAY WE THINK
ABOUT INTROPY FROM A THERMAL
DYNAMIC REVIEW RATHER THAN HOW
THEORISTS THINK OF IT AS AN
INTRO PY PERSPECTIVE AND WE NEED
TO LEARN FROM PEOPLE THAT KNOW
ABOUT QUANTUM INFORMATION THEORY
AND INTERBRIDGE THAT GAP BETWEEN
THE PHYSICS AND THERMO DYNAMICS
WORLD AND THE INFORMATION WORLD
THAT'S BECOMING SO VITAL TO MANY
OF THE CURRENT A.I. APPLICATION.
I DON'T HAVE ANY ANSWERS BUT
IT'S AN IMPORTANT AREA.
>> I'LL PUT A PLUG IN FOR
BREAKOUT SESSION NUMBER 4,
QUANTUM COMPUTING AND PERHAPS
THEY'LL DELVE INTO PERHAPS
QUANTUM SENSING.
>> GOING BACK TO OUR PREPARED
QUESTIONS, WHAT OPPORTUNITIES
HAVE OPENED UP FOR YOU AS A
RESULT OF YOUR MSM EXPERIENCES?
>> IN ATTENDING THIS MEETING
IT'S A GREAT LEARNING EXPERIENCE
FOR ME.
ALWAYS IS.
THE OPPORTUNITY TO ENGAGE AND
HIRE GREAT Ph.D. STUDENTS AND
THE INTERDISCIPLINARY EXPERIENCE
THEY GET IS FAR BEYOND ANYTHING
I SAW DURING MY Ph.D. FOR SURE.
THE THINGS THEY GO ON AND DO AND
YES, A LOT OF THEM ARE GOING TO
INDUSTRY NOW.
THE NEW COLLABORATIONS HAVE
MEANT A LOT TO ME PERSONALLY AND
PROFESSIONALLY AND THE
OPPORTUNITY TO GET INTO
LYMPHATICS AND EXPLORE THEM
FURTHER AND THE THINGS WE'RE
DOING NOW I NEVER DREAMED OF
DOING THIS KIND OF WORK WHEN I
WAS EARLIER IN MY CAREER.
AGAIN, I'M VERY THANKFUL FOR IT.
>> ANYONE ELSE?
ANY NEW OPPORTUNITIES YOUR
EXPERIENCES HAVE ALLOWED FOR?
>> I CAN PERHAPS COMMENT FROM
AGAIN A RATHER PERSONAL POINT OF
VIEW IN TERMS OF OUR WORK AT THE
BIO ENGINEERING INSTITUTE.
WE BUILT THE INSTITUTE ON THE
PREMISE THAT YOU REALLY NEEDED
THREE THINGS.
YOU NEEDED TO BE DEVELOPING NEW
INSTRUMENTATION BECAUSE MOST NEW
SCIENCE COMES WHEN YOU THINK OF
A WAY OF DEVELOPING A NEW
INSTRUMENT.
NEED EXPERIMENTALISTS, CLINICAL
AND PRECLINICAL EXPERIMENTAL
WORK OBVIOUSLY TO PROVIDE THE
DATA AND YOU NEED MODELLING TO
INTERPRET THE DATA.
THOSE THREE TOGETHER I THINK
WE'VE ALWAYS SEEN AS THE
FOUNDATION FOR THE BIO
ENGINEERING INSTITUTE.
WHAT I HADN'T ANTICIPATED FROM
THE INSTRUMENTATION SIDE WOULD
COME MEDICAL DEVICE
OPPORTUNITIES AND SPIN-OUT
COMPANY OPPORTUNITIES WHERE A
LOT OF STUDENTS IF YOU CREATE A
CULTURE AND AN ENVIRONMENT WHERE
THE SMALL NUMBER OF STUDENTS WHO
REALLY DO WANT TO BECOME
ENTREPRENEURS AND KNOW HOW TO
DRIVE A COMPANY AND THAT BECOMES
THEIR DIRECTION, OPPOSED TO AN
ACADEMIC DIRECTION, IT BECOMES
EXTREMELY VALUABLE TO HAVE AN
ENVIRONMENT WHERE THOSE PEOPLE
CAN REALLY DEVELOP THEIR SKILLS
AND END UP CREATING COMPANIES
THAT EMPLOYEE THE GRADUATES THAT
COME OUT OF THAT FROM OTHER
PEOPLE.
-- IT'S VERY MULTIDISCIPLINARY
AND YOU GET SERENDIPITOUS
OUTCOMES YOU WOULDN'T ANTICIPATE
FROM THE START AND THAT COULD BE
VERY IMPORTANT IN TERMS OF
MAINTAINING THE ORIGINAL
INFRASTRUCTURE AND OFFERING
OPPORTUNITIES FOR JOBS FOR THE
GRADUATES.
THAT WAS ANN UNEXPECTED
ADVANTAGE FOR US IN HAVING
CREATE THE MULTIDISCIPLINARY
ENVIRONMENT.
>> DENISE, LET ME KNOW ON
TIMING.
>> WE HEARD THE COFFEE AND TEA
IS HERE AND WAITING FOR US.
BEFORE IT GETS COLD WE COULD
ADJOURN UNLESS THERE'S OTHER
COMMENTS?
>> I THOUGHT IT MIGHT BE A GOOD
IDEA TO HAVE THEM ALL HAVE A
CLOSING COMMENT SINCE WE HAVE
SOME TIME IN THE SESSION.
>> WE'LL GO DOWN THE ROAD.
JIMMY, DO YOU WANT TO START?
>> ONE OF THE THINGS IS CURRENT
AND FUTURE PLANS.
I ENJOYED DENISE'S TALK THIS
MORNING AND MADE A LOT OF NOTES.
WE'RE ALSO HEADING MORE IN THE
LYMPH NODES.
YOU HAVE 500 IN YOUR BODY AND
THEY'RE CRUCIAL FOR LOTS OF
DISEASES.
THERE'S AN ISO TROPIC DIFFUSION
TO INFECTION DIFFUSION REACTION
EQUATIONS AND ALL SORTS OF
THINGS INTERESTING TO STUDY
THERE SO THAT'S WHERE I'M
HEADING.
PROBABLY KEEP ME BUSY THE REST
OF MY CAREER.
>> I THINK MY FINAL COMMENT
WOULD JUST BE THAT BIO
ENGINEERING AND PHYSIOLOGY
APPLICATIONS INVOLVING
MULTISCALE MODELING IS AN
INCREDIBLY INTERESTING FIELD
BECAUSE IT EVOLVES -- YOU THINK
OF ANY PHYSIOLOGICAL PROCESS IN
THE BODY AND INVOLVES ALL OF
ENGINEERING, EVERY PROCESS WILL
REQUIRE YOU TO THINK ABOUT
ELECTRICAL CHARGE, TRANSFER,
MACAQUES, BIO CHEMICAL HEAT
TRANSFER, WHATEVER.
WHAT OTHER DISCIPLINE GIVES YOU
THE ABILITY TO PULL IN ALL THOSE
VAST ARRAY OF KNOWLEDGE ABOUT
PHYSICS AND ENGINEERING AND
MATHEMATICS AND COMPUTER SCIENCE
AND ON AND IT REALLY REQUIRES
ALL OF THOSE DISCIPLINES TO BE
WORKING TOGETHER TO SOLVE THESE
PROBLEMS.
I THINK IT'S A VERY VERY
PROMISING EXCITING FIELD FOR
YOUNG PEOPLE TO GET INTO.
>> I'D SAY THAT WE'RE STILL ONLY
BEGINNING TO APPRECIATE THE
COMPLEXITY OF MULTISCALE
BIOLOGICAL INTERACTIONS.
IN A SENSE WHAT WE NEED IS
PATIENCE.
PATIENCE IN DEVELOPING TOOLS.
PATIENCE IN DEVELOPING
COMMUNITY.
PATIENCE IN DEVELOPING
SCIENTIFIC UNDERSTANDING.
I DON'T THINK WE'LL BE OUT OF
PROBLEMS IN 10 YEARS OR EVEN 20
YEARS.
SO I THINK LONG-TERM
PERSPECTIVES FOR TOOLS AND
SCIENTIFIC DEVELOPMENT ARE
IMPORTANT AND IT'S NOT ALWAYS
EASY TO MAINTAIN THE FOCUS.
IT'S WONDERFUL IMAG HAS BEEN
HERE 20 YEARS.
THAT'S A LONGER PERIOD OF
ATTENTION THAN MOST TOPICS HAVE.
AND I THINK YOU HAVE AT LEAST 20
YEARS MORE OF PROBLEMS TO
ADDRESS.
>> ALL AGREE WITH ALL MY BUDDIES
HERE.
THERE'S AMAZING THINGS WE CAN DO
IN THE NEXT COUPLE DECADES.
I HOPE WE CAN GET YOUNG
SCIENTISTS TO JOIN US.
WE'LL NEED ALL THEIR BRAINS AND
ENERGY TO SOLVE SOME OF THE
REALLY BIG PROBLEMS IN THE
WORLD.
>> I'D LIKE TO ECHO ALL MY
PREVIOUS SPEAKERS AND EXPRESS MY
GRATITUDE FOR THE FUNDING THAT
GAVE OUR GROUP THE CRITICAL MASS
AND CREDIBILITY TO DO WONDERFUL
WORK AND AUTO SPECIALLY LAUNCH
CAREERS OF YOUNG RESEARCHERS WHO
NOW HAVE FACULTY POSITIONS HERE
AND EVEN IN CHINA.
>> GREGOR WAS SAYING AM I THE
ONLY MSM FUNDED PROJECT AND I
SAID IT WAS DIVIDED BETWEEN MSM
AND NIH AND NASA AND NSF FUNDING
IN THE EARLY DAYS.
I WANT TO THANK YOU ALL FOR YOUR
COMMENTS BUT ALSO ON THIS WIKI
PAGE YOU'LL SEE THE SURVEY THAT
AHMET ERDEMIR CONDUCTED.
IT'S REALLY A SHAME HE COULDN'T
BE HERE TODAY BECAUSE OF TRAVEL
ISSUES.
HE WAS ACTUALLY THE INSPIRATION
FOR THE MEETING AND SAID, HEY,
LET'S PULL THESE ORIGINAL PEOPLE
AND SEE WHAT THESE 20 YEARS DID
WITH THEM.
LET'S SEE WHAT HAPPENED TO THEM.
IF YOU ALL CAN LOOK AT THE
SURVEY THAT'S IN THE WIKI AND
LOOK AT THE SURVEY RESPONSES AND
LOOK AT AHMET'S SYNTHESIS OF
THESE RESPONSES HE WOULD HAVE
PRESENTED TO YOU.
UNFORTUNATELY THE SOUND SYSTEM
DIDN'T WORK FOR THAT BUT HE
WOULD LOVE TO HEAR YOUR
COMMENTS.
AS WELL AS HE HAS SAID PLEASE
FEEL FREE TO DOWNLOAD THE SURVEY
FORM AND FILL IT OUT YOURSELVES
EVEN IF YOU HAVEN'T BEEN DOING
IT 20 YEARS HE'D LIKE TO SEE
YOUR THOUGHTS.
I'LL END THERE AND THANK
EVERYONE AGAIN.
SO BEFORE EVERYONE GETS UP I'D
LIKE ALL THE IMAG FOLKS IN THE
ROOM TO STAND UP TO BE
RECOGNIZED THE IMAG FOLKS HAVE A
BLUE STICKER ON THEIR BADGES.
IF YOU'RE IMAG, PLEASE STAND UP
AND WAVE.
WE'RE CELEBRATING YOU FEEL FREE
TO STOP THEM WHEN YOU'RE
DRINKING YOUR COFFEE AND ASK
THEM WHAT AGENCY YOU'RE FROM.
WE HAVE 10-PLUS GOVERNMENT
AGENCIES IN IMAG ALL INTERESTED
IN PROMOTING MODELLING FOR
BIOLOGICAL AND BEHAVIORAL
SYSTEM.
ENJOY YOUR COFFEE AND SEE AND
WE'LL MEET HERE JUST BEFORE
10:50.
GOOD MORNING.
I'M FROM THE UNIVERSITY OF
WASHINGTON FROM SEATTLE.
WE'RE STARTING THE PATH TO
CREDIBILITY.
THE IDEA THAT I WAS INSPIRED BY
THE GROUP DID ON THE 10 RULES OF
CREDIBILITY.
I'M INTERESTED IN THE IDEA OF
CREDIBILITY OF MODELS.
I'LL TALK ABOUT STANDARDS
WHETHER YOU LIKE IT OR NOT.
I AM A MICROBIOLOGIST IN THE A
COMPUTER SCIENTIST.
I UNDERSTAND MY PEOPLE RESISTED
BECAUSE WHAT ARE THE BENEFITS TO
USING STANDARDS.
I'LL TRY TO CONVINCE YOU THERE
ARE REAL BENEFITS.
FIRST SOME HISTORY.
THERE WEREN'T MANY MODELERS
THEN.
ONLY THREE MODELLING TOOLS
WIDELY USED AT THAT TIME ON THE
ENTIRE PLANET.
IT WAS A SMALL COMMUNITY AND
THEY WERE SMALL.
VERY FRIENDLY LIKE THIS
COMMUNITY HERE.
PEOPLE WANTED TO CHANGE MODELS
BETWEEN THE TOOLS BECAUSE
DIFFERENT TOOLS HAVE DIFFERENT
STRENGTHS.
WE STARTED THINKING ABOUT THIS.
YES, SO IT WAS MYSELF, PEDRO
MENDEZ, YANI HOFFMYER AND OTHERS
AS WELL STARTED THINKING ABOUT A
FILE FORMAT HOW PEOPLE CAN
EXPORT AND REIMPORT INTO ANOTHER
TOOL.
OF ALL THE AREAS ACADEMIC SHELF
WEAR HAS A SHORT SHELF LIFE AND
I REALIZE THE WAY TO PRESERVE
THEM WAS TO HAVE A COMMON
STANDARD.
THOUGH THE TOOLS DIED THE MODEL
LIVED ON.
I CAN LOAD IN MODELS PUBLISHED
IN 2003 AND STILL GET THEM TO
RUN TODAY.
THE WORLD WAS CHANGING AT THE
TIME ESPECIALLY IN THE U.S. A
PUSH TO GO SYSTEMS BIOLOGY AND
THE REST OF THE WORLD NOTICED
AND THERE WAS A PROGRAM THAT
BASICALLY WAS JAPANESE
GOVERNMENT GIVING OUT BLANK
CHECKS TO SENIOR PEOPLE TO SPEND
MONEY ON WHAT THEY THOUGHT WAS
IMPORTANT.
HIRAKI KATAN WAS ONE OF THOSE
AND WE HAD A HOME TO THAT
PROJECT IN CAL TECH IN PASADENA.
I WAS A COMPUTER CONSULTANT FOR
THE FINE ARTS COMMUNITY IN
EDINBURGH AND HAD A LOT OF FUN
AND ENDED UP IN ACADEMIA IN
PASADENA IN 2000.
THE BIG SURPRISE WAS IT SPURRED
AN ENTIRE COMMUNITY THAT
EMERGED.
AT THE SAME TIME, PETER HUNTER
WAS WORKING ON A STANDARD.
BOTH ARE STILL GOING.
WHAT'S SURPRISING IS A VIBRANT
COMMUNITY EMERGED.
IT'S LIKE AN EXCHANGE STANDARD
FOR EXCHANGING MODELS.
WHAT'S EXCITING ABOUT THAT.
MY PASSION IS HOW CELLS WORK NOT
STANDARDS.
I'VE WRITTEN MORE ON HOW
METABOLISM IS REGULATED THAN
STANDARDS.
A NECESSARY EVIL I RECOGNIZED AT
THE TIME.
A WHOLE SERIES OF NEW EFFORTS
AND STANDARDS EMERGED.
PACKAGED FOR THE DIAGRAMS WE USE
IN SYSTEMS BIOLOGY.
[OFF-MIC]
>> IT'S NOT SHARE?
-- SHARING?
IS IT SHARING NOW?
SEE IF YOU CAN FIND THE CONTROL
PANEL.
>> I DIDN'T MEAN TO INTERRUPT.
>> IT'S OKAY.
>> THE MINUTE YOU HAVE A
STANDARD PEOPLE START COLLECTING
MODELS TOGETHER SO REPOSITORIES
WERE BORN.
THERE'S A BUNCH OF REPOSITORIES
NOW IN EXISTENCE.
THEY HOLD LITERALLY THOUSANDS OF
MODELS NOW.
EITHER IN SPML OR ISPML.
IF YOU'RE INTERESTED IN MORE
THERE'S A WHOLE HISTORY WRITTEN
ON THE COMBINE.
THE NEW ORGANIZATION WAS FORMED
CALLED COMBINE.
THERE'S MORE ON WIKIPEDIA CAN
READ ABOUT AND THE COMMUNITY
GETS TOGETHER.
THE OTHER THING I WANT TO
EMPHASIZE GRACE MENTIONED IN
RESPECT TO IMAG THE EFFORTS ARE
GRASSROOTS.
THERE'S NO TOP DOWN OR
HIERARCHY.
SOMETHING HAPPENS BECAUSE
SOMEBODY COMES IN AND SAYS I
NEED X TO HAPPEN AND NEED TO BE
ABLE TO MODEL THIS OR THAT AND
THE COMMUNITY STARTS TO --
PEOPLE BREAK OFF AND FORM SMALL
GROUPS AND THEN IT COMES BACK
AND THE WHOLE WORK GETS FOLDED
INTO A STANDARD.
WHEN PEOPLE COMPLAIN TO ME YOU
CAN'T DO SOMETHING, I SET GET
INVOLVED AND YOU'LL GET IT.
IT WILL HAPPEN.
I JUST WANT TO EMPHASIZE THAT.
WHEN WE GET TO THIS -- TODAY.
SO THERE'S A CALL DEVELOPED IN
THE FIRST 10 YEARS AND AFTER
THAT THE EXTENSION MECHANISM WAS
CREATED WHERE YOU CAN ADD
PACKAGES EXTENSION PACKAGES TO
SBML AND THEY WERE SUPPORT IMAG.
PROBABLY GRACE DIDN'T REALIZE IT
WAS HAPPENING AT THE TIME BUT IT
WAS.
YOU GET A GRANT AND DO OTHER
THINGS SOMETIMES.
A COUPLE OF THESE EFFORTS
MATERIAL
MATERIALIZED BECAUSE OF THAT AND
THESE MODELS AND LAST YEAR A
SPECK WAS REWRITTEN.
WE HAD MODULAR MODELS IT HASN'T
BEEN USED SO MUCH BUT WE CAN
BREAK UP A MODEL INTO SUB MODELS
AND THEN COMBINE THEM AT THE END
OR SWAP NEW MODELS IN OR OUT.
DISTRIBUTE OF UNCERTAINTIES AND
VISUALIZATION.
BY FAR THE MOST POPULAR ONE OF
ALL WAS CONSTRAINT-BASED MODELS
AND ADD EXTRA INFORMATION
SPECIFIC TO THE MODELS.
GENOME SCALE MODELS WAS BY FAR
THE MOST POPULAR PACKAGE OR
POPULAR USE OF SBML.
THERE'S HUGE DATABASES OF MODELS
ENTIRELY IN SBML.
THIS IS MAINLY THE DEPARTMENT OF
ENERGY EFFORT.
THERE'S OTHER THINGS HELPED
ALONG THE WAY THROUGH IMAG.
THE VISUALIZATION STANDARD.
VERY SUBTLE STANDARDS AND THERE
WAS UML.
THERE WAS A HUGE PUSH FROM IMAG
RESOURCES AND THERE'S AN ENTIRE
PACKAGING MECHANISM THAT SLOWLY
IS BEING ROLLED OUT THAT LETS
YOU PACKAGE EVERYTHING YOU NEED
FOR A MODEL INTO BASICALLY ONE
ZIP FILE ESSENTIAL BUT IT'S A
BIT LIKE A WORD FILE IF YOU
EXCHANGE THE EXTENSION TO ZIP
IT'S LIKE A ZIP FILE AND IT'S
EVERYTHING YOU NEED TO DESCRIBE
A DOCUMENT.
SAME THING IS IN THIS CASE.
VARIOUS THINGS HAVE COME OUT OF
IMAG OF A RESULT AND INITIALLY
THE FUNDING CAME FROM JAPAN AND
THEN TAKEN OVER BY DARPA AND
THEN THE DOD AND THEN NIH AND
ALSO SUPPLEMENTED BY IMAG.
HERE'S SOME TERMINOLOGY AND
WE'LL MOVE INTO THE NEXT TALK IN
A MINUTE BUT WHAT WE'LL TALK
ABOUT FOR THE NEXT HALF AN HOUR.
SBML IS MODEL DESCRIPTION FORMAT
SUB CELLULAR PROCESSES AND
ANTIMONY THE HUMAN READING SBML
AND THE SED-ML A SIMULATION
EXPERIMENT DESCRIPTION LANGUAGE
AND COMBINE IS A WAY TO FORM
EVERYTHING YOU NIGHT FOR A MODEL
AND SHIP IT OFF TO SOMEBODY AND
SAY THIS IS THE ENTIRE THING YOU
NEED TO RUN MY MODEL AND
UNDERSTAND MY MODEL AND SO ON
AND CAN BE PUT ON REPOSITORIES
AS WELL.
I THINK THAT'S ALL I'M GOING TO
SAY ON THAT OTHER THAN TO
REINFORCE IMAG THE LAST 10 YEARS
AT LEAST HAD FAIRLY IMPORTANT
IMPACT ON THE DEVELOPMENT OF
STANDARDS.
GOING FORWARD I KNOW IT'S NOT
VERY EXCITING BUT IF YOU'RE
GOING TO DIGITAL TWINS YOU'LL
HAVE TO BITE THE BULLET AND
EXPAND THE STANDARDS MORE
BECAUSE SBML WILL HAVE TO HAVE
NEW STANDARDS.
WE'LL START THE FIRST PROPER
TALK.
THE 10 RULES FOR MODEL
CREDIBILITY AND THE LATEST
DEVELOPMENTS THERE.
WE HAD ONE LITTLE ISSUE.
THE U.S. TRANSPORTATION SYSTEM
FAILED US.
TWO OF THIS MAIN SPEAKERS WERE
NOT ABLE TO ATTEND DUE TO DELAYS
IN FLIGHTS AND SO ON BUT WE'LL
HAVE SOMEONE TO TAKE OVER TO
TALK ABOUT MODEL CAPABILITY.
>> BELIEVE IT OR NOT I WASN'T
EVEN SUPPOSED TO BE HERE.
SORRY.
CAN YOU HEAR ME BETTER NOW?
OKAY.
ORIGINALLY I WASN'T SUPPOSED TO
BE HERE AND -- THANK YOU.
AT THE LAST SECOND I DECIDED TO
SHOW UP SINCE IT'S THE 20 YEAR.
I TOLD JERRY AND AHMET I DON'T
WANT TO PRESENT.
I JUST WANT TO BE AN ATTENDEE
AND HERBERT SAID JOIN THE PANEL.
I SAID, FINE, I'LL DO THAT BUT I
DON'T WANT TO PRESENT.
HERE WE ARE.
ANY WAY, I'LL GO OVER THE
DEVELOPMENT AND FUTURE OF THE 01
SIMPLE RULES WHICH I THINK MANY
ARE RELATIVELY FAMILIAR BY NOW
AND THANKS TO GRACE FOR
ENCOURAGING THE REST OF THE
COMMUNITY TO APPLY THE RULES
WHICH THE COMMITTEE ON
INCREDIBLE PRACTICE ON MODELLING
SIMULATION HAS DEVELOPED SINCE
2012.
I FIRST GOT INVOLVED WHEN I WAS
AN IMAG REPRESENTATIVE FOR NASA
IN 2011 AND TO SEE HOW THAT HAS
EVOLVED HAS BEEN A VERY EXCITING
ADVENTURE.
I KNOW SOMETHING LIKE INCREDIBLE
PRACTICE TO SAY IT'S EXCITING IS
KIND OF WEIRD.
SO BASICALLY HOW DO WE GET HERE?
LIKE I MENTIONED IN 2011 WHEN I
FIRST GOT INVOLVED I REMEMBER --
REALLY?
MY VOICE DOESN'T PROJECT VERY
WELL.
I APOLOGIZE.
IS THAT BETTER?
I REMEMBER WHEN THEY WERE
LOOKING AROUND THE ROOM GETTING
FRUSTRATING THE WORK WASN'T
GETTING TRANSLATED TO THE CLINIC
AND THE QUESTION IS WHY ISN'T
THIS HAPPENING?
WHAT IT CAME DOWN TO IS
BASICALLY THE END USERS SUCH AS
CLINICIANS DIDN'T KNOW WHETHER
THEY COULD BELIEVE THE MODELS.
THE QUESTION OF LIKE ARE WE
REALLY USING -- ARE WE APPLYING
MODELLING SIMULATION.
WHAT WE DISCOVERED IS THERE WAS
NO COMMON PRACTICE GUIDELINES TO
DO THIS AT LEAST WITHIN THE
MULTISCALE MODELING COMMUNITY AS
IT RELATES TO HEALTH CARE.
FORTUNATELY ORGANIZATIONS LIKE
NASA HAD SOME EXPERIENCE AND THE
BALL GOT ROLLING BUT IT HAS
POTENTIAL TO INFORM HEALTH CARE
RESEARCH, CLINICAL DECISION AS
WELL AS EDUCATION IN HEALTH
CARE.
THE VARIOUS GOVERNMENT AGENCIES
AN INDUSTRY HAD BEEN INVESTING
IN THIS DOMAIN AND THE MODEL ON
THE RIGHT I HAVE SOME
FAMILIARITY WITH AND WE
DISCOVERED WHEN ASTRONAUT ARE ON
LONG DURATION FLIGHTS THERE WERE
CHANGES IN VARIOUS ASPECTS OF
THEIR VISION HEALTH.
ONE WAS SWELLING OF THE OPTIC
DISC NERVE AND ELEVATED ICP AS A
RESULT OF THE FLUID SHIFT WAS
CAUSING PRESSURE AT THE BACK OF
THE EYE AND DISTENSION OF THE
OPTIC NERVE AND SWELLING OF THE
OPTIC NERVE DISC.
THE MODEL WAS FUNDED AND THEY
SIMULATED BASICALLY ELEVATED ICP
AND IT DID GIVE INSIGHT THIS
COULD BE A CAUSAL MECHANISMS.
I WANTED TO EMPHASIZE IT'S AN
INSIGHT NOT THE ANSWER.
IT'S IMPORTANT HOW WE INTERPRET
MODELS AND APPLY THE RESULTS
FROM THEM.
THAT'S PART OF THE CREDIBLE
PRACTICE.
NASA LEARNED THE HARD WAY WHEN
YOU PUSH LIMITS OF MODEL TOO FAR
BEYOND ITS SCOPE IT COULD LEAD
TO SOME FAIRLY CATASTROPHIC
OUTCOMES WHERE THE RE-ASSESSMENT
TEAM WAS RESTRICTED TO USING A
MATHEMATICAL MODEL WITH HERITAGE
AND CONSIDERED TO BE
CONSERVATIVE BECAUSE OF ITS
HERITAGE BUT WAS DESIGN FOR
PREDICTING FAIRLY ACCURATELY
WHEN SMALL DEBRIS WOULD IMPACT
LIKE THE AFFECT IT WOULD HAVE
WHICH IS BASICALLY FOR SIZES OF
ABOUT 49 CUBIC CENTIMETERS.
THE FOAM THAT FALLS DURING
LAUNCH IS UP TO 20,000 CUBIC
CENTIMETERS.
VASTLY DIFFERENT SIZES.
THEY USED THE MODEL TO
EXTRAPOLATE IN A FAIRLY
QUALITATIVE WAY AND CAME TO THE
CONCLUSION THAT FOAM FALLING OUT
OF AN ANGLE OF ABOUT 21 DEGREES
THAT THE SHUTTLE SHOULD STILL BE
SAFE DURING THOSE CONDITIONS.
IN REALITY, THAT'S WELL BEYOND
THE SCOPE.
THEY PUT TOO MUCH WEIGHT.
THE MODEL WAS RUN CORRECTLY AND
PREDICTS WERE CORRECT.
BUT THE WEIGHT THEY PUT ON THE
PREDICTION WAS WAY BEYOND THE
SCOPE WHICH RESULTED IN
BASICALLY THE EVENTUAL DISASTER
OF COLOMBIA.
THEY CAME UP WITH NASA STANDARDS
7009 AND TAKING ON LESSONS
LEARNED WE STARTED THE COMMITTEE
ON CREDIBLE PRACTICE AND
MODELLING SIMULATION.
MEMBERS FROM THE FDA WERE ALSO
INVOLVED.
SEVERAL ACADEMIC INSTITUTIONS
AND INDUSTRY.
MAL KERNER AT ANCIS PARTICIPATES
IN THIS AND THEY WORK ON
CONSISTENT TERMINOLOGY.
WHEN TALK TO A BIOLOGIST VERSE
US A MATHEMATICIAN AND ASK THEM
WHAT VERIFICATION VALIDATION
MEANS THEY COULD HAVE DIFFERENT
INTERPRETATIONS.
HAVING A CONSISTENT WAY OF
COMMUNICATING WHAT WE'RE TRYING
TO DO WITH MODELLING SIMULATION
IS VERY IMPORTANT AS PART OF THE
CREDIBILITY WORK FLOW AND
DEMONSTRATE THE WORK FLOWS
BECAUSE IF WE'RE NOT ACTUALLY
ACTIVELY DEMONSTRATING HOW TO GO
THROUGH THE WORK FLOWS IT'S NOT
VERY USEFUL.
TO SUPPORT NEW AREAS OF RESEARCH
AND PROMOTE GOOD PRACTICE.
THESE ARE OUR BASIC CHARGES.
THIS IS BASICALLY HOW WE
STRUCTURED THE COMMITTEE.
THERE'S THE BASIC MEMBERS TO
EXECUTE THE CHARGE OF THE
COMMITTEE AND WE HAVE ADVISORY
COUNCIL MEMBERS THAT ADVISE US
HOW TO MOVE FORWARD AND REVIEW
THE WORK SO THE WORK WE'RE DOING
IS USEFUL FOR MSM COMMUNITY.
WE'RE ALWAYS LOOKING TO ADD NEW
MEMBERS.
I WOULD HIGHLY ENCOURAGE
ESPECIALLY YOUNG INVESTIGATORS
TO GET INVOLVED.
IF YOU GET ON THIS EARLY IN
TERMS OF DEMONSTRATING
CREDIBILITY THE ROBUSTNESS OF
YOUR MODELS ARE GOING TO BE THAT
MUCH BETTER.
AND THERE'S A STANDARD THAT WAS
ESTABLISHED FOR REGULATORY
APPLICATIONS OF WHEN MODELLING
IS USED TO ASSESS THE EFFICACY
OF A NEW BIOMEDICAL DEVICE.
IN ALL THOSE THREE CASES --
>> CAN YOU HEAR ME?
DON'T YOU LOVE TECHNOLOGY?
IT'S NOT ALL STRAIGHTFORWARD.
THANKS FOR WAITING.
I KNOW IT'S THE LAST PART OF
WHAT YOU WERE SAYING AS YOU WERE
INTRODUCING THE 10 SIMPLE RULES
FOR CRITICAL PRACTICE.
MOST OF YOU HEARD ABOUT THE 10
RULES OF CRITICAL PRACTICES AND
YOU HAVE LOOKED AT DIFFERENT
EXAMPLES.
ONE THING WE DID --
>> JERRY, REAL QUICK --
>> CAN YOU MUTE THE NACHER
AUDITORIUM QUICKLY.
>> HOW'S THAT?
>> THANK YOU.
>> IS THAT BETTER?
>> PERFECT.
SORRY ABOUT THAT.
>> GO AHEAD, JERRY.
>> YOU CAN START NOW.
>> IS THIS MUTED?
>> YOU CAN START, JERRY.
>> OKAY.
SORRY ABOUT THAT.
ALL RIGHT.
I'M NOT SURE WHERE I LEFT OFF SO
I'LL GO BACK.
AND I'M SURE MOST ABOUT THE 10
SIMPLE RULES AND MAYBE APPLIED
THEM IN YOUR OWN WORK.
THEY'RE THE INDUSTRY INTEREST TO
ENSURE WE HAVE A TRUST WORTH
CREE SET.
-- TRUSTWORTHY SET.
ONE OF THE FIRST THINGS WE DID
FOR A NATURAL APPLICATION WAS WE
TOOK ONE OF THE MODELS I HAD
DEVELOPED WITH A MODEL AND WE
ADAPTED WHAT WE HAD FROM
STANDARD TO THE 10 SIMPLE RULES.
THERE'S AN EXAMPLE AND THE REST
OF THE COMMUNITY AS FAR AS SO
MANY EXERCISES HAVE BEEN
THROUGH.
THERE'S A NUMBER IN THE SAME
FORMAT UTILIZED FOR CREDIBILITY
OF DIFFERENT IMAG MODELS
AVAILABLE TO THE COMMUNITY.
AND THERE'S A LOT OF EXAMPLES
OUT THERE IF YOU'D LIKE IT TAKE
A LOOK AT THOSE.
THEY'RE VERY INTERESTING TO SEE
WHAT PEOPLE HAVE INTERPRETED.
AND THERE'S DIFFERENT ASPECTS OF
THE RULE TO ADDRESS SOME OF THE
INFORMATION THEY WANT TO
COMMUNICATE ABOUT WHAT THE KNOW
IS IMPORTANT ABOUT THE
CREDIBILITY OF THEIR PROBLEM.
IT'S VERY INSIGHTFUL TO LOOK AT
THOSE EXAMPLES.
NOT JUST THE RULES THEMSELVES
BUT THOSE ARE AVAILABLE AND
THERE'S QUESTIONS HOW WE
INTERPRET THESE APPROACHES.
ONE OF THE ACTIVITIES WE WERE
TASKED TO DO WAS 2018 WE
REVIEWED ALL CREDIBILITY POINTS.
THIS WAS A GROUP FORCE BUILDING
A CREDIBILITY PLAN AND THE
COMMITTEE TOOK A GREAT DEAL OF
TIME EVALUATING.
IT WASN'T JUST LIKE YOUR
VALIDATION ISN'T SUFFICIENT.
WE ARE LOOKING AT THE
CREDIBILITY PRACTICES AND HOW
SUFFICIENT OR INSUFFICIENT OR
NOT PROBABILISTIC IT IS.
THE KEY FROM A GROUP OF
INDIVIDUALS THAT DIDN'T HAVE TO
DO WITH THIS RESEARCH AND FREELY
COMMUNICATE SUFFICIENTLY TO
COMMUNICATE AND UNDERSTAND THE
PROBLEM.
THESE ARE THE RESULTS WE GOT.
CAN YOU LOOK AT THE SUBMISSION.
THERE'S ALSO WHERE THERE'S LIKE
INSUFFICIENT.
KEEP IN MIND FOR US WAS THAT
DIFFERENT PARTS OF WHAT WE
CONSIDERED TO BE THE SUM OF THE
10 RULES AND WAS IMPORTANT OR
NOT IMPORTANT AND FIGURE OUT AND
LOOKING AT HOW DIFFERENT KINDS
OF INFORMATION IS NEEDED AND
VERY SPECIFIC GENERIC SYSTEM
THAT ALLOWS US TO LOOK AT
CREDIBILITY ACROSS THE ENTIRE
RANGE.
THAT'S ONE OF THE PIECES WE
NEEDED TO UNDERSTAND THE ROLES
FOR CREDIBILITY OURSELVES.
ONE THING TO KEEP IN MIND WE
LEAN ON DENISE'S TALK EARLIER
WHERE SHE AND OTHERS WORKED
TOGETHER TO GET FEEDBACK BACK
AND FORTH AND SOME OF OUR
THINKING OF HOW TO COMMUNICATE.
NOT ONLY THAT BUT DID
CORRELATION OF THE INFORMATION
AND FOUND OUT THERE'S PLACES
WHERE THIS COMMUNITY AFFECTS
INDIVIDUAL CATEGORIES OF
INFORMATION.
LIMITATIONS IS ASSOCIATED WITH
USING APPROPRIATE DATA AND WHAT
IS THAT REPRESENTING?
IT SEEMS TO ME A LOT OF THE IS
DATA DRIVEN AND TAILORED AND
CALIBRATED AND THE LIMITATIONS
FOR ACQUIRING THE DATA WAS PART
OF THE PROBLEM AND THAT WAS
ELUCIDATED IN DOING THE
EXERCISE.
YOU MENTIONED THE DEVELOPMENT OF
THESE 10 SIMPLE RULES.
WE HAVE PUBLICATIONS TO FIND THE
10 RULES AND GIVE AN OUTLINE THE
KEY INFORMATION ABOUT THIS THAT
IS CRITICAL IN OUR SUPPLEMENTAL
MATERIAL FOR THE PAPER.
WE'RE ASKING BENEFITS AND
PITFALLS IN NOT HAVING THIS
INFORMATION.
THIS RELATES VERY WELL TO THE
ENGINEERING FIELD AND OTHER WORK
ROBUSTLY REPRESENTED AS THE
DAILY PRACTICE OF MAKING
DECISIONS IN THOSE FIELDS.
IF YOU THINK OF THE APPLICATION
WE WANT THE FEEDBACK OF WHAT'S
GOOD AND WHAT'S NOT.
WHEN WE FIRST STARTED THERE WAS
CONCERNS ABOUT THIS AND
SOMETIMES COMMUNICATION AND WHAT
DOES THIS TAKEAWAY FROM THIS?
FOR ME I WANT TO SAY THANK YOU
FOR LETTING ME TALK AND I
APPRECIATE THE DISCUSSION AND
HOPEFULLY I'LL GET TO BE THERE
AT THE NEXT MEETING.
NOW TURN IT OVER TO AHMED TO
FINISH THE SESSION.
>> THANK YOU, JERRY.
I ASSUME EVERYBODY CAN HEAR ME.
I'M WITH THE COMMITTEE THERE AND
IF YOU REALIZE HISTORICAL
ACTIVITIES FOR THE ORIGIN OF THE
COMMITTEES AND THE RULES AND
WE'RE MOVING MORE TOWARDS
AUTOMIZIZATION OF THESE.
AND IT'S LED BY AND CREATED TO
CREATE THE ASSUMPTION OF MODELS
LIKE THESE.
WE LOOK AT ALL THE RULES AND
THERE'S RULES LIKE ADOPTED IN A
PRACTICE.
AND THE INFORMATION PROVIDED IN
REALIZATION TO PRACTICE AND THE
THEORY OF THE WHOLE PRACTICE
THROUGH THE LENS PROPOSED AND
FORTIF
FORTIFIED AND DIFFERENT DOMAINS
AND WE APPLY THESE AND
UNDERSTANDING THE COMMUNICATION
AN OUTREACH AND THIS PROVIDES
AND OPERATIONALIZES THE 10
SIMPLE RULES AND FOR OUR PURPOSE
MAY NOT BE ABOUT COMMUNICATING
OUTREACH BUT MAY BE
IMPLEMENTATION PRACTICE.
WE'RE FORTUNATE TO SEE THE
ADOPTION OF THE COMMITTEE WAS
TAKEN WELL AND OUR PUBLICATION
CAN REACH BUT ALSO BEING USED IN
A SENSE IN AN EXPLORATORY SENSE
AND IN STORIES AND THERE'S GOOD
EXAMPLES.
IT'S REVIEWED.
WHERE ARE WE GOING FROM HERE ON?
IT WAS MADE POSSIBLE BY THE
COMMUNITY.
THIS WAS MADE POSSIBLE BY THE
IMAG AND MSM COMMUNITY.
IT WAS BEYOND EACH OF OUR
RESEARCH PROGRAMS.
IT SYNERGIZED THE DIVERSE
PERSPECTIVES FROM DIFFERENT
AGENCIES, DIFFERENT
INSTITUTIONS, DIFFERENT
BIOMEDICAL DOMAINS AND THERE'S
RUBRIC SUPPORTING WHERE THEY ARE
WITH MODELLING DOMAINS AND
BIOMEDICAL DOMAINS.
OF COURSE IT ALSO PROVIDES
SPECIALIZATION OPPORTUNITY GOING
DEEPER IN A CERTAIN TYPE OF
MODELLING FRAMEWORK OR CERTAIN
BIOMEDICAL DOMAIN WHERE WE CAN
ESTABLISH CONFORMANCE THRESHOLDS
AND CHAINS.
AND THIS IS A GREAT TIME TO
LET'S FACE IT, WE ALL THOUGHT
THE TOOLS ARE LIKE COMMON SENSE
BUT WE REALIZE WE MAY NOT BE
LINED UP IN THEIR COMPLETENESS
AND ENTIRETY OR EVEN COMMUNICATE
THEM.
IT HELPS US REMIND OURSELVES AND
HELPS US WITH MODELLING
SIMULATION AND THE WHOLE ECO
SYSTEM.
AND WE ALSO FEEL LIKE THE RULES
ARE AGNOSTIC IN A SENSE AND
DOING THE MODELLING I'M TALKING
ABOUT DATA-DRIVEN APPROACHES
INCLUDING A.I./M.L. AND IT'S THE
MANIFESTATION OF THE CONCEPTS
MODELLING AND WE'RE HOPING IT
MIGHT BE A JUMPING BOARD FOR
METASCIENCE PRACTICES.
THIS WAS MORE OR LESS VOLUNTEER
WORK AND THERE WAS AN ADVISORY
BOARD AND WE'RE LOOKING FOR
GOVERNMENT PRESSURE BECAUSE WE
LIKE TO DIVERSIFY COMMUNITY
ENGAGEMENT.
WE WILL SEE HOW THE RULES MAKE
PRACTICE ITSELF AND THUS THEY'RE
LOOKING FOR CONTRIBUTIONS I'LL
STOP HERE AND TAKE YOUR QUESTION
S
>> THANK YOU FOR THAT TALK.
TWO ABSENT FANS.
>> HERBERT, DO YOU MIND MUTING
NATCHER.
OKAY.
SO I'M BASICALLY GOING TO TALK
ABOUT SOME ASPECTS OF
CREDIBILITY.
I WAS INSPIRED BY THE WORK THIS
GROUP DID ON THE 10 SIMPLE
RULES.
SOME HAVE TAKEN IT ON BOARD AS
SOMETHING TO PURSUE FOR THE
FUTURE.
SO SOME OF MY COLLEAGUES WE RUN
THE CENTER OF REPRODUCIBILITY
CENTER FOR NIBIB.
SOME OF THE WORK I'LL TALK ABOUT
HERE IS FROM THE CENTER.
I'LL TALK ABOUT WHAT WE HAVE
DONE AND THEN POSSIBLE FUTURE
DIRECTIONS ESPECIALLY IN
RELATION TO MODEL CREDIBILITY.
THE FIRST THING IS AND I THINK
YOU MENTIONED IN YOUR COMMENTS
IN THE WIKI.
THIS SPREAD IN EUROPE AND NOW
COMING ACROSS THE POND AS A SET
OF PRINCIPLES WE SHOULD TRY TO
FOLLOW.
FINDABILITY, ACCESSIBILITY,
INTEROPERABILITY AND
REUSABILITY.
THE ONE QUESTION IS HOW IT
APPLIES TO THE COMMUNITY.
I'D LIKE TO ADD A COUPLE MORE TO
THAT.
UNFORTUNATELY IT DOESN'T MAKE A
NICE ACRONYM.
I HEARD THE STORY FAIR WAS
PICKED SPECIFICALLY BECAUSE
POLITICIANS COULD APPRECIATE IT
BECAUSE IT WAS NICE AND EASY AND
MEANT SOMETHING FAIR.
I CAN'T GET A NICE ACRONYM FROM
THE OTHER THINGS.
FOR THIS COMMUNITY A COUPLE
THINGS WE COULD STRIVE FOR.
THE BOTTOM LINE IS CREDIBILITY.
IS WORK REPRODUCIBLE AND IS YOUR
WORK REUSABLE?
CAN I TAKE YOUR MODEL, EXTEND IT
AND TAKE PIECES OUT.
THE OTHER THING I'M PUSHING THE
UNDERSTANDABLE BECAUSE WE'RE
RUNNING INTO MACHINE LEARNING
AND A.I. WHERE UNDERSTANDABLE IS
LESS AND LESS SO IS YOUR MODEL
UNDERSTANDABLE.
I CAN TURN THESE INTO SENTENCES.
REPRODUCIBILITY CAN WE CREATE
WHAT YOU DID?
CAN YOU CREATE A PUBLISHED MODEL
IN NEW WORK OR PULL THINGS OUT
OF YOUR MODEL AND REUSE IN
ANOTHER MODEL.
CAN I UNDERSTAND WHAT YOU DID?
WHY DID YOU MAKE THE MODEL YOU
DID?
WHAT ASSUMPTIONS DID YOU MAKE?
WHERE YOU GET YOUR DATA FROM?
AND CREDIBILITY, THE BIG ONE CAN
WE TRUST YOUR MODEL.
FROM THE COVID CRISIS IT MADE IT
CLEAR THERE'S A NEED FOR A WAY
FOR YOUR AVERAGE POLITICIAN TO
ASSESS TO USE ONE MODEL OR
ANOTHER AND WHETHER A SCIENTIFIC
ADVISER COULD HAND THAT
INFORMATION OVER TO A POLITICIAN
IN A DIGESTIBLE FORM.
DURING THE COVID CRISIS
TRILLIONS OF DOLLARS WERE AT
STAKE BASED ON PREDICTIONS MADE
BY MODELS AND NO FORMAL WAY TO
ASSESS THE CREDIBILITY OF THE
MODELS.
I THINK CREDIBILITY IS REALLY
HIGH UP.
IF YOU'RE GOING TO MOVE TOWARDS
DIGITAL TWINS THAN CREDIBILITY
HAS TO BE HIGHER ON THE LIST AS
WELL.
I'M GOING TO GO THROUGH THESE
POINTS HERE.
REPRODUCIBLE AND
UNDERSTANDABILITY AND THE
OTHERS.
REPRODUCIBILITY.
MOST MODELS ARE NOT
REPRODUCIBLE.
THERE'S A COUPLE EXAMPLES.
THERE'S HUNDREDS OF THESE IF NOT
THOUSANDS.
IT'S NOT DONE ON PURPOSE.
THE REWARD SYSTEM IS SKEWED AND
PEOPLE ARE NOT REWARDED FOR
PUBLISHING REPRODUCIBLE MODELS
AND THERE'S A FUNNY ONE ON THE
LEFT HAND CORNER, LESSONS LEARN
FROM QUANTUM BIOLOGY AND THE
ANALYSIS PROCEDURE AND ONE
LESSON LEARNED IS MAKE SURE YOU
INCLUDE YOUR ANALYSIS PROCEDURE.
A COUPLE MODELS HERE ARE NOT
REPRODUCIBLE AT ALL NO MATTER
WHAT YOU DO.
THE INTERESTING ONE IS THE
MICROPLASM ONE THE WHOLE CELL
MODEL WAS NOT REPRODUCIBLE.
THERE'S AN ENTIRE WORKSHOP
DEVOTED TO REPRODUCE THAT AND
THAT DOESN'T WORK.
THIS VERY COMMON.
IN THE A.I. WORLD THE MACHINE
LEARNING WORLD MORE COMMON.
I CAN SAY THERE'S GOOD NEWS.
15 YEARS AGO 100% BETWEEN 97%
AND 100% OF ALL MODELS IN
SYSTEMS BIOLOGY COULD NOT BE
REPRODUCED FROM THE PUBLISHED
FORM.
I HAVE A SIDE NOTE THERE.
I'M NOT GOING TO DWELL ON THE
TERMINOLOGY USED IN THIS FIELD
BECAUSE THAT'S AN ENTIRE THING.
THERE'S A LOT OF VARIABILITY IN
WHAT PEOPLE MOVE IN
REPRODUCIBILITY AND SO ON.
THERE'S A GREAT PREPRINT ON
VARIOUS TERMINOLOGIES DIFFERENT
COMMUNITIES USE.
15 YEARS AGO PRETTY MUCH EVERY
MODEL WASN'T REPRODUCIBLE.
THIS WAS EXTRACTED FROM THE
CURATION OF OVER 1,000 MODELS.
TODAY 15% ARE REPRODUCIBLE.
THE PAPER THAT CAME OUT A YEAR
OR TWO AGO FROM THE BIO MODELS
TEAM IN CAMBRIDGE DID A LARGE
STUDY OF REPRODUCIBILITY AND
LOOKED AT IT OVER TIME AND THEIR
CONCLUSION WAS 15 YEARS AGO WAS
TERRIBLE AND TODAY IT'S BETTER.
HALF THE MODELS ARE
REPRODUCIBLE.
THERE'S A LARGE NUMBER OF
CURATED MODELS AS WELL.
MOSTLY IN SBML FORMAT.
THE GROUP THAT WROTE THE PAPER
GET A LOT OF TRAFFIC TO THEIR
SITE FOR MODELS.
FROM FINE TO SYSTEMS BIOLOGY.
I WILL ADMIT THERE ARE NO
STANDARDS FOR MULTISCALE
MODELING.
THIS SAY BIG GAP IN THE FIELD.
THERE'S NO STANDARD FOR
REPRESENTING AGENT-BASED
MODELLING OR ANY MULTI-CELLULAR
MODEL REALLY.
SO I'M GOING TO TALK ABOUT SBML
AND THAT'S THE ONLY ONE WE CAN
TALK ABOUT THE MOST WELL
DEVELOPED AND POPULAR ONE AS
WELL.
MAYBE IN THE NEAR FUTURE WE CAN
START THINKING ABOUT CREATING
NEW STANDARDS THAT CAN WORK
ALONGSIDE SBML IN THE
AGENT-BASED MODELLING, FOR
EXAMPLE.
ANYWAY, HERE'S A NICE FAT CARROT
FOR US.
THIS WAS PUBLISHED EARLIER LAST
YEAR AND THERE WAS A GERMAN
GROUP THAT DID ANALYSIS ON THE
CITATION OF MODELLING PAPERS.
THEY CAME TO THE CONCLUSION IF
YOU DO USE SOMETHING LIKE THE
STANDARD LIKE SBML YOU'RE FAR
MORE LIKELY TO BE CITED.
THE REASON FOR THAT THEY THINK
IS SUCH MODELS ARE REUSABLE.
IF YOU CAN'T REUSE THE MODEL,
NOBODY'S GOING TO REUSE IT AND
PROBABLY CITE THE PAPER.
IT'S GREAT INCENTIVE TO PUBLISH
REPRODUCIBLE MODELS YOUR WORK
WILL HAVE A HIGHER IMPACT AND
THAT'S WHAT WE ALL WANT.
THE REUSABLE MODELS ARE CITED.
WHAT KIND OF INFRASTRUCTURE HAVE
WE BEEN WORKING ON IN OUR
CENTER?
WE HAVE TWO MAJOR SITES.
WE MENTIONED ONE THE
REPRODUCIBILITY PORTAL.
THIS IS ATTACHED TO MAJOR
RESOURCE THAT IS HOSTED AT
UCONN.
THEY HAVE 1300 MODELS LARGELY
CURATED THAT WOULD REPRODUCE
SIMULATIONS.
SO 1300 ARTICLES YOU CAN SEARCH
FOR.
THIS IS A SIMPLE PORTAL USES THE
REST API WHERE YOU CAN SEARCH
FOR PAPERS AND PICK THEM UP AND
YOU'LL GET DETAILS ON THE PAPER
AND YOU'LL GET THE DATA THAT WAS
SIMULATED AT UCONN AND YOU'LL
GET THE MODEL ITSELF IN THE
BLACK PANEL.
YOU CAN CUT AND PASTE AND USE IT
STRAIGHT AWAY.
IT'S A USEFUL RESOURCE IF YOU
HAVE A MODEL YOU'D LIKE TO REUSE
AND IF YOU HAVE A MULTISCALE
MODEL YOU MAY GET ONE PIECE THIS
WAY.
BIO SIMULATION IS A BIG THING
AND THERE'S MANY SIMULATORS
BROUGHT IN FROM THE VARIOUS
GROUPS.
15 TO 20 SIMULATORS OF VARIOUS
KINDS.
SIMULATION I SEE AS A GREAT SITE
FOR DOING MODEL VERIFICATION.
IN FACT YAN WAS TELLING ME THIS
MORNING OVER 1300 MODELS PRETTY
MUCH ALL HAVE BEEN VERIFIED,
MEANING, EACH ONE HAS BEEN RUN
ON TWO COMPLETELY DIFFERENT
SIMULATORS AND HAVE GIVEN THE
SAME RESULT.
MAY THINK, OF COURSE, THAT WILL
HAPPEN.
NOT SO FAST.
A LOT OF CURATION AND EVERY ONE
OF THOSE MODELS HAS GONE
THROUGH.
IF I RUN IT ON THE PLATFORM WILL
I GET THE SAME ANSWER.
AND YOU CAN RUN THIS FOR
VERIFICATION.
THIS IS ALL THE STANDARDS I CAN
THINK OF.
A BUNCH OF STUFF THAT COMES IN
BOTH STANDARDIZED THE WAY OF
SIMULATION PROJECT IS SET UP AND
STANDARDIZED THE WAY THE RESULTS
HAVE GENERATED AND THE WAY THE
DATA IS VISUALIZED.
THIS MEANS YOU CAN GO IN, PICK A
MODEL, RUN IT, SEE THE SAME
RESULTS YOU GET IN THE
PUBLICATION AND SAY RUN IT.
THIS IS FROM 18 RESEARCHERS OVER
FOUR YEARS.
A VERY BIG PROJECT.
THIS IS IF I RAN THE SAME WOULD
I GET THE SAME AND SIMILAR TO
THE WAY YOU IMPLEMENTED IT ON
THE PLATFORM YOU USED.
I WANT TO CONFIRM THERE'S NO
WEIRD ARTIFACTS BY REGENERATING
THE MODEL ON A COMPLETELY NEW
PLATFORM.
IT'S EASY TO RUN PUBLISHED
SIMULATIONS.
AND SOME SIMULATORS ARE HARD TO
INSTALL.
YOU WANT TO HAVE A QUICK LOOK, A
QUICK TRY AND CAN GO AND RUN IT
ON A SIMULATOR YOU'LL FIND.
IT HAS OVER 1,000 -- IT'S NOW
1300 REPRODUCIBLE MODELS.
YOU CAN DOWNLOAD THOSE MODELS.
AND THEY'RE ALL ATTACHED TO
PUBLISHED ARTICLES.
DOWNLOAD THEM AS RUNABLE AND THE
REPRODUCIBILITY IS A SITE THAT
USED THE REST API.
THE SIMULATION IS A BIT OF A
LABYRINTH BUT THE BIO SIMULATION
PORTAL FOCUSES JUST ON SEARCHING
FOR A PAPER AND BE THE SEEING
THE SIMULATIONS FROM THE PAPER
DOWNLOADING THEM.
ALL RIGHT.
THIS IS WHAT THE BIO SIMULATION
SITE LOOKS LIKE AS WELL.
IT'S A FAIRLY BIG SITE.
THERE'S A LOT OF PARTS TO IT.
IF YOU WANT TO HAVE A QUICK LOOK
IN GO TO THIS SITE AS WELL.
IF YOU LIKE LOOKING AT ALL THE
CORNERS, YOU CAN EASILY GET
LOST.
THE FUTURE EFFORTS ON THIS SIDE
ON THE REPRODUCIBILITY SIDE,
MENTIONED MUCH ABOUT SAED-ML ITS
AN ATTEMPT TO TRY TO DESCRIBE
HOW YOU ROUND THE MODEL IN AN
EXECUTABLE INDEPENDENT WAY.
IT'S LIKE A COOKING RECIPE WHERE
I DID THIS AND THEN THIS.
IT'S BASICALLY A HIGH-LEVEL
DESCRIPTION AND THEN CONVERT
THAT TO AN EXECUTABLE FORM LIKE
A PYTHON OR JULIUS SCRIPT AND
THEN RUN IT.
YOU CAN RETARGET THE SIMULATION
EXPERIMENT TO ANYTHING YOU WANT.
TODAY, PYTHON IS THE FLAVOR OF
THE MONTH.
10 YEARS IT WILL PROBABLY BE
JULIA.
YOU NEED THE FLEXIBILITY TO MOVE
FROM FLAVOR OF THE MONTH TO
FLAVOR OF THE MONTH AND THIS
WILL LET YOU DO IT.
THERE ARE LIMITATIONS.
ONE OF THE THING IS TO REVAMP
SED-ML AND THERE'S MULTISCALE
MODELS.
THIS IS THE BIG MISSING PIECE IN
THE ENTIRE DOMAIN.
WE'D LIKE TO HAVE MODELERS
REPRODUCE MULTI-SCALE MODELS
BECAUSE MOST AREN'T.
HELP PACKAGE UP MULTI-SCALE
MODELS BECAUSE THEY'RE COMPLEX.
THE AREA HASN'T DEVELOPED MUCH
BECAUSE IT'S A COMPLEX AREA.
IT'S NOT AN EASY THING TO DO.
WE'LL WORK WITH PEOPLE AND THEN
OTHERS IN THE AUDIENCE.
THEY'RE THREE BIG PLATFORMS.
KE CAN WORK WITH THEM AND GET IT
WORKING WE'LL DO WELL.
THIS WAS COPIED FROM GOOLG AND
THIS IS A FRAGMENT OF THIS AND
442 LINES OF MATH LAB CODE AND
39 AND IT TOOK US A MONTH TO
CREATE THIS IN STANDARD FORM TO
RUN IT ON ANY PLATFORM.
AND THERE'S SIMULATION
PLATFORMS.
IT TOOK TIME TO MAKE IT
REPRODUCIBLE.
WE DO NOT RECOMMEND WRITING YOUR
MODELS IN SOME PLATFORMS AND GO
FOR SOMETHING LIKE SBML.
IT WILL FUTURE PROOF YOUR MODEL
AND MAKE THE MODEL MORE REUSABLE
AND SO ON.
THIS IS NOW REUSABLE FORM.
YOU SEE ALL THE ACTIONS RELATED
TO THE MODEL.
THOSE CAN BE CUT AND PASTED AND
PUT INTO OTHER MODELS.
TO GIVE AN EXAMPLE, I'M NOT
GIVING A LINK TO THIS BECAUSE
IT'S STILL UNDERGOING, WE HAVE
CREATED A STANDARD MODEL FOR THE
EGF PATHWAY BROKEN INTO SUB
MODELS BECAUSE IT'S SUCH A BIG
SYSTEM.
YOU CAN SEE THIS IS ON THE
GITHUB PAGE AND SEE THE MODELS
THERE THE GAP MODULE AND GAREN
MODEL AND SUS MODEL AND SO ON.
THIS GETS PUT INTO AN EXECUTABLE
AND RUN AND WE CAN DO THE
VALIDATION AND FITTING MENTIONED
THIS MORNING.
UNDERSTANDABLE MODELS.
I'M NOT GOING TO DELVE TOO LONG
ON THIS AND UNDERSTANDING THIS
CAN MEAN A LOT OF THINGS TO A
LOT OF PEOPLE BUT FROM THIS
POINT OF CONTEXT CAN I IDENTIFY
THE PARTS YOU USED IN YOUR MODEL
UNAMBIGUOUS LOY.
IF YOU USED GIC WHAT DOES THAT
MEAN OR XYZ.
WHAT DOES THAT MEAN?
YOU TAKE YOUR MODELS WITH EXTRA
METADATA SO A COMPUTER OR HUMAN
CAN START TO FIGURE OUT WHAT IS
IN YOUR MODEL AND WHAT ARE THE
COMPONENTS AND SO ON.
HO
NOT ONLY THAT BUT WHAT
ASSUMPTIONS WERE MADE AND SO ON.
THIS IS WHAT IT LOOKS LIKE.
THIS IS A PIECE OF XML.
THIS IS WHAT IT LOOKS LIKE WHEN
YOU ADD THE ANNOTATION TERMS.
IT'S ALL COMPUTER READABLE AND
BACK TO A MODELER TO SEE WHAT IS
INSIDE THE MODEL.
WE HAVE VARIOUS TOOLS TO DO THIS
THAT HAVE COME OUT OF THE
CENTER.
ONE IS MODELS LIKE THIS AND IT'S
INCREDIBLY TEDIOUS AND BORING
WHICH IS WHY NOBODY DOES IT.
ONE THING WE DEVELOPED WAS AN
AUTOMATIC ANNOTATION SYSTEM
MERELY FOR METABOLIC MODEL AND
IT WILL ANNOTATE IT FOR YOU BY
MINING EXISTING ANNOTATED MODELS
AND IT WORKS QUITE WELL AND DIG
OUT A FULLY ANNOTATED MODEL FOR
YOU AND THIS LET'S YOU FINE TUNE
THE ANNOTATIONS AND YOU CAN
CORRECT THEM THERE AND BROWSE
BIO MODELS AND AUTOMATIC
CREATION AND SO ON.
THIS IS ANOTHER TOOL YOU CAN USE
ESPECIALLY IF YOU'RE A FAN OF
VISUAL STUDIO CODE.
NOW TO MODEL CREDIBILITY.
THE LAST THING.
THIS WAS DEFINITELY INSPIRED BY
THE GREAT WORK DONE BY THE
COMMITTEE AND INSPIRED BY AN
EXISTING TOOL CALLED MEMOTE.
IT WAS DEVELOPED IN 2020 IT'S A
CREDIBILITY TOOL THAT DOES A
DEEP DIVE TO AN SBML MODEL AND
DOES CHECKS ON IT.
IT'S FOR THE WHOLE GENOME SCALE
MODELLING BECAUSE THEY'RE VERY
LARGE MODELS.
THE BIGGEST ONES ARE LIKE
THOUSANDS OF REACTIONS.
YOU CAN'T MANUALLY CHECK THAT.
AND YOU CAN CHECK MASS BALANCE.
THE FIRST YOU GET THE LONG LIST
OF THINGS THEN GENERATES HOW
WELL YOU DID IN A REPORT.
IF YOU LOOK HERE IT TALKS ABOUT
DYNAMIC CONSTRAINTS YOU PUT IN
AND REACTIONS AND ANNOTATED
PIECES AND THINGS LIKE THAT.
IT'S THIS KIND OF THING THAT CAN
ADD TO THE CREDIBILITY.
I WAS SURPRISED TWO YEARS AGO IN
ANOTHER PROJECT I'M DOING WE
NEEDED TO MODEL A LARGE MODEL
AND WE NEEDED TO GET HOLD OF THE
MODEL AND SOMEBODY IN THE GROUP
SAID HAVE YOU CHECKED IT WITH
MEMOTE YET.
I WAS SURPRISED.
PEOPLE ARE USING THIS BEFORE
THEY HAVE A MODEL TO USE IT.
THESE ARE THE 10 SIMPLE RULES
AND WOULD LIKE TO DO DEEP DIVES
IN THE SPECTRUM.
WE HAVE TWO PAPERS WE PUBLISHED.
ONE IN 2019 BASICALLY BRINGING
MORE COMPUTER SCIENCE
METHODOLOGIES TO MODELLING AND
GOT A REVIEW WHICH INCLUDES
DISCUSSION ON THE SIMPLE RULES
THERE AS WELL.
THE IDEA WE WOULD COMBINE --
YOU'D GET A COMBINE WHICH HAS
ALL THE INFORMATION ON THE MODEL
THEN YOU COULD DO A BATTERY OF
TESTS OF VARIOUS CREDIBILITY
TESTS AUTOMATICALLY AND SCORE AT
THE END.
THESE ARE THE KINDS OF THINGS
YOU COULD DO.
WE WANT TO BREAK UP THE STATIC
AND DYNAMIC TESTS.
DO TEST ON THE MODEL WITHOUT
RUNNING THE MODEL AND DYNAMIC
TEST ACTUALLY RUN THE MODEL.
THESE ARE THE KINDS OF THINGS
WE'RE WORKING ON.
VERIFICATION WE'RE WORKING ON
THROUGH THE BIOLATION SITE.
AND YOU CAN LOOK AT THE MODEL
AND IT GOES THROUGH CHECKING
THROUGH IT MAKE SURE THEY MAKE
SENSE.
LARGE MODELS IT BECOMES
IMPOSSIBLE TO DO MANUAL CHECKS
AND FOR THE NERDS OR GEEKS IN
THE AUDIENCE INTERESTED IN HOW
THIS WAS IMPLEMENTED, WE'RE
MOVING MORE AND MORE TOWARD
CLOUD-SIDE WEB APPS ON GITHUB
WHICH IS FREE OR CLOUD FAIR.
THIS RUNS IN THE BROWSER AND
RUNNING PYTHON IN THE BROWSER.
A NEW TECHNOLOGY CAME OUT LAST
YEAR AND COMBINED THE ESTIMATES
INTO DESCRIPTORS AND IT'S
SUPPORTED NOW ON THE WEB.
WE GET TO DOUBLE DIP WHICH IS
NICE.
FOR C + + AND PYTHON TECHNOLOGY.
THIS IS MORE ROBUST TO CHANGES
IN TECHNOLOGY I CAN DO PAGES
FROM THE 1990s AND RUN DESK TOP
APPS FROM THE 1990s.
EVEN WHEN YOUR GRANT STOPS THESE
THINGS KEEP RUNNING.
LOTS OF PEOPLE INVOLVED.
WE HAD SOMEONE THAT STARTED A
MODELLING COMPANY AND WE'RE
STILL IN COMMUNICATION.
WE HAVE NEW PEOPLE BROUGHT IN
THE CENTER I WANT TO GIVE THANKS
TO JOE HALLESTEIN AND DOES A
TREMENDOUS OF WORK WITH US AND
WE WORKED A LOT WITH TEAMS ON
THE U.K.
AND KM SMITH RECURATED MODELS TO
MAKE SURE THEY COULD ALL BE
VERIFIED.
I THINK THERE'S MORE PEOPLE I
COULD MENTION BUT THERE ARE MORE
PEOPLE AND THE FUNDERS AND THE
NATIONAL SCIENCE FOUNDATION AND
A NUMBER OF NIH INSTITUTES.
THANK YOU VERY MUCH.
>> SO WE'RE NOW MOVING TO THE
QUESTION AND ANSWER SESSION.
THERE SHOULD BE ANOTHER TWO
PEOPLE BUT I THINK THEY'LL BE
ONLINE.
IF WE HAD A HIGH SPEED TRAIN
SYSTEM HERE THEY'D BE HERE BUT
WE DON'T.
>> I CAN'T FIND THE THING.
WHAT DO YOU GUYS WANT TO ADDRESS
FIRST?
I'LL HAVE TO SPEAK UP MORE.
AM I SUPPOSED TO TURN IT UP?
AHMED?
>> AHMED WAS ASKING ME TO
REFRESH THE PAGE.
>> CAN YOU TRANSLATES AND SAY
WHAT AHMED JUST SAID?
I DIDN'T CATCH IT.
>> HE SAID IF THEY HAVE ANY
QUESTIONS THEY CAN PUT IN THE
CHAT IN THE WIKI, PLEASE.
IS THERE ANYTHING FIRST?
YOU GUYS CAN POST YOUR ANSWERS
ON THE WIKI IF YOU DON'T HAVE
TIME TO VOICE IT.
AND PRESS THE BUTTON.
>> I THINK WE ARE NOT SUPPORTED
TO MAKE SUPPOSED TO MAKE MODELS
REUSABLE.
NO ONE'S FORCING KNOW DO
ANYTHING BUT IT'S NICE IF THEY
ARE REUSABLE AND IT SEEMS WE'LL
MAKE A BIGGER IMPACT IF THE
MODELS ARE REUSABLE BECAUSE
PEOPLE WILL BE ABLE TO REUSE
THEM.
IN TERMS OF THE SCIENTIFIC
IMPACT WILL BE HIGHER I THINK.
>> CAN I MAKE A COMMENT?
SO I WAS ACTUALLY JUST TALKING
WITH JOHN KORVATZI THE MEDICAL
DEVICE COMMUNITY IS DYING FOR
THE MULTI-SCALE MODELLING THEY
END UP GOING TO THE DRAWING
BOARD THEMSELVES THERE'S
MULTISCALE MODELS.
>> THE MULTISCALE MODELING
CONSORTIUM IS A GROUP OF
MODELERS WHO WANT TO JUST FIND
OUR MODELS.
I'M HOPING TO WORK WITH THEM TO
FIGURE OUT IF THEY CAN DO A
NEEDS ASSESSMENT TO TELL US WHAT
KIND OF MODELS THEY WANT.
BUT THEY START WITH LITERATURE
AND FIND HUNDREDS OF MODELS FOR
SHOULDER MODELS IF THEY WANTED
TO MAKE SHOULDER PROSTHESIS AND
THEY HAVE NO IDEA WHICH ONE TO
START WITH OR WHERE TO GO OR HOW
FAR IT'S GONE.
>> IT'S PROBABLY ALL WRITTEN IN
DIFFERENT FORMS AS WELL.
>> IT'S VERY DIFFICULT TO TEAR
THEM A PART AND DO THE BITS YOU
NEED.
>> WE CAN THINK OF THE CHALLENGE
S.
IN SOME SENSE SOME ARE BIGGER
THAN THE OTHERS.
USING A MODEL FOR A RESEARCH
PURPOSE IS DIFFERENT THAN BEING
ABLE TO REUSE OR ADAPT OR MODIFY
FOR OTHER PURPOSES AND MAINTAIN
AND SUSTAIN THE MODEL.
>> I LIKE TO THINK WHEN I'M IN A
RESEARCH DOMAIN AND TRYING TO
LEARN ABOUT THE PHYSICAL
PRINCIPLES AND WE NEED TO HAVE
HOW THE BODY CHANGE AND EVALUATE
THE INPUT FROM THE MODEL AND HOW
IT FEEDS INTO YOUR DECISION
MAKING.
AROUND IT'S RELATED TO THE
CONTENT OF APPLICATIONS.
AND WHAT YOU FIND IN THE
LITERATURE THEY MAY NOT HAVE
ANYTHING TO DO THOUGH THEY SAID
THEY'RE A MODEL TO PROVIDE THE
INFORMATION NEEDED ABOUT THE
QUESTIONS AS MUCH AS HOW THE
FRAMEWORK HAVING THE MODEL
THAT'S ALREADY BEEN USED.
>> I HAVE TERRIBLE LUCK WITH
MICS IF YOU HAVEN'T NOTICED.
ANY WAY, GRACE, JERRY AND AHMED
HAVE DONE A GREAT JOB OF
ANSWERING SOME QUESTIONS ONLINE.
I'LL FOCUS ON SOME OF THE
QUESTIONS ASKED THAT HAVE NOT
BEEN ANSWERED IF THAT'S OKAY
WITH YOU.
SOME ARE NEAR AND DEAR TO MY
HEART.
>> TELL ME WHICH ONE AND I'LL
SCROLL TO IT.
>> UNDERSTANDING WHEN WE'VE DONE
ENOUGH.
THAT'S A QUESTION THAT ALWAYS
COMES UP.
IF YOU GO INTO IT JUST KNOWING
WHEN TO BUILD A MODEL YOU'LL
NEVER BE DONE SO YOU HAVE TO
DEFINE YOUR CONTEXT CLEARLY.
IF YOU LOOK AT THE RUBRIC
THERE'S THREE ASPECTS YOU SHOULD
LOOK AT.
WHAT YOU'LL USE IT FOR AND HOW
WILL IT INFLUENCE YOUR DECISION
MAKING PROCESS WHETHER FOR
RESEARCH, CLINICAL OR
EDUCATIONAL PURPOSES.
ONCE YOU'VE ANSWERED THE
QUESTIONS CLEARLY IN DEFINING
YOUR CONTEXT THEN AT THAT POINT
YOU KNOW HOW MUCH CLARIFICATION,
VALIDATION AND DOCUMENTATION AND
EXTERNAL REVIEW YOU NEED AND SO
FORTH.
THAT'S WHAT DRIVES THE THRESHOLD
YOU NEED TO BASICALLY DO YOUR
CREDIBILITY PROCESS.
AND ONCE YOU'VE DEFINED THAT YOU
TAKE IT THROUGH AND GO THROUGH
AN ITERATIVE PROCESS AND
SOMEWHERE DOWN THE LINE IF YOU
DECIDE TO CHANGE ASPECT OF YOUR
MODELLING SIMULATION OBJECTIVES,
THEN YOU HAVE TO GO BACK AND
REASSESS YOUR CONTEXT AND GO
THROUGH THE PROCESS AGAIN.
THAT'S HOW YOU'RE GOING TO KNOW
WHEN YOU'VE DONE ENOUGH.
>> IF THEY WANT TO USE THE MODEL
UP A SLIGHTLY DIFFERENT CONTEXT
THEN HOW DO THEY REUSE IT?
OR --
>> THAT'S BASICALLY WHAT I WAS
GETTING AT WHEN I SAID AT SOME
POINT DOWN THE ROAD WHETHER YOU
AS THE PERSON WHO DEVELOPED IT
OR SOMEBODY WANTS TO REFINE IT
OR CHANGE IT SLIGHTLY YOU'LL
HAVE TO GO THROUGH THE PROCESS
OF EVALUATING THE THRESHOLDS.
THEN BASICALLY YOU HAVE TO
ENSURE YOU'RE MEETING -- BECAUSE
MAYBE THE DEGREE TO WHICH YOU'RE
TRYING TO USE IT IS AT LOWER
THRESHOLD AND YOU SHOULD BE
GOOD.
YOU SHOULD STILL BE ABLE TO USE
IT AND MOVE FORWARD.
BUT IF YOU SAY THE AMOUNT OF
VALIDATION NECESSARY WILL
REQUIRE MORE RIGOR YOU'LL HAVE
TO ESTABLISH THAT AND GO THROUGH
THE CREDIBILITY PROCESS AGAIN.
DOES THAT ADDRESS IT?
I HEARD THE BELL.
>> WE HAVE -- WELL, LUNCH IS
READY BUT --
>> WHILE WE HAVE JERRY ON THE
PHONE, ONE QUICK COMMENT.
>> I WANT TO MAKE ONE LAST
COMMENT.
IT'S ABOUT WHEN YOU DO
REEVALUATION AND THERE'S WORK
DONE IN THE PAST ISN'T READING
TOWARDS THE CREDIBILITY
ASSESSMENT.
BUT CREDIBILITY IS -- IT'S NOT A
GRAY AREA.
EVALUATION OF GAPS IS WHERE YOU
STAND RIGHT NOW IN TERMS OF THE
EVIDENCE AND RIGOR TO APPLY TO
YOUR MODEL WHAT IS NEEDED TO
INFORM THE DECISION TO THE BEST
LEVEL POSSIBLE.
NOT EVERYBODY WILL GET A GOLD
STANDARD EVALUATION.
SOMETIMES IT'S NOT POSSIBLE.
DO YOU STILL HAVE ENOUGH
INFORMATION THAT YOU CAN INFORM
THE DECISION MAYBE WITH OTHER
OBSERVATIONAL INFORMATION
LEVERAGED.
IT'S A CONTINUUM.
THINK OF IT LIKE THAT.
IT'S A PART OF YOUR PRACTICE.
IT'S PART OF YOUR WORK FLOW AND
HOW YOU MOVE THESE THINGS
FORWARD IN DRIVING THE GAPS DOWN
AND NOT TRYING TO GET TO THE
ULTIMATE RIGOR BECAUSE IT'S NOT
ALWAYS POSSIBLE AND NOT ALWAYS
NEEDED FOR IMPORTANT DECISIONS.
>> THANKS, JERRY.
BACK TO GRACE.
>> GREAT.
THANK YOU, EVERYONE.
LETS GIVE THEM A HAND AND JERRY
THANK YOU FOR STICKING OUT ON
ZOOM AND THANK YOU, FOR STEPPING
IN EVEN IF YOU DIDN'T WANT TO.
>> FEEL FREE TO COME UP AND ASK
ME QUESTIONS BECAUSE TO BE
HONEST THERE'S A LOT OF
QUESTIONS HERE I WANT TO ANSWER.
I REALLY WANT TO HELP.
PLEASE FEEL FREE.
>> CAN YOU ALSO POST YOUR ANSWER
ON THE WIKI SO IT'S SAVED AND
ARCHIVED.
>> DON'T TALK TO ME.
>> HE'LL BE BUSY WRITING.
>> TALKING IS GOOD AND I FEEL I
JUST HAD THE PARALLEL
CONVERSATION IN THE BRAIN
INITIATIVE MEETING ABOUT DATA
SCIENCE AND THE THRESHOLD YOU GO
TO CURATE THE DATA TO MAKE IT IN
THE RIGHT CURATION.
ANYWAY, LUNCH IS OUTSIDE.
GO FIND YOUR LAST NAME IT'S
ALPHABETICALLY ORIENTED FROM
LEFT TO RIGHT.
IF YOU ORDERED A BOX LUNCH WE
PUT A NAME ON IT WITH THE
SHARPIE AND THE TEAM FROM REAL
IMPACT ALPHABETIZED AND WE HAVE
NICE SESSIONS STARTING FOR YOU
AT 1:45.
THANK YOU, ENJOY YOUR LUNCH.
SO IF YOU ARE ON YOUR
COMPUTERS GO ON PLENARY IS
SESSION 1.3 IN REMEMBRANCE.
WITH HERBERT SAUO -- SAURO AND
MYSELF WITH HAVE PIERRE HUNTER
AND MITCHELL COLBANK.
SO WHEN WE WERE GOING THROUGH
THE 24 ORIGINAL GRANTEES OF THE
MULTISCALE MODELING CONSORTIUM
FUNDED IN 2005 WE DISCOVERED WE
LOST UNFORTUNATELY THREE OF THEM
DURING THE COURSE OF THE LAST 20
YEARS.
WE FIRST LOST MARCO CABRERA AND
HAD FOLKS WRITE THEIR THOUGHTS
ON MARCO'S LEGACY HERE.
YOU'RE WELCOME TO READ THAT
BECAUSE IT WAS ACTUALLY QUITE A
SHOCK WHEN IT HAPPENED.
THEN WE ALSO LOST DAVID KINE IN
2007 AND JIM BACENSCHWIETEN AND
WELL GIVE AN OPPORTUNITY TO HEAR
ABOUT THE FOLKS AND WHAT THEY
DID IN THE CONSORTIUM AND AFTER
COME LOOK AT THE POSTER HERE.
YOU CAN SEE THE SIGNATURES OF
ALL THREE OF THE FOLKS PRETTY
CLEARED ON THAT POSTER.
I'LL START WITH MY SLIDES AND
WE'LL DO EACH PERSON AND THEN
WE'LL PAUSE SO THE FIRST PERSON
SO MY SLIDES START WITH THIS
PICTURE.
THAT WAS TAKEN IN MONTREAL
DURING THE SECOND MULTISCALE
MODELING CONSORTIUM MEETING.
YOU CAN SEE ALL THREE OF THEM IN
THIS PICTURE.
SO MARCO IS HERE.
HE'S HIDING BEHIND BRIDGETTE
WILSON AND DAVID KAI IS HERE AND
JIM AND JIM BASINGTHWITGTE HERE
AND OUR ORIGINAL COHORT FROM THE
2008 MEETING IN MONTREAL.
ACTUALLY OUR THIRD MEETING AND
DENISE IS HERE.
THIS IS THE POSTER THAT'S UP
FRONT HERE.
THIS IS THE POSTER CREATED FOR
THAT MEETING.
MARCO'S WORK IS DOWN HERE,
ACTUALLY AND DAVID'S WORK IS
HERE AND THEIR SIGNATURES ARE
NEXT TO THE FIGURES THEY
CONTRIBUTE AND JIM'S IS HERE.
SO MARCO CABRERA WAS FUNDED BY
THE ONE GRANT SUPPORTED BY NASA,
ACTUALLY, RON WHITE SUPPORTED
THAT GRANT ON TIME COURSE OF
METABOLIC ADAPTATIONS DURING
LOADING AND UNLOADING FROM CASE
WESTERN RESERVE UNDERSTAND.
-- UNIVERSITY.
HE WAS INTO MUSCULOSKELETAL
MODELLING AND LOOKING AT THE
ORGAN LEVELS AND THE DIFFERENT
CHAINS OF EVENTS.
HE WAS PRETTY MUCH ONE OF THESE
PEOPLE VERY ENERGETIC AND WAS
VERY ATHLETIC I THINK HE DIED
AFTER RUNNING AND WAS RUNNING
AND HAD A STROKE IN HIS OFFICE.
HE WAS ONE OF THE FIRST WORKING
GROUP LEADS.
HE LED THE CARDIAC AND
PHYSIOLOGY WORKING GROUP 2.
THIS GIVES AN IDEA OF THE
WORKING GROUPS THAT WERE FORMED.
THE CONSORTIUM WAS FORMED UNDER
THE PREMISE THAT PEOPLE WANTED
TO HAVE WORKING GROUPS AND WRITE
PAPERS TOGETHER AND THESE WERE
THE FIRST WORKING GROUPS.
HE LED IT TO PHYSIOLOGY AND
THAT'S OF INTEREST TO NASA.
I DID COLLECT THIS RECOLLECTION
OF MARCO THROUGH AHMED'S SURVEY
HE DID WITH THE ORIGINAL
AWARDEES.
MARCO'S PASSING WAS MY SADDEST
DAY AND WAS SO YOUNG AND WAS
DRIVING METABOLIC COMPLEX
SYSTEMS AND CARDIAC MUSCLE
METABOLISM.
WE WERE WORK ON OUR FIRST MSM
PROJECT WHICH IS WHAT I WAS JUST
DESCRIBING TO YOU WHERE I WAS A
CO-I.
I'M THANKFUL FOR HIM INTRODUCING
ME TO THIS LOADING AND UNLOADING
MUSCLE ADAPTATION.
UNLOADING IS WHERE MUSCLE MASS
CAN BE REDUCED UNLESS IT IS
TRAINED.
WE WERE HAVING CHALLENGES TO
MODEL THE COMPLEX PHENOMENON
YESTERDAY WE MADE GREAT PROGRESS
UNTIL MARCO'S PASSING.
THAT IS WHAT WE HAVE FOR MARCO.
DOES ANYONE ELSE WANT TO SAY
ANYTHING ABOUT MARCO OR ABOUT
THE FIELD OF WORK?
YOU'RE WELCOME TO POST YOUR
THOUGHTS IN THE WIKI IN THE
COMMENT SECTION.
I HAVE THIS SLIDE.
GREGOR, I KNOW YOU WANTED TO
PRESENT.
I'LL GO QUICKLY THROUGH.
THIS IS THE SLIDE DAVID
PRESENTED DURING THE FIRST
MULTI-SCALE MODELLING MEETING IN
2006.
THE PRIMARY VISUAL CORTEX WITH A
MILLION NEURONS AND
PHYSIOLOGICALLY CONSTRAINED.
I'LL LET GREGOR EXPLAIN THE
SCIENCE OF THE NEURODYNAMICS TO
AN EMBEDDED NETWORK APPROACH.
THREES INTERESTING PICTURES FROM
DIFFERENT RETINAL CORTICOMAPS TO
LSD.
I'LL LET GREGOR SAY A FEW WORDS.
>> HE WAS BORN IN WENZHOU CHINA
AND PASSED IN NEW YORK AND
PASSED ON OCTOBER 21, 2017.
HE HAD CANCER.
AN INTERESTING STORY IS THAT HIS
FAMILY COMES FROM A PLACE CALLED
CAI AND HAD LIVED THERE FOR A
THOUSAND YEARS.
HE GOT HIS BACHELOR'S IN 1984 IN
PHYSICS AND THEN LIKE ALL THE
CRAZIES, HE WENT TO NORTHWESTERN
TO TRY TO DO PARTICLE PHYSICS.
SAW THE LIGHT PRETTY FAST AND
STARTED DOING I THINK CONDENSED
MATTER PHYSICS.
HE ACTUALLY STUDIED IN LOS
ALAMOS.
HE WAS A STUDENT THERE AND
POSTDOC AND THEN A POSTDOC AT
NYU.
THEN HE HAD FACULTY APPOINTMENTS
FIRST AT THE UNIVERSITY OF NORTH
CAROLINA CHAPEL HILL FOR ONE
YEAR AND THEN THEY TOOK HIM BACK
TO NYU AND HE WENT TO SHANGHAI
ZHENTON UNIVERSITY AND THERE HE
FOUNDED THIS INSTITUTE FOR
NATURAL SCIENCES.
AND HE HIRED THE GOOD PEOPLE
THERE.
IT'S STILL A VERY GOOD
INSTITUTE.
AT THE END THERE'S A PICTURE.
SO THE FIRST THING HE DID WAS OF
NOTICE IS MATHEMATICAL FOR
METAPHYSIC AND FOUND AN EQUATION
THAT IS COMPLETELY INTEGRATABLE.
FOR A MATHEMATICIAN IT'S A BIG
DEAL.
IT'S AN EQUATION YOU CAN
EXACTLY.
HE DID THIS IN 1994.
I THINK BASICALLY FOR HIS
THESIS.
THERE WAS A MODEL THAT WHICH IS
EASIER TO DESCRIBE THAN ACTUAL
TURBULENCE IN WATER BUT STILL
NOT COMPLETELY TRIVIAL.
AND THERE'S THE EQUATION.
IF THOSE STRANGE DERIVATIVES
TELL YOU SOMETHING IT'S OKAY AND
IF NOT IT'S JUST A STRANGE
LOOKING EQUATION.
IN ANY CASE, THIS EQUATION WAS
SUPPOSED TO BE A MODEL OF
SOMETHING CALLED WAVE TURBULENCE
AND SO THERE WAS ANOTHER KINETIC
EQUATION DERIVED FROM THIS WHOSE
EQUILIBRIUM SOLUTIONS WOULD BE
STRAIGHT LINES OF WAVE NUMBER
VERSUS ENERGY CONTENT.
THERE WOULD BE STRAIGHT LINE AND
CON FURM THE THEORY OF WAVE
TURBULENCE.
AND THEY DIDN'T FIND THE
STRAIGHT LINES.
THEY FOUND SOMETHING ELSE.
THERE'S STILL A CONTROVERSY OF
WHAT THAT IS BUT DAVID COMPUTED
WELL ENOUGH, LONG ENOUGH TO
ACTUALLY FIND FIND THE SOLUTIONS
THEY WERE FIRST LOOKING FOR.
AND HE GOT A LOT OF RECOGNITION
FOR IT.
THAT'S EXACTLY THE SAME AS WHAT
GRACE WAS SHOWING.
THERE'S THE NEUROSCIENCE.
HE DID A TON OF THINGS IN
NEUROSCIENCE.
LIKE ONE OF MY STUDENTS AND HE
AND I TRIED TO ANSWER THE
QUESTION WHETHER OUR VISION IS
COMPRESSED OR OPERATES LIKE
COMPRESSED SENSING AND YES DOES.
THIS IS PROBABLY THE PINNACLE OF
HIS SCIENTIFIC CAREER.
HE DESIGN THE COMPUTATION.
THE GUY WHO IMPLEMENTED IT WAS
ADI RANGAN.
THE IDEA IS IF YOU SHOW AN
EXCLAMATION POINT IN PIECES.
FIRST THE POINT AND THEN THE
EXCLAMATION IF YOUR SHOWN THAT
ON AN APPROPRIATE TIME SCALE
YOU'LL SEE THE DOT RISES OUT OF
THE POINT.
HERE'S THE COMPUTATION ON THE
RIGHT THAT SHOWS THAT'S THE
CASE.
SO YOU CAN SEE THE FIRST COLUMN,
FORGET ABOUT THE OTHER COLUMNS.
YOU SEE THE FIRST COLUMN AFTER
SO COLUMN I.
YOU SEE THERE'S A DOT AND THEN
THE SUB THRESHOLD VOLTAGE RISES.
THEN YOU SHOW THE BAR AND THIS
BATCH OF RISING THRESHOLD
VOLTAGE STARTS SPREADING.
SO IT IS LIKE THE BAR IS GROWING
OUT OF THIS INITIAL BATCH WHERE
YOU SAW THE DOT.
HE DID A LOT OF THIS IN CHINA
WITH A POSTDOC ON BLOOD FLOW.
HOW THE BLOOD FLOWS IN VEINS AND
CAPILLARIES AND ARTERIES AND HOW
THIS NETWORK SPREAD AND PRUNES
ITSELF.
HE EVEN HAD -- THEY HAD A
COMPUTATION OF AN AFFECT THAT IS
USED IN TRADITIONAL CHINESE
MEDICINE.
SOME SORT OF A SPECIAL
PULSATION.
IT WAS VERY INTERESTING STUFF.
AND THEN I DON'T KNOW IF THIS IS
THE LAST ONE.
HE ALSO DID SOMETHING ON
INTERNAL WAVES ON THE BOTTOM.
THERE'S A LONG WAVE.
I DON'T KNOW HOW MUCH.
SEVERAL HUNDRED METERS BELOW THE
SURFACE.
THE QUESTION IS WHAT'S THE
SIGNATURE OF SUCH A WAVE?
THE BOTTOM WAVE IS SOME SORT OF
SUBMARINE.
YOU CAN SEE ON TOP WHAT THE
SIGNATURE IS.
SO THERE WAS A PAPER TO THAT
AFFECT.
BUILDING ON THE TOP TWO FLOORS
WAS DAVID'S INSTITUTE IN
SHANGHAI IN 2010.
NOW, JUST TO TELL YOU HOW GOOD A
MENTOR HE WAS, THERE'S THE LAST
SLIDE.
THIS IS THE GROUP FROM THE
CONFERENCE IN ATHENS, GEORGIA.
THERE'S DAVID.
THREE OR FOUR PEOPLE IN THIS
GROUP WERE MENTORED BY DAVID OR
COLLABORATED WITH DAVID.
ONE OF MY LAST MEMORIES OF HIM
WAS WE WENT I THINK AROUND
CHINESE NEW ERIN 2017.
WE WENT TO ONE OF THOSE ALMOST
LIKE HOLE IN THE WALL PLACES.
HE ORDERED THIS BIG THING FULL
OF NOODLES AND GOODIES IN THERE
FROM BOK CHOI AND CHICKEN AND
SHRIMP AND HAPPY FAMILY AND WE
WERE ONE UNFORTUNATELY FOR THE
LAST TIME.
>> THANK YOU, GREGOR I'LL SHOW
THE LAST ONE FOR JIM BASINGTHWAI
BASSINGTHWAIGHTE I WAS READING
HIS AND THINKING HE WAS THINKING
OF DIGITAL TWINS IN 2006.
HE WAS TALKING ABOUT MODELLING
ON THE FLY, REAL TIME AND ALL
THE OTHER DISCUSSIONS HE LAD AND
TALKED ABOUT MODULAR MODELLING
AND AUTOMATING MODULAR PROCESSES
FOR MODELLING.
WE HAD A MEMORIAM FOR HIM ON THE
WIKI BECAUSE THE YEAR PRIOR TO
HIS DEATH HE ASKED FOR NIBIB TO
SUPPORT OVER 400 MODELS CREATED
BY THE PHYSIUM PROJECT AND
THERE'S A LINK ON THE WIKI AND
THEY TRANSFERRED THEM OVER AND
YOU CAN SEE THE PHYSIOTHERE AND
DURING HIS MEMORIAL WE HAD
PEOPLE CONTRIBUTE MESSAGES AND
THERE'S SEVERAL THERE.
AND THEY COLLABORATE WITH JIM
UNTIL HIS PASSING IN 2022 AND HE
COLLABORATED WITH HIM ON THE
ANALYZING CARDIAC POWER GRID
PROJECT FOR ADAPTIVE RESPONSES
TO FLUCTUATING LOCAL DEMAND
SINCE 2015 AS WELL AS SEVERAL
OTHER PROJECTS BEFORE THAT
INCLUDING THE VIRTUAL PHYSIO
PROJECT AND WAS TERRIFIC AND MY
TRAINING WITH HIM IS BEHIND MY
SUCCESS AND TAUGHT ME ABOUT BIO
COMPUTATIONAL ENGINEERING AND
WAS A PIONEER IN OUR FIELD AND
I'M INDEBTED TO HIM FOR THE
GROWTH OF OUR FIELD.
THAT'S ALL I HAVE.
>> I WORKED WITH JIM FOR ALMOST
15 YEARS NOW.
I KNOW HIM A LITTLE BIT.
OTHER PEOPLE KNOW HIM BETTER
THAN I DO.
WE USED TO MEET IN HIS OFFICE A
LOT AND ARGUE A LOT ABOUT
STANDARDS.
I REMEMBER TALKING ABOUT THE
REAL TIME STUFF ACTUALLY.
I WANT TO SHOW FIRST A VIDEO
THAT WAS MADE.
THEY REALIZED THE REAL TIME
SIMULATION THAT JIM WANTED TO
DO.
>> JIM WAS PART OF THE WHOLE
PROCESS.
EVEN WITH HIS PAINS HE'D BE
LYING IN THE LAZY BOY CHAIR AND
WOULD GET VERIFICATION TO GET
ANDREW AND RON JOHN DO
UNDERSTAND IF THE WIKI WAS
INTELLIG
INTELLIGIBLE.
HE VALIDATED IT.
>> I THOUGHT I'D GIVE YOU A FEW
SLIDES.
FIRST, HIS ROLE IN THE PROJECT.
HE COINED THE TERM AND THE IDEA
INITIALLY AND PART OF THE UNION
OF PHYSIOLOGICAL SCIENCES
COUNCIL IN 1993.
HE CHOSE THAT ON THE BASED IT
COMES FROM PHYSIOLIFE AS A
WHOLE.
HE PROVIDED A DESCRIPTION OF
PHYSIO DYNAMIC AND BEHAVE OF THE
ORGANISM.
HE WAS THE FIRST CHAIR OF THE
COMMISSION ON BIO ENGINEERING
AND PHYSIOLOGY AND WITH OTHERS
AND I ATTENDED AFTER THE 33
CONFERENCE IN 1997.
THAT WAS THE FIRST TIME I REALLY
GOT TO KNOW JIM AND STARTED
WORKING WITH HIM.
AT THAT MEETING WE CREATED A
COMMISSION WHICH I WAS IN I THEN
CHAIRED AFTER THAT MEETING.
AND HE CAME DOWN TO NEW ZEALAND
A NUMBER OF TIMES.
I CAME DOWN WITH SLIDES.
ONE WAS WITH JOAN IN QUEENSTOWN
IN 2001 WITH A PHYSIO MEETING
AND HERE WITH ANDREW McCULLOUGH
AND THIS IS A BOAT THAT SAILS
DOWN QUEENS TOWN AND I THOUGHT
I'D MENTION A COUPLE OF HIS
PUBLICATIONS I THINK WERE
INSTRUMENTAL IN KICKING OFF
PHYSIO RELATED TO CARDIAC WORK.
THIS IS FROM A WORKSHOP FROM
UCSD IN 1997 AND THE OTHER IS
HON THE CARDIAC PHYSIOME IN
2008.
WE WILL MISS HIM ENORMOUSLY AND
WAS A SUPERB HUMAN BEING.
A LOVELY MAN.
THANK YOU.
>> HE WAS A PROFESSOR OF BIO
ENGINEERING AT UNIVERSITY OF
WASHINGTON.
I DOUG INTO HIS PAST.
HE WORKED HIS WAY INTO ACADEMIA.
HE WAS BORN IN TORONTO, CANADA
BUT THEY MOVED TO THE LAKE SHORE
IN KIP DER GARDEN AND HIS DAD
WAS AN ENGINEER AND WORKED IN
THE NAVY ON RADAR.
HE WENT TO UNIVERSITY OF
TORONTO.
HE HAD THIS IDEA HE'D BE A
MEDICAL DOCTOR BUT DID HIS
DEGREE IN GENERAL SCIENCE BUT
SPECIALIZED IN PHYSIOLOGY AND
CHEM AND EMPHASIS IN MATHEMATICS
WHICH DIDN'T BOTHER HIM.
HIS DAD WAS AN ENGINEER.
HE SPENT A COUPLE YEARS IN THE
AIR FORCE AND BECAUSE HE WAS
BROKE HE MET OTHERS AND WAS
BROKE AND GOT A JOB IN TRONT.
-- TORONTO.
HE SAID YOU WOULDN'T WANT TO GO
THERE.
THE TEMPERATURE WAS MINUS 60
HALF THE YEAR AND WAS A SMALL
RURAL PLACES.
HE DIDN'T SURVIVE TOO LONG THEN
MOVED TO DO A POST GRADUATE
DEGREE AT THE MEDICAL SCHOOL IN
LONDON.
HE CAME BACK TO THE U.S. AND GOT
A JOB AT THE MAYO CLINIC AND HE
APPLIED WITH A LOT JOBS AND
FINALLY MAYO GAVE HIM THE
OPPORTUNITY TO WORK THERE.
HE DID HIS Ph.D. WITH THE PERSON
FAMOUS FOR INVENTING THE G SUIT
THAT KEEPS ASTRONAUTS AND
HIGH-ALTITUDE PILOTS WORKING
PROPERLY HE GOT HIS Ph.D. THERE.
ME MOVED THEN IN THE MIDST 70s
TO UNIVERSITY OF WASHINGTON.
THEY STARTED A CENTER AND HE
DISCOVERED THE FACULTY THERE
WERE ALL IN SOFT MONEY AND HE
PROPOSED MAYBE YOU DO SOME
TEACHING AND THEY WERE OPPOSED
TO THAT.
I STILL FEEL THAT IN THE
DEPARTMENT NOW THAT WAS A GOOD
EXPERIENCE FOR RESEARCHERS AND I
WOULD TEND TO AGREE.
HE SET UP A NUMBER OF COURSES
AND GOT THE EDUCATIONAL SYSTEM
AND HAVE 30 Ph.D. GRADUATES AND
A BUNCH OF MASTER PROGRAMS.
WE DID WAS ALWAYS DOING
MATHEMATIC MODELLING IN THE '60s
BECAUSE OF HIS BACKGROUND AND
DID MORE IN THE '80s.
I'M SURE A NUMBER WERE DRAWN TO
CHAOS IN THE 1990s AND HE WAS
TOO.
AND HE HAD A GOOD RESEARCHER
FRIEND AND DID A Ph.D. IN 1987
AND WROTE A BOOK ON FRACTO
PHYSIOLOGY AND HE STARTED
PURSUING A PHYSIO MORE IN THE
2000s.
THAT'S WHEN THEY RELEASED HIS
SIMULATION PROGRAM.
THAT WAS A PRODUCT OF EARLIER
ONES TURNED OUT SO -- IN THE
2010s I EMBRACED HIM TO GET ALL
500 MODELS, 400 OF THEM EVEN IF
YOU COUNT THE ADJACENT WORK THE
MODELS ARE STILL THERE YOU CAN
LOAD TO ANY MODERN TOOL TODAY.
SUMMARIES OF THE SOME OF THE
WORK HE DID.
SAME WITH PETER THEY WERE MAYBE
TOO EARLY ON THE PHYSIO SEEING
THE REBIRTH IN THE FORM OF THE
DIGITAL TWINS SLIGHTLY MODIFIED,
MIND YOU, BUT STILL SIMILAR.
AND TRYING TO REPLACE SBML AND
MORE DETAILS AND THAT MATERIAL
IS ON THE WEBSITE.
HE WAS VERY PASSIONATE ABOUT HIS
WORK AND WE WORKED RIGHT UP TO
THE END.
IN FACT HE WAS PRETTY HEALTHY UP
TO WHEN HE WAS '91 AND LAST YEAR
HIS HEALTH STARTED TO DECLINE
AND THAT DIDN'T STOP HIM OF
COURSE.
ACCEPT UP METABOLIC MODELS WHEN
MISSING THE OTHER MODELS.
THAT'S BEEN A PROBLEM FOR MANY
DECADES.
HE PROVIDED A VERY NICE SOLUTION
BY ADDING TERMS TO THE
EQUATIONS.
THERE'S IN DEPTH ORAL HISTORIES
ON THE SITE AND FINALLY AND
THERE'S PLACE TO SIT.
IT WAS CROWNED FULL OF BOOKS.
HE HAD MORE AT HOME AND HAD THE
LARGEST COLLECTION OF FRACTAL
BOOKS I'VE EVER SEEN AND I HAVE
A BIG LIBRARY SO I CAN DO THE
COMPARISON.
HIS FAVORITE PLACE WAS SITTING
IN THE DESK RUNNING SIMULATIONS.
YOU CAN SEE SOME THERE.
THERE'S A MINE OF INFORMATION
WHEN IT CAME TO MODELLING AND
CLOSE TO FINISHING A TEXTBOOK ON
MODELLING WHICH MIGHT YET BE
PUBLISHED.
I TRIED TO PERSUADE HIM TO
PUBLISHING THE TEXTBOOK ALL THE
TIME BECAUSE WHEN SOMEBODY
PASSES ON ALL THEIR EXPERIENCE
DISAPPEARS WITH THEM AND IT'S --
HE HAD A TREMENDOUS AMOUNT OF
EXPERIENCE.
ONE ANECDOTE FROM HIS CHILDHOOD.
THEY HAD THEIR OWN RADIOS IN
AND COMMUNICATED WITH HIS
FRIENDS ACROSS THE STREET AND
GREATLY MISSED BY ALL OF US.
>> I'M HONORED TO BE UP HERE
TALK.
I DIDN'T GET TO MEET JIM BUT I
FEEL LIKE I KNOW JIM BECAUSE
I'VE GOTTEN THE CHANCE TO WORK
WITH MANY PEOPLE HERE AS FAR AS
THE IMAG MSM AND HAD DAN BEARD
AS A MENTOR AND BASED ON THE
STORIES I HEARD I FEEL LIKE I
KNOW JIM BECAUSE HE KIND OF
INSTILLED PRACTICES IN HIS
MENTEES NOW CONTINUING TO BE
MENTORS TO US.
THE IDEAS THAT WERE PUSHED
FORWARD AND WHERE JIM WAS A
FOCUS ON AND STILL CONTINUING
AND THINKING WITH THE CARDIAC
PHY
PHYSIOME IN IRVINE WAS HIS WORK
IN THE MODELLING COMMUNITY.
IN COLLABORATION WITH OTHER
CO-ORGANIZERS AND GETTING INPUT
FROM DAN BEARD WE SAID WE NEED
TO START BUILDING INCLUSIVE
COMPUTATIONAL MODELS AND LOOK AT
SEX DIFFERENCES AND HEALTH
DISPARITIES AND NEED TO PUT THIS
AT THE FOREFRONT AND THE TURNOUT
FROM OUR CONFERENCE WAS THIS IS
SOMETHING THAT NEEDS TO BE
TALKED ABOUT AND WE'RE TRYING TO
BE AHEAD OF THE CURVE.
I THINK THAT'S WHAT JIM WOULD
WANT AS WELL AND UNDERSTANDING
HEALTH DISPARITIES AND ALSO
GOING BACK TO MULTI-SCALE
MODELLING AND THINGS PUSHED
THROUGH THE IMAGO AND JIM'S WORK
AND FOCUSSED ON DIGITAL TWINS
AND GOT TO SEE PEOPLE TALKING
ABOUT THE DIGITAL TWINS AND THE
MOVE TOWARDS THIS IN THE
CARDIOVASCULAR FIELD AND THE USE
OF MECHANISTIC MODELLING.
IT REALLY HIGHLIGHTS THE
EXCELLENCE JIM PUT AS WELL AS
PETER INTO THE PHYSIOME.
WE HOSTED IT AT EDWARDS IN
IRVINE AND ONE OF THE BIGGEST
HEART VALVE PRODUCERS IN THE
WORLD AND IT HIGHLIGHT THE
IMPORTANCE OF THIS GROUP AND OF
PEOPLE LIKE JIM PUSHING FORWARD
THIS COMMUNITY TO USE
MECHANISTIC MODELLING TO BETTER
HEALTH CARE.
AND THIS WAS IN IRVINE AND THIS
WAS FANTASTIC AFTER COVID TO SEE
PEOPLE COME BACK AND BE A PART
OF THE COMMUNITY.
IT'S FANTASTIC TO HIGHLIGHT THE
WORK JIM DID AND TO SEE WHERE
IT'S PROGRESSED INTO THE CARDIAC
PHY
PHYSIOME.
THIS IS OUR GROUP IN IRVINE.
IN CONTINUING THE IDEA OF THE
CARDIAC PHYSIOME AND MECHANISTIC
MODELLING AND TWINS NOW IT'S
STARTING TO INTEGRATE ACROSS THE
WORLD AND SO THE NEXT CARDIAC
PHYSIOME WILL TAKE PLACE THE
THIRD OF THREE, THE FIRST
PHYSIOLOGICAL HUMAN WILL TAKE
PLACE IN GERMANY AND COMPUTER IN
CARDIOLOGY AND PHYSIOME IS THE
LAST AND IT'S A NICE TRAIN RIDE
THROUGH GERMANY FOR ALL THREE.
MY MENTOR AT UCI DOES NOT DO
MECHANISTIC MODELLING BUT DOES
ALL OF IT FROM A LOT OF THE WORK
JIM DID AND OUR OTHER TEAM.
I'D LIKE TO END HERE THAT DAN
WASN'T ABLE TO ATTEND BUT GAVE A
REALLY GREAT PRESENTATION ON ALL
JIM'S WORK AND SORT OF HIS
IMPACT ON OUR COMMUNITY IN THE
SAME PICTURE AND WHAT WE HEARD
JIM'S FAVORITE PLACE WAS BEHIND
HIS DESK DOING MODELLING AND
SIMULATION AND ALL HIS MENTEES
ARE IN STILLING US IN US AS
EARLY-CAREER INVESTIGATORS.
I'M SAD TO HAVE NEVER MET HIM
BUT I FEEL I KNOW HIM BECAUSE OF
THIS COMMUNITY.
>> DOES ANYONE HAVE COMMENTS TO
MAKE?
>> IT'S A NICE WAY TO END.
THERE WAS A DEPRESSING
MULTISCALE MODELING CONVERSATION
HOW NOBODY'S SHARING MODELS AND
NOBODY'S ADOPTING STANDARDS AND
JIM WOULD SAY OUR ONLY HOPE IN
THE NEXT GENERATION.
>> ALL RIGHT, SESSION'S CLOSED.
>> THANK YOU FOR THE WONDERFUL
SESSION AND NICE REMEMBRANCE.
IT'S A PERFECT SEGUE FOR THE
REST OF THE MEETING.
WE'LL BE TALKING ABOUT WHAT'S
HAPPENING IN THE PRESENT WITH
THE NEXT SESSION AND TOMORROW
WE'LL BE GOING IN THE FUTURE.
I'M REED SHABMAN AT THE NATIONAL
INSTITUTE OF ALLERGY AND
INFECTIOUS DISEASES AND BECAME
INTERESTED IN IMAG IN THE MIDDLE
OF COVID AND GOT TO KNOW FOLKS
THROUGH THAT WORKING GROUP.
IT'S A GREAT PRIVILEGE TO START
TO INTRODUCE OUR FIRST SET OF
SPEAKERS FOR THIS TIME AND START
WITH DR. GARY AND I ALWAYS ENJOY
LISTENING TO HIM TALK AND
LOOKING FORWARD TO WHAT HE HAS
TO SAY TODAY.
I'M GET HIS SLIDES UP.
THIS IS ABOUT HARD PROBLEMS AND
I'M A CLINICIAN AND WANT TO BE
ABLE TO TAKE CARE OF PATIENTS
AND TREAT THEM AND CAME UP WITH
AXIOMS OF PRECISION MEDICINE AND
NUMBER 4 IS THE PROBLEM THAT'S
THE VALLEY OF DEATH WE TAKE THE
INFORMATION INTO EFFECTIVE
THERAPEUTICS AND THIS MOST THE
FOCUS IS ON THIS STEP WHICH
DOESN'T ADDRESS THE ACTUAL
PROBLEM BECAUSE THE FACT OF THE
MATTER EVERY FAILED CLINICAL
TRIAL USED A DRUG THAT EVERYONE
THOUGHT WAS A GOOD IDEA AND
PASSED EVERY METRIC OF SUCCESS
UP TO THAT PARTICULAR POINT.
SO THIS IS A PROBLEM OF
ABROGATED CELLULAR BEHAVIOR
TRANSLATING ACROSS THIS CONCEPT
FROM BIO PLAUSIBILITY TO THE
HETEROGENEITY AND MESSINESS IN
CLINICAL TRIALS AND APPLICABLE
TO NEW DRUGS, COMBINATION OF
DRUGS, ANYTHING WHERE YOU TAKE A
BIOLOGICAL ENTITY AND PUT IT
INTO A CONTEXT FOR WHICH IT'S
NEVER BEEN USED BEFORE AND THIS
SAY SUMMARY HOW EVERYTHING'S THE
SAME WHICH GETS TO THE GRANT
THAT I GOT AT THE END OF THIS
FOR WHICH I'M A LITTLE STRESSED
BECAUSE I'M TRYING TO APPLY FOR
THE LAST CARRY FORWARD REQUEST
FOR RESIDUAL MONEY BUT WHEN WE
WERE DOING THIS, WE IDENTIFIED
EIGHT OF THESE SORTS OF THINGS
THAT ARE BYPRODUCTS OF THE
TERMINAL LOGICAL WARS GO TO
STOCHASTICITY AND THE LIMITATION
WE HAVE IN ADDRESSING THE ISSUES
AND THE GRANT USED MODELS AS YOU
SURROGATE TO HOW WE GO ABOUT
DRESSING THE ISSUES WITH
MULTI-CELLULAR BIOLOGICAL
SYMPTOMS.
THESE ARE WHAT NEEDS SOLVE FOR
ME TAKE CARE OF PATIENTS BETTER.
WHAT'S THE EFFECT OF MOLECULAR
INTERVENTION ACROSS BIOLOGICAL
HETEROGENEITY AND WE'RE DEAL
WITH THE STEP OF INCOMPLETENESS
AND WANT TO KNOW WHEN WE MAKE A
HYPOTHESIS OF A PARTICULAR
EFFECT AND COMPUTATIONAL
REPRESENTATION OF THAT AFFECT,
WHAT'S THE CONTRIBUTION OF
UNKNOWN OR UNREPRESENTED
COMPONENT TO AN INTERVENTION.
I WOULD MAKE THE HERETICAL
STATEMENT AND MAKE CRITERIA FOR
WHAT YOUR MODEL IS A GOOD MODEL.
IT HAS TO INCORPORATE
UNIDENTIFIABLITY AND A LOT OF IS
NO HELP BECAUSE OF THE THEOREM
PART OF CAUSAL REASONING THAT
SAYS YOU CANNOT IDENTIFY TRANS
LEVEL CAUSE AND EFFECT USING
DATA CENTRIC MEASURES ON THE
DATA SET.
MY ASSERTION IS THEY CANNOT
AVOID UNIDENTIFIABILITY.
THIS IS A PROBLEM BECAUSE IF YOU
CHOOSE TO MAKE A PARTICULAR
MODEL YOU'RE MAKING A SECTION OF
CHOICES ABOUT WHAT YOU'RE GOING
TO REPRESENT AND WHAT YOU'RE
GOING TO ABSTRACT AWAY AND DOING
SO IN AND OF ITSELF FORMS A BIAS
WITH THE RESPECT OF THAT
PARTICULAR MODEL TO REPRESENT
THE POPULATION.
AVOIDING BIASES CAN BE USED WITH
THIS PARTICULAR PRINCIPLE CALLED
THE MAXIMAL PRINCIPLE DRAWN FROM
INFORMATION THEORY.
YOU CAN READ THE SLIDE AS WELL
AS I CAN.
THE POINT IS YOU WANT TO AVOID
BIAS AND MITIGATE FAILURE FOR
ANY CONTROL DISCOVERY BY
ACCOUNTING FOR ALL THE
UNPOTENTIAL REPRESENTED
INTERACTIONS OR UNREPRESENTED
STUFF IN YOUR MODEL THAT IS
THERE AND THAT'S OUR GUIDING
PRINCIPLE.
MY COLLEAGUE CAME UP WITH THE
AND WE'RE COMING UP WITH
PARAMETERS AND GETS US KILLED IN
THE REVIEW BECAUSE WE DEFINE
WHAT THE PARAMETERS MEAN.
WHEN WE TALK ABOUT PARAMETERS OR
STRENGTHS AND DEGREES OF
CONNECTIVITIES BETWEEN ALL THE
THINGS YOU CHOOSE TO REPRESENT
IN YOUR MODEL BUT NOT EXPLICITLY
REPRESENTED IN THE MODEL AND
THIS SAY MATHEMATICAL MATRIX WE
OPERATE OVER AND ALL THE
POSSIBLE CONFIGURATIONS THAT
CANNOT BE FALSIFIED BY DATA.
THAT'S AN IMPORTANT DISTINCTION
BECAUSE WARE NO LONGER FITTING.
WE'RE IDENTIFYING VASTS SWATHS
OF ENSEMBLES OF MODELS THAT
CANNOT BE FALSIFIED BY WHAT YOU
HAVE.
THERE'S A PAPER ABOUT THAT BUT
NO ONE EVER READS IT.
AND THERE'S OF COURSE SINCE
WE'RE OPERATING OVER MILLIONS OF
SIMULATIONS, THIS REQUIRES
MACHINE LEARNING AND ARTIFICIAL
INTELLIGENCE OF WHICH WE EMPLOY
A GREAT DEAL.
CHASE CAME UP WITH THIS
PARTICULAR THING AND HERE'S THE
PAPER THAT COMES UP WITH THAT.
WE USED THE RESPONSE SYSTEM.
THIS IS A WIRE FRAME DIAGRAM FOR
THAT.
AND HERE'S AN EXAMPLE AND WHAT
HAPPENS WHEN YOU OPERATE OVER AN
MRM.
ON THE LEFT SIDE THE BASE MRM OF
THE IBM IS A MATRIX WHEN THE
ENTITIES AND RULES AND THE MODEL
AND THE COLOR REPRESENT THE
STRENGTH OF THOSE INTERACTIONS.
YOU CAN SEE WE MAKE ASSUMPTIONS
ABOUT WHAT WE ARE GOING TO
REPRESENT.
AND WE INTUITIVELY KNOW THIS BUT
NEVER HAVE A WAY OF REPRESENTING
THAT.
IT'S NOT JUST ONE PARTICULAR
EXAMPLE.
THERE'S AN ENSEMBLE OF THESE
THINGS WHICH ARE SHOWN IN THE
HEAT MAP ON THE RIGHT SIDE WHICH
REPRESENTS THE BOUNDARY
CONDITIONS FOR THE
NON-FALSIFIABLE PARAMETER
CONFIGURATIONS WITHIN THE
MATRIX.
IF WE OPERATE WITHIN THAT
PARTICULAR SPACE WE BELIEVE WE
ARE ENCOMPASSING WHAT IS
POSSIBLE BIO LOGICALLY, WHICH IS
WHAT IS IMPORTANT.
WE CAN'T HAVE A TALK WITHOUT
TALKING ABOUT A.I. I USUALLY
SLAM IT BUT I'LL TALK ABOUT HOW
TO CREATE THE SYNTHETIC DATA
EVERY A.I. REQUIRES.
THIS IS A PAPER WHY WE CAN'T
USING MANY OF THE METHODS PEOPLE
USE TO CREATE SYNTHETIC DATA
FROM OTHER DOMAINS.
THESE PARTICULAR THINGS ARE
PRESENT IN MOLECULAR TIME SERIES
DATA BECAUSE IT'S CIRCULAR TO
TRY TO USE MACHINE LEARNING TO
IDENTIFY THE VARIANT AND CREATE
THE SYNTHETIC DATA BASED ON THAT
PARTICULAR FINDING.
MY SUPPOSITION IS YOU HAVE TO
SIMULATE IT IN ORDER TO FLUSH
OUT ALL THE POTENTIAL C
CONFIGURATION AND FIND A
GENERATIVE MODEL IF YOU DON'T DO
THAT PARTICULAR PROCESS.
THE PAPER IS IN PROCESS.
HERE'S AN EXAMPLE OF THIS BASED
ON A DATA SET OF TRAUMA PATIENTS
FROM THE MILITARY T DAT
PROTOCOL.
THE REAL DATA ARE THE DATA
POINTS THAT ARE THERE AND ANYONE
WHO DEALS WITH CLINICAL DATA --
THEY -- ANYONE WHO RECOGNIZES
THE DATA POINTS ARE ALL OVER THE
PLACE.
YOU CAN DRAW A REGRESSION LINE
THAT SEPARATES THE TWO BUT IT'S
MEANINGLESS BECAUSE YOU LOSE A
BUNCH OF EXAMPLES.
WE SAID IF THAT DATA POINT
EXISTS THE BIOLOGICAL SYSTEM CAN
GENERATE IT AND WE OPERATE OVER
MRMs ABLE TO ENCOMPASS TE DATA
AND FLUSH OUT ALL THE POSSIBLE
CONFIGURATION CAN CONTRIBUTE TO
THE DATA.
IT'S TO HAVE THE MASSIVE SET OF
TRAJECTORIES POSSIBLE.
THE HARD THING IS CONTROLLED
DISCOVERIES.
YOU WANT A RATIONAL APPROACH AND
IDENTIFY THE FEATURES OF THIS
SYSTEM SO YOU DON'T GO DOWN THE
RABBIT HOLE OF CONSTANTLY ADDING
MORE DETAIL.
WHAT I TALKED ABOUT TO THE MEP
IS NOT APPLICABLE TO THE
DISCOVERY.
HERE YOU TRY TO BREAKTHROUGH ALL
THAT UNCERTAINTY AND FIND THE
CONTROL THAT IS ROBUST THAT CAN
EFFECT, EFFECTIVE CONTROL
DESPITE ALL THE SORTS OF THINGS
YOU HAVEN'T CHARACTERIZED AND
THE THINGS YOU DON'T KNOW AND
HAVEN'T INCLUDED IN YOUR
PARTICULAR MODEL.
SO WE USE DEEPER ENFORCEMENT
LEARNING PART OF THE GRANT IN
COLLABORATION WHERE WE TREAT THE
CONTROL PROBLEM AS A GAME PLAYED
AGAINST A SIMULATION FOR
IDENTIFYING ADAPTIVE
MULTIMODELAL CONTROL POLICIES
FOR BIOLOGICAL PROBLEMS NAMELY
SEPSIS AND THIS IS A PAPER AND A
RECENT PAPER IN THE COVID ERA
HOW WE CAN MANIPULATE CYTOKINE
STORM IN AN EFFECTIVE POPULATION
WHERE WE DON'T HAVE ANTIBIOTICS
USING DRUGS WE ALREADY HAVE.
THIS IS WHAT THE CONTROL ACTIONS
OVERLOOK FOCUSSING ON THE RIGHT
SIDE THAT SHOW VARIABLE PATIENT
TRAJECTORIES.
INDIVIDUALIZED TRAJECTORIES AND
I SEE THIS I WILL DO X IS
EFFECTIVE ACROSS THAT ENTIRE
SPACE.
MEDICAL DIGITAL TERMS I HATE THE
TERM BUT IT IS NOW DIRECTLY
RELATED TO THE CONCEPT OF
PRECISION, PERSONALIZED
MEDICINE.
THE CONCEPT OF A TWIN MEANS YOU
CAN NO LONGER TALK ABOUT
POPULATIONS.
I NEED TO HAVE A PARTICULAR WAY
TO REPRESENT A PARTICULAR
INDIVIDUAL IN THE PARTICULAR WAY
TO BE ABLE TO AFFECT TREATMENT
FOR THAT SPECIFIC PERSON.
IT'S HOW YOU USE MECHANISTIC
MODELS FOR DRUG DEVELOPMENT
DIGITAL TWINS.
THIS IS AN ASPIRATIONAL FIRST
APPROXIMATION WHAT THAT MAY LOOK
LIKE AND YOU TRAIN AN A.I.
CONTROLLER AND ABLE TO CURE A
PATIENT WHO OTHERWISE WOULD HAVE
DIED AND CURE EVERY PATIENT.
THE FUTURE.
DOING WHAT IS NECESSARILY HARD.
I MAKE INTO BONES ABOUT WHAT MY
PARTICULAR AGENDA IS.
I THINK THE THING THIS
PARTICULAR GROUP DEALS WITH AS
NOTED IN THE ORIGINAL PANEL
REPRESENTS THOUGHTS, APPROACHES
AND BIOLOGICAL TARGETS THAT DO
NOT FIT INTO STANDARD REVIEW
PANELS.
SORRY, I'LL USE THAT WORD.
THIS IS VERY OFF THE WALL IDEAS
THAT ARE DIFFERENT FROM WHAT
MOST PEOPLE WHO EVEN THINK THEY
ARE INNOVATIVE IN THEIR
PARTICULAR DOMAINS DON'T GET IT.
THE HARD PROBLEM I WANT TO BE
ABLE TO ADDRESS IS HOW DO WE
TAKE THE VAST MAJORITY DOES.
THEY STUDY MOLECULES AND HOW IT
AFFECTS CELLS INTO EFFECTIVE
THERAPEUTICS WHICH IS WHAT THE
VAST MAJORITY OF PHARMA DOES,
THEY MAKE DRUGS.
THE INSIGHT FROM THIS PARTICULAR
GRANT ARE THE NEED TO ACCOUNT
FOR HETEROGENEITY AND HOW IT'S
MANIFESTED AND HOW WE WE SIGHT
AND THE PERPETUAL INCOMPLETENESS
WE HAVE TO DEAL IN AND WE CAN'T
EXPECT TO KNOW EVERYTHING TO DO
SOMETHING USEFUL.
THE METHODOLOGICAL LIMITATIONS
WE HAVE WE NEED TO FOCUS ON IN
MOVING FORWARD.
THE ASSERTION IS BEING ABLE TO
ADDRESS THIS PARTICULAR PROBLEM,
REQUIRES COMPLEX MECHANISM BASED
MULTI-SCALE MODELS AND EVOLVING
HOW WE THINK ABOUT PARAMETERS,
EITHER NOT JUST RATE CONSTANCE
AND CALIBRATION AND VALIDATION,
I.E. IT'S NOT JUST FITTING.
ADJUSTING WHAT IS CONSIDERED TO
BE A COMPUTATIONAL TASK BECAUSE
WE CAN RUN BILLIONS OF
SIMULATIONS.
THAT SHOULD NO LONGER BE A
LIMITATION AND ACCEPT THAT
UNIDENTIFIABILITY IS INFERETRIN
TO IDENTIFYING WHETHER A DRUG OR
CONTROL STUDY WILL WORK IN A
CLINICAL POPULATION.
THANK YOU VERY MUCH I APPRECIATE
ALL THE WORK MY COLLEAGUE HAS
DONE BECAUSE HE'S DONE ALL THE
WORK AND I TALK ABOUT IT.
IF YOU SEE HIM, THANK HIM FOR
ME.
LAST THING, PART OF THE VIRAL
PANDEMIC WORKING GROUP ONE
OUTPUT IS THE GLOBAL ALLIANCE
FOR IMMUNE PREDICTION AND
INTERVENTION AND TALK TO TOM
HELIKAR TO FIND OUT MORE.
>> I WANT TO REMIND EVERYBODY TO
POST QUESTIONS IN THE WIKI.
THERE'S ONE COMMENT OF THE
VALLEY OF DEATH.
ONE QUICK QUESTION AND YOU'RE
LOOKING FUTURISTIC IN TERMS OF
LARGE DATA CAPTURE.
AGE WITH CHAT GPT CATCHING DATA
FOR SOME MODELS IS ESSENTIAL DO
YOU THINK WE'RE PAST AN
INFLECTION POINT ON WHAT'S GOING
TO ACCELERATE THESE THINGS OR
STILL IN ITS INFANCY.
AND WE ARE USING MODEL-DRIVEN
EXPERIMENTAL DESIGN.
THAT'S SOMETHING WE DON'T DO AND
BIOLOGISTS WANT TO THINK THEY
HAVE THE BEST IDEAS AND HARD TO
CONVINCE THEM SOMETHING THEY
DON'T OTHERWISE WANT TO DO.
THAT'S SOMETHING WE NEED TO
ASPIRE THIS AND IN A CONTROL
LOOP AND SEPSIS IS REAL FAST BUT
REAL TIME DOESN'T NEED TO BE ALL
THE TIME RIGHT NOW.
SOME DISEASE PROCESSES CAN TAKE
LONGER.
YOU DON'T HAVE TO DEAL WITH IT
ALL THE TIME.
THAT PARTICULAR THING TO DIRECT
THE TYPE OF DATA COLLECTION WE
NEED IS ADVANTAGEOUS.
I'LL INTRODUCE OUR NEXT SPEAKER
FROM THE UNIVERSITY OF FLORIDA.
>> THANK YOU FOR THE OPPORTUNITY
TO TELL YOU ABOUT THE WORK WE'VE
DONE.
WE HAVE BROADLY MODULAR DESIGN
USING THE IMMUNE RESPONSE TO
RESPIRATORY FUNGAL INFECTIONS AS
A GUIDING USE CASE AND THE
FUTURE IS MEDICAL DIGITAL TWIN
TECHNOLOGY.
>> THIS IS WHAT WE THINK AS THE
CENTRAL PAPER THAT CAME OUT OF
THIS GRANT.
IT'S A FRAMEWORK TO MAKE
COMPUTATIONAL MODELS MODULAR.
WHEN WE STARTED THIS I WAS NOT
THINKING ABOUT DIGITAL TWINS IN
THE MEANTIME BY THE TIME WE GOT
TO PUBLISH THIS WE DID AND IT
SEEMED LIKE A PERFECT TECHNOLOGY
FOR IT.
WHAT I'M GOING IT TELL YOU IS A
SIMPLE HIGH-LEVEL VIEW.
WE HAVE A POSTER HERE, NUMBER
EIGHT.
IF YOU WANT TO SEE DETAILS OF
THIS, PLEASE GO AND SEE THE
POSTER.
SO THEN MANY APPROACHES TO
MODULARIZING MODELS AND THE
APPROACH WE PRESENT HERE IS
REALLY QUITE SIMPLE.
THE IDEA IS RATHER THAN
MODULARIZING THE MATHEMATICAL
MODEL YOU MODULARIZE THE
SIMULATION MODEL.
IT CREATES A HUB AND SPOKE
ARCHITECTURE WHERE THE HUB IS
THE MODEL STATE.
THAT'S WHERE ALL THE DATA LIVE
THAT CHARACTERIZE THE MODEL AT A
GIVEN POINT IN TIME AND THE
INDIVIDUAL MODULES OPERATE ON
THIS DATA SPACE THE MODEL STATE.
THEY MODIFY SOME PORTION FOR
THIS PARTICULAR MODULE AND
SUBMODEL AND NO TWO PASS DATA
AND THE WAY INDIVIDUAL DYNAMIC
PROCESSES ONLY THROUGH AN
OVERLAP OF THE MODEL STATE THEY
MODIFY.
IN THIS CASE MODULES BECOME
TRULY INDEPENDENT.
CAN TURN OFF ONE WILLY-NILLY.
ANYTHING BREAKS WH.
WHEN YOU ADD NEW BIOLOGY YOU ADD
DIFFERENT PIECES AND NEED TO
MODIFY MODULES TO RECOGNIZE THE
NEW BIOLOGY.
IF YOU DON'T DO THAT THERE'S
ALSO NO HARM DONE.
SOME PARTS WILL SIMPLY NOT BE
CORRECT BUT IT CONTINUES TO
EXECUTE.
HERE'S THE URL GRANT WITH THE
NSF GRANT.
THIS GRANT HAS HAD A TREMENDOUS
IMPACT ON MY RESEARCH AND THAT
OF MY GROUP AND COLLABORATORS IN
MANY PREDICTABLE WAYS BUT LOTS
MORE UNPREDICTABLE WAYS.
I WILL SAY SOMETHING MORE ABOUT
THAT IN A MINUTE.
THERE'S GUIDING MEDICALCATION IS
THE AND THERE'S RESPIRATORY
INFECTIONS FROM FUNGI.
YOU CAN SEE ON THE LEFT IT'S A
MULTISCALE PHENOMENON.
IT REQUIRES AN ORGANISM LEVEL
COMPONENT.
WE BRING IN SPORES.
THEY LODGE IN THE RESPIRATORY
TREE.
THERE ARE TISSUE SCALE PHENOMENA
AND INTRACELLULAR PROCESSES
INVOLVED IN THE WHOLE THING.
IT'S INHERENTLY A MULTISCALE
PROCESS.
A LOT OF THE EXPERIMENTAL WORK
THAT SUPPORTED THIS WAS
SUPPORTED BY AN ACCOMPANYING RO1
FROM NIAID.
WE PUBLISHED A MODEL IN THE
UPPER RIGHT.
YOU CAN SEE A PAPER IN THE
JOURNAL OF THE ROYAL SOCIETY
INTERFACE WE APPLIED IT IN A
NUMBER OF WAYS.
WHAT YOU SEE ON THE LOWER RIGHT
IS A RECENT PAPER WE PUBLISHED
USING A VERSION OF THE MODEL
THAT LOOKS AT THE QUESTION AS TO
WHY SOME PATIENTS WITH SEVERE
COVID CAN DEVELOP INVASIVE ASPIR
GELOSIS AND IT'S A PROBLEM ONLY
FOR A SEVERELY IMMUNOCOMPROMISED
PATIENTS.
WHAT'S THE FUTURE OF MEDICAL
DIGITAL TWINS THERE'S A GENERIC
MODEL AND THERE'S ONE EXAMPLE WE
WANT TO USE.
THAT'S COMBINED FROM DATA AND
BECOMES PERSONALIZED AND
ADAPTIVE OVER TIME.
MAYBE A PERSONALIZED ADAPTIVE
MODEL FOR GARY MAY BE A BETTER
TERM SINCE HE DOESN'T LIKE THE
DIGITAL TWIN BUT IT'S A THING.
WE SHOULD PROBABLY STICK TO IT.
WE'D LIKE TO BUILD A DIGITAL
TWIN FOR PNEUMONA PATIENTS FROM
ALL KINDS OF PATHOGENS.
THE IDEA IS YOU HAVE A PNEUMONIA
PATIENT IN THE HOSPITAL AND
YOU'D BE ABLE TO PREDICT FOR
THAT PARTICULAR PATIENT WHAT
MALL INTERVENTIONS MIGHT BE AND
ON THE ONE HAND IS CHALLENGING
BECAUSE THE TIME FRAME IS SHORT
LIKE IN GARY'S CASE HE'S
INTERESTED IN SEPSIS.
THINGS HAPPEN FAST.
ON THE OTHER HAND THAT'S WHERE
COMPUTATIONAL FORECASTING
MODELLING LIKE THIS WOULD BE
USEFUL IF TIME IS OF THE
ESSENCE.
IT WOULD NOT BE SURPRISING TO
THIS COMMUNITY MECHANISTIC
MODELS ARE IMPORTANT FOR THIS
PURPOSE.
THERE'S NO ACTIONABLE
INFORMATION TO A PHYSICIAN HOW
THAT CAN BE CHANGED IF WE CAN
MAKE MECHANISTIC MODELS OR SOME
MECHANISMS IMPLEMENTED THERE
WOULD BE A CHANCE TO DISCOVER
MODELS CAN PLAY AN IMPORTANT
ROLE.
WE WERE FABER SURPRISED TO SEE
THE PEOPLE THAT SIGNED UP AND
THE IDEAS I'VE GOTTEN AND THE
DISCUSSIONS AND MANY OF THE
PEOPLE THIS IS BEING CONTINUED
BY JAMES AND TOM HELIKAR.
THERE'S A COUPLE PAPERS FROM THE
WORK GROUP IN ADDITION TO MANY
OTHERS AND ONE OF THE THINGS WE
FOCUSSED ON IS HOW DO WE GO
ABOUT BUILDING DIGITAL TWINS AND
WHAT ARE THE MEDICAL NEEDS.
WHAT ARE THE MODELLING NEEDS.
WHAT ARE THE EXPERIMENTAL NEEDS.
I THINK WE HAD A NUMBER OF
COMMENTS THIS MORNING ABOUT HOW
DO WE TRAIN PEOPLE FOR
MULTI-SCALE MODELLING.
THIS IS A PARTICULARLY
CHALLENGING ENVIRONMENT YOU HAVE
MEDICINE AND MODELLING AND
EXPERIMENT IN PEOPLE WHO ARE
WITHIN THIS SPACE TO WORK.
THEY NEED TO UNDERSTAND
SOMETHING ABOUT ALL THREE OF
THEM.
DENISE THIS MORNING MENTIONED
MATHEMATICS.
MATHEMATICS PLAYS AN INCREDIBLY
IMPORTANT ROLE IN A NUMBER OF
WAYS.
FOR EXAMPLE, DEVELOPMENT OF
FORMAL METHODS IS A REALLY
IMPORTANT ONE.
AND IF YOU WANT TO HEAR MORE
ABOUT DIGITAL TWINS, THERE'S A
BREAKOUT SESSION TOMORROW
AFTERNOON.
BREAKOUT SESSION 1.
I ENCOURAGE YOU TO GO THERE AND
THAT'S ALL I HAVE TO SAY.
THANK YOU.
[APPLAUSE]
.
>> THANK YOU, EVERYBODY FOR
USING THE WIKI.
WE HAVE SOME QUESTIONS COMING
IN.
ONE WAS PUT A COUPLE DAYS AGO BY
MG KOLBANK AND I'LL PARAPHRASE,
HOW COULD WE HANDLE USING OUR
DATA FROM MULTIPLE AND UNIQUE
EXPERIMENTS WILL THERE BE A
CHALLENGE FOR STANDARDIZATION AS
YOU DEVELOP THESE?
>> THERE'S THE CHALLENGE OF
STANDARDIZING DATA.
DATA FROM ONE HOSPITAL WILL BE
VERY DIFFERENT FROM ANOTHER
HOSPITAL.
OF COURSE THE DATA ULTIMATELY
GOES TO BUILDING A DIGITAL TWIN
LIKE THIS STARTS WITH
EXPERIMENTAL DATA FROM IN VITRO
EXPERIMENTS, MOUSE EXPERIMENTS,
TISSUE CULTURE EXPERIMENTS,
ORGANOID EXPERIMENTS.
PATIENT DATA EXTRACTED FROM
PATIENT SAMPLES AND ELECTRONIC
HEALTH RECORDS.
THERE'S A TREMENDOUS NUMBER OF
DIFFERENT THINGS COMING IN AND
HERBERT MADE THAT POINT,
STANDARDS ARE IMPORTANT AND FOR
EXAMPLE THE EUROPEAN UNION HAS A
LARGE SCALE STANDARDS PROJECT ON
THE EUROPEAN HEALTH SPACE THAT
FOCUSES ON DATA STANDARDS AND
MODEL STANDARDS.
>> ONE MORE.
DR. CHEN FROM NIH SAID REGARDING
ADAPTABILITY IN THE CONTEXT OF
DIGITAL TWINS HOW MUCH REAL
WORLD DATA WOULD YOU EXPECT
YOU'D NEED TO START A MODEL AND
WHEN WOULD IT BE UPDATED?
>> IT WILL DEPEND ON THE TIME
SCALES ON WHICH YOU NEED THE
INFORMATION.
FOR EXAMPLE, IF YOU WORK ON TB
LIKE DENISE DOES OR CANCER, YOUR
TIME SCALES WILL BE VERY
DIFFERENT OF SOMEBODY INTERESTED
IN SEPSIS OR PNEUMONIA.
IT'S TOTALLY DETERMINED BY YOUR
APPLICATIONS.
AND FOR THAT MATTER BY WHAT YOU
CAN DO IN THE CLINIC.
>> SO IT DEPENDS.
FAMOUS ANSWER.
THANK YOU.
>> CAN YOU HEAR ME?
THANK YOU FOR THE OPPORTUNITY TO
PRESENT.
IT'S A PLEASURE TO BE HERE.
WE ARE A RELATIVELY NEW GROUP IN
MULTI-SCALE MODELLING COMMUNITY.
WE HAVE TWO MODELERS AND ONE
IMMUNOLOGIST AND ONE VIROLOGIST
AND HAVE A GREAT GROUP OF
STUDENTS AND POSTDOCS.
AND WE HAVE THIS MULTI-SCALE
MODELLING GRANT.
HERE I HIGHLIGHT A FEW RESULTS.
ORIGINALLY WE PLANNED TO USE BE
AN EXPENSIVE MOUSE MODEL SYSTEM
TO UNDERSTAND THE RULES OF
WANING IMMUNITY AND THEN
HOPEFULLY TRANSLATE IT TO THE
HUMAN MODELS AND OPTIMIZE
IMPLEMENTATION AND ALSO DEVELOP
BUT THANKS TO THE PANDEMIC, WE
HAD LOTS OF DIRECT HUMAN DATA.
AND I WILL SHOW SOME OF THE
RESULT OF THAT DATA AS WELL
BECAUSE IT WAS FOOD TO OUR
GRANT.
THERE ARE MAJOR GAPS TO FILL.
WE NEED TO HAVE A GOOD HOST
MODEL.
THAT'S A START.
WE STILL DON'T UNDERSTAND THE
WANING OF IMMUNE RESPONSES THAT
GOES AFTER INFECTIONS AND
VACCINATION.
IN CASE OF VIRUSES THAT EXHIBIT
STRAIN VARIATION IT'S NOT SO
SIMPLE.
EVERY TIME YOU BUILD IMMUNITY
AND THE INTERACTIONS ARE QUITE
COMPLEX.
SECOND CHALLENGE WE NEED TO
BETTER UNDERSTAND HOW TO LINK
BETWEEN HOST MODEL.
WHAT ARE THE CORRELATIONS OF
PROTECTION AND ARE WE MISSING
SOMETHING, WHAT'S THE
CONTRIBUTION OF DIFFERENT SITES
TO THE PROTECTION AND FINAL
CHALLENGE HOW TO WE VALIDATE THE
MODELS.
WHAT DATA DO WE NEED?
I'LL TOUCH ALL OF THIS.
FIRST I WANT TO START FROM THIS
PAPER.
USUALLY VACCINES IS UPDATED IF
THEY GENERATE A RESPONSE AND
HERE IS A SURPRISING RESULT FROM
THE EXPERIMENTS THAT WERE DONE
IN MARK UP MODEL SYSTEM.
THIS IS WITH PRIMATES.
THEY DID MACAQUE IMMUNITY
EVALUATION AND THIS IS ANTIBODY
RESPONSE TO ORIGINAL STRAIN,
WUHAN STRAIN IN THE VACCINE AND
THEY GAVE TO THE MACAQUE THE
MODERNA BOOST OR OMNICRON BOOST
AND IT WAS SURPRISING THE
RESPONSES WERE GENERATED VERY
SIMILAR TO WUHAN AND OMICRON
STRAIN.
THEN WE STRUGGLED IN THE RESULT
IN WHAT WE LEARNED.
WE MODELLED THE RESULT FOR H1N1
AND HERE'S THE SIMULATION AND
WHAT THE MODEL HAS IS SEVERAL
KEY FEATURES AND THERE'S A
LIMITED ANTIBODY RESPONSE AND A
RULE FOR CROSS-IMMUNITY.
YOU HAVE SOME RULES FOR IMMUNITY
AND HOW IT'S BOOSTED AND WE
CONSULTED WITH OUR IMMUNOLOGIST.
THE RESULT IS MIXED RESULT.
ACTUALLY THERE WAS AN EXPANSION
OF IMMUNE RESPONSE TO OMNICRON
AND BECAUSE THE TOXIN AND IT WAS
LOW YOU DON'T SEE IT IN THE
EXPERIMENT.
IF YOU GIVE ANOTHER SEVERAL
BOOST, THIS IS OMNICRON OR WUHAN
STRAIN THEN YOU'LL SEE THE
DIFFERENCE.
THAT MEANS IT'S ACTUALLY BETTER
TO GIVE OMNICRON TO PRIME THE
IMMUNITY.
NOW, I'LL HIGHLIGHT A FEW OF THE
RESULTS.
SO IF YOU WANT TO UNDERSTAND HOW
THE IMMUNITY WANE AND HERE'S THE
RESULT FOR THE T CELL RESPONSE.
IDEALLY WE NEED THE LONGITUDINAL
DATA.
ALSO, WE WOULD LIKE TO FIND THE
VARIOUS THAT IS NOT IN
CIRCULATION.
GIVE IT ONCE AND SEE HOW IT'S
WANING AND THEY GIVE YELLOW
FEVER VACCINE TO PEOPLE AND THIS
IS GENERATED AND QUICKLY
RESOLVED AND THEN ONE DOSE OF
THE VACCINE GENERATE THE LONG
LASTING IMMUNITY.
AND FOR EACH INDIVIDUAL THE DATA
CONNECTED IN LINE.
THE BLACK SQUARES ARE DIFFERENT
STUDIES.
THIS IS CROSS SECTIONAL STUDY
AND HERE CONTRIBUTES GIST ONE
SAMPLE.
THE DATA EXPANDS OVER 30 YEARS
AND BOTH IS A LOG SCALE.
FIRST THE STUDY COLLECTED AT
FIRST HERE THE BEST MODEL THAT
FEEDS THE DATA WAS SIMPLE
SURPRISINGLY.
AND WHAT IS MORE SURPRISING IS
IF WE CONTINUE THE SLIDE IT'S
ALSO VERY NICELY CONSISTENT WITH
DIFFERENT DATA SET.
WE CAN CONTINUE THE IMMUNITY FOR
THE CELLS AND IT'S BASE CRY JUST
THE PARAMETERS AND THEN COVID
CAME IT WAS ANTIBODY DATA THAT
WE ANALYZED.
FOR DIFFERENT EPITOPES AND FOR
DIFFERENT PART OF THE VIRUS THE
ANTIBODIES ALSO CAPTURED NICELY
WITH THE SIMPLE PARALLEL.
NOW WE CAN USE THE PARALLEL TO
CONFER DIFFERENT ARRANGE.
S.
THE MODERNA VACCINE IS BETTER
THAN PFIZER.
HERE YOU CAN COMPARE THE THREE
SETS OF DATA BECAUSE THE SAMPLES
WERE PROCESSED AND I ALSO
STRAINED THE EXCESS SO YOU CAN
SEE AND DO THE COMPARISON.
AND THERE'S HUGE HETEROGENEITY
IN HUMAN RESPONSES TO THE
INFECTION.
MUCH LESS TO THE VACCINES.
THEN HAVE YOU THE HOST MODEL AND
WE HAVE THE HOST MODEL WITH
DIFFERENT LEVELS OF COMPLEXITY.
AND BETWEEN THE HOST MODEL WE
CAN USE THE MODEL THAT MANY
PEOPLE USE.
AND WHAT DATA DO WE NEED TO
VALIDATE THE MODEL?
IF YOU WANT TO UNDERSTAND HOW
VACCINE INDUCED IMMUNITY WANES
SO WE CAN DO THE INTERVENTION
AND UPDATE THE VACCINE, WE NEED
TO ESTIMATE THE VACCINE INDUCED
PROTECTION IN THE POPULATION.
IF YOU LOOK AT THE LITERATURE
AND IT CAME OUT THAT IT'S A LONG
STANDING CHALLENGE.
ESPECIALLY FOR OTHERS IT WANED
SEASONALLY AND FOR COVID IT
WANED VERY FAST.
WE COULDN'T FIND RELIABLE
METHODS TO CAPTURE IT.
ONE OF THE METHODS USED IS
CALLED EXTENSION OF THE HAZARD
MODEL UTILIZING -- YOU LOOK AT
THE METHOD.
AND THEY GENERATED THE
MULTI-SCALE SIMPLE MODEL IT WAS
100,000 INDIVIDUALS AND WE
VACCINATED 40% WITH A SPREAD OF
VACCINATION AND THEN ALLOWED
PEOPLE TO HAVE THE WANING OF
IMMUNITY.
GENERATED THE DATA AND APPLY THE
METHOD.
IT FAILS COMPLETELY TO CAPTURE.
THIS IS WANING BECAUSE THE MODEL
WE KNOW HOW THE WANING SHOULD
LOOK LIKE.
SO WE WENT INTO ALL THE DETAILS
OF THE METHODS.
YOU TAKE THE DATA EXPANSION AND
THEN MADE SEVERAL MORE
APPROXIMATION AND TOOK EACH STEP
THE DATA FROM THE MULTI-SCALE
MODEL AND APPLY THEM LIKE HERE.
AND IT'S VERY NICE SO WE LOOK AT
THE NUMBER OF EVENTS.
AND WHAT WE FOUND IF YOU USE THE
MODEL WITH THIS PARTITIONING IN
IT'S TRICKY HOW TO CHOOSE BUT WE
PUBLISHED THE PAPER WHERE WE
SHOWED HOW TO OPTIMIZE IT AND
CALL IT THE TBI METHOD.
THIS IS VERY NICELY CAPTURED AND
INDUCE PROTECTION AND EVEN WITH
THE EXTENSION IT'S ALREADY
INTRODUCED.
BUT THE BIGGEST HAPPENED ON THE
LAST STEP.
IF YOU TAKE THE DATA AND USE THE
METHODS USED TO MEASURE THIS IS
THE GREEN ONE.
IT'S ACCURATELY CAPTURED ONLY AT
THE PEAK OF PANDEMIC OTHERWISE
UNESTIMATED.
AND THIS IS HETEROGENEITY.
WE BUILT A MODEL WHERE WE
INTRODUCED THE SUSCEPTIBILITY
AND HETEROGENEITY IN VACCINE
RESPONSES AND WE MODEL IT WITH
THE DISTRIBUTION AND LOOKED AT
THE LITERATURE AND METABOLIZED
THE OPERATION FROM THE DATA AND
ALSO INTRODUCED THE
HETEROGENEITY IN VACCINE
PROTECTION.
WE MODELLED SO THAT WAS
ACCORDING TO THE PARALLEL AND
THEN NEED TO WANE THE IMMUNITY
TO THE PROTECTION AND WE DID IT
IN TWO MODELS.
ONE WE HAVE THE CORRELATE MODEL
AND WAS METABOLIZED AND ANOTHER
WAS THE EXTENSION MODEL.
HERE IS THE RESULT.
SO IF YOU DON'T HAVE ANY
HETEROGENEITY, THEN THE METHODS
WE DEVELOPED I SHOWED ON THE
PREVIOUS SLIDE CAN ACCURATELY
CAPTURE THE WANING AND THIS IS
DUE TO ANTIBODIES.
BUT IF YOU INTRODUCE THE
HETEROGENEITY IN SUSCEPTIBILITY
WE MIGHT SEVERELY UNDERESTIMATE
THE WAITING AND IF YOU INTRODUCE
IT IN VACCINE RESPONSES WE TEND
TO OVER ESTIMATE THE WANING.
BUT USUALLY IT'S LIKE TWO
COMPETING HETEROGENEITY ON THE
VACCINES AND THE DIFFERENT
DIRECTION.
AND WE PARTLY METABOLIZED FOR
INFLUENCE AND COVID.
MIGHT FIND IN THE LITERATURE IF
YOU COMBINE PEOPLE USED THE
PREVIOUS
PREVIOUS USED THE METHODS THAT
IS THE NUMBERS YOU'D SEE IN THE
LITERATURE.
AND I WOULD LIKE TO ACKNOWLEDGE
OUR COLLABORATORS.
AND OUR FREE SOFTWARE WE USED
AND JULIA BECAUSE THAT WAS WHAT
WE USED FOR OUR MODEL.
AND FUNDING SOURCES, HERE'S IS A
FEW ONGOING POINTS AND CHANGES
AND I'M HAPPY TO RESPOND TO
QUESTIONS IF THERE ARE ANY.
>> EXCELLENT DATA.
DO WE HAVE TIME FOR ONE
QUESTION.
I DIDN'T SEE ANY IN THE WIKI.
I CAN ASK ONE QUICKLY.
I'M INTERESTED IN THE MODEL FOR
ANTIBODY WANING IN THE COMPLEX
OF THE IMPORTANCE AND WHAT DO
YOU SEE AS THE MOST EXCITING
APPLICATION?
ONE THING I WAS THINKING ABOUT
IS CLINICAL TRIALS WHERE YOU'RE
TRYING TO DECIDE ON A DOSE AND
EARLY IN COVID --
>> THERE'S THE EPIDEMIOLOGICAL
MODEL?
>> MODELLING THE WANING
ANTIBIOTIC RESPONSE AND HOW IT
CORRELATES WITH PROTECTION.
HOW DO YOU SEE IT SO THE FIRST
ONE IS THERE'S DATA HOW IT
INCREASED PRODUCTION DEPENDS ON
THE ANTIBODIES.
IF YOU FLIP IT THEN IT WILL BE
MORE LIKE HOW WE CAME UP WITH
THE EQUATION.
THEN WE DEVELOPED ANOTHER MODEL
STOCHASTIC EXTENSION.
YOU GIVE THE VIRAL TO THE PERSON
AND FOR EACH VARIANT THERE'S THE
PROBABILITY TO BE KILLED OR
SURVIVE.
AND THEN THIS PROBABILITY ALSO
DEPENDS ON THE ANTIBODIES.
IF YOU CAN LEARN YOU'LL GET THIS
RELATIONSHIP.
SO, YEAH.
>> VERY EXCITING.
THANK YOU.
>> DR. SOSHA WILL PRESENT NEXT
FROM JOHNS HOPKINS UNIVERSITY.
>> I'D LIKE TO THANK THE
ORGANIZERS FOR INVITING ME.
I'LL SHOW YOU SOME OF THE
IMMUNOLOGY IN THE FIELD OF USING
CHECKPOINT INHIBITORS OVER
DIFFERENT CANCER THE RATE OF
RESPONSES FAIRLY MODEST.
IT'S LESS THAN 15%.
SECONDLY, THERE'S A COMBINATION
OF DIFFERENT DRUGS USUALLY IN
IMMUNE THERAPIES NOT USED AS A
MONOTHERAPY BUT IN DIFFERENT
COMBINATIONS.
AND PLAUSIBLE OPTIONS.
THERE'S BEEN 3,000 CLINICAL
TRIALS IN THE FIELD.
MOSTLY IN THE UNITED STATES AND
TIMING AND SEQUENCES AND THE
QUESTION IS IT POSSIBLE TO USE
COMPUTATIONAL MODELLING AND
PARTICULARLY THE OPTIMAL
CLINICAL TRIAL DESIGN AND USE IT
AS A MODEL INFORM DRUG
DEVELOPMENT TOOL.
THE FIRST IS A NON-SPATIAL QSP
PLATFORM.
IT GOES FROM MOLECULAR AND
CELLULAR SCALE AND THIS IS IN
THE IMMUNOLOGICAL SYNAPSE AND
THIS IS INCORPORATED IN THE
MODEL.
THE MODEL REPRESENTS THE WHOLE
PATIENT AND CONSISTS OF TUMOR
CENTRAL COMPARTMENT SIR --
CIRCULATING LYMPH NODE AND THE
REST OF THE PATIENT PERIPHERAL
TISSUES.
HERE WE SHOW JUST ONE TUMOR BUT
RECENTLY WE'VE BUILT THIS MODEL
FOR MULTIPLE NODULES AND
TYPICALLY FOR ONE NODULE IT
AMOUNT TO ABOUT 200 ORDINARY
DIFFERENTIAL EQUATION AND
QUICKLY GROWS TO MORE OR LESS A
NUMBER OF METASTASIS.
OR A PATIENT POPULATION COHORT
IN THE CLINICAL TRIAL.
WE CONDUCT VIRTUAL CLINICAL
TRIAL S THIS IS POWERED BY
IMMUNOGENOMIC INFORMATION IN
CREATING VIRTUAL PATIENT.
I'LL SHOW THIS DATA.
THIS IS THE PLATFORM WE APPLIED
THIS TO BREAST CANCER, NON-SMALL
CELL BREAST CANCER AND THE
CARCINOMA THE SUBJECT OF THE UL
1 GRANT AND THIS IS ALSO BREAST
CANCER WITH MULTIPLE METASTATIC
KNOWLEDGE -- NODULES.
THIS IS AN EXAMPLE OF ANALYZING
PHASE 3 TRIPLE NEGATIVE BREAST
CANCER TRIAL WITH A COMBINATION
OF CHEMOTHERAPY WHICH IS A
STANDARD OF CARE AND PDL1
INHIBI
INHIBITOR ATEZOLIZUMAB AND EACH
TRAJECTORY OF A GROWING TUMOR A
CHANCE TO UNRAVEL THE
MECCISTMECCISTIC
LY -- MECHANISTICALLY THE
BEHAVIOR.
THE UPPER GRAPH IS FOR
CHEMOTHERAPY AND TO THE RIGHT
EACH BAR IS A PATIENT.
THIS IS SHRINKING TUMORS AND
PROGRESSING TUMORS, PROGRESSIVE
DISEASE, STABLE DISEASE AND
PARTIAL RESPONSE, COMPLETE
RESPONSE.
IS HAVING CREATE THE PLATFORM
FOR SPECIFIC CANCER AND EVEN
SPECIFIC DRUG I SHOULD SAY THE
SYSTEM IS MODULAR.
FOR EXAMPLE, PHARMACO KINETIC
SAY MODULE AND COULD BE
SUBSTITUTED DEPENDING ON WHAT
DRUG IT IS AND IF THE
COMBINATION IS AN ORAL DRUG THIS
IS ALSO A MODULE.
WE CAN THEN SIMULATE AND LACK
FOR THE PREDICTIVE BIOMARKER.
CAN WE PREDICT IF A GIVEN
PATIENT WOULD RESPOND OR NOT
RESPOND AND WHAT THE AND THE
MYELOID DERIVED SPECIFIC CELLS
AND T CELLS AND ON AND ON AND
THE RATIO OF CD8 CELLS WHICH ARE
TUMOR CANCER CELLS AND
REGULATORY T CELLS THAT SUPPRESS
THE KILLING.
YOU CAN SEE IN THIS CASE, THIS
IS RESPONDERS NOT RESPONDERS FOR
IMMUNOTHERAPY OF ATEZOLIZUMAB
AND THIS IS ELEVATED IN
RESPONDERS.
YOU CAN SEE THERE IS NO VERY
CLEAR BIOMARKERS.
THERE'S A LARGE OVERLAP OF
VARIABLES.
THAT'S WHAT THIS CAN DO AND YOU
CAN HAVE STATISTICAL METHODS TO
ASSESS PARAMETERS.
THIS IS A NON-SPATIAL MODEL.
BUT TUMOR SPATIAL HETEROGENEITY
IS WELL KNOWN HALLMARK OF
CANCER.
IT DETERMINED THE RESPONSE TO
THERAPY.
DETERMINES DRUG RESISTANCE AND
SO ON.
SO WE'VE DECIDED TO BUILD A
SPATIAL PLATFORM CALLED SPQSP
AND DENISE MENTIONED THIS
MORNING THERE'S A POSTDOC FROM
SEVERAL YEARS AGO AND
RESPONSIBLE FOR STARTING THE
MODEL.
WE COMBINE THE WHOLE PATIENT QSP
OR BASE WITH AN AGENT-BASED
MODEL FOR 3-D PRESENTATION FOR
WHOLE TUMOR OR PORTION OF A
TUMOR.
SOMETIMES FOR A LARGE TUMOR IT'S
COMPUTATIONALLY NOT POSSIBLE TO
REPRESENT THE WHOLE TUMOR.
IT LOOKS LIKE THIS ON THE LEFT.
WE HAVE A WHOLE PATIENT
REPRESENTATION AND QSP AND ON
THE RIGHT WE HAVE AN AGENT-BASED
MODEL BUILT FROM A 20 BY 20 BY
20 VOXELS.
CORRESPONDING TO A SINGLE CANCER
CELL AND YOU CAN SEE THERE'S
DIFFERENT TYPES OF CANCER CELLS
AND THERE ARE DIFFERENT FLAVORS
OF IMMUNE CELLS AND THE
COMPLEXITIES IS GROWING AS WE
PROCEED WITH THE MODEL AND WE
APPLIED THIS TO BREAST CANCER,
LUNG CANCER, CELLULAR CARCINOMA.
THERE'S A MOLECULES INCLUDING
IMMUNE CHECKPOINT AND SOLUBLE
FACTORS LIKE TWO INTERFERON
GAMMA AND DRUG AND THOSE ARE
DESCRIBED BY PARTIAL
DIFFERENTIAL EQUATIONS.
HERE'S AN EXAMPLE.
THERE'S GROWING TUMOR GROWTH
FROM THE CELL AND WE HAVE A
TYPICAL TREATMENT PERIOD.
ON THE LEFT IS TREATED WITH
INHIBI
INHIBITOR.
AND THE TUMOR IS EVENTUALLY
DISSOLVED.
AND WE CAN PREDICT THIS AT THE
LEVEL OF INDIVIDUAL PATIENT.
THIS IS POWERED BY A SINGLE CELL
RNA SEQ ANALYSIS NON SPATIAL.
WE ASSIMILATE THE TUMOR FOR A
YEAR AND A SPLICE THROUGH THE
TUMOR THAT SHOWS THE IMMUNE
ARCHITECTURE OF THE COMPLEX
TUMOR MICROENVIRONMENT AND YOU
CAN SEE.
THESE ARE THE NOTATIONS FOR
DIFFERENT CELLS THIS
ARCHITECTURE IS MIXED.
THE TUMOR IS SO-CALLED COLD AND
ON THE RIGHT IS
COMPARTMENTALIZED ARCHITECTURE
OF WARMER OR HOT TUMOR AND THAT
TURNS OUT TO BE RESPOND TO A
NON-RESPONDER AND RESPONDER.
SO THE NEXT COUPLE SLIDES IS THE
ACTUAL RESULT OF THE UL 1
MULTISCALE GRANT AND HERE WE
SIMULATED THE NEAR ADJUVANT
TRIAL FOR HEPATOCELLULAR
CARCINOMA WHICH IS CANCER WITH
LOW RESPONSE RATE.
THIS WAS ANTI-PG1 INHIBITOR IN
CONSTITUTION WITH MULTI-KINASE
INHIBITOR NEOADJUVANT AND WE
AIDED THIS WITH DIGITAL
PATHOLOGY AND SPATIAL
TRANSCRIPTOMICS.
THIS IS DOWN TO SINGLE CELL
RESOLUTION.
THE TECHNOLOGIES QUICKLY
EVOLVING IS THEN THIS CASE
THERE'S 27 MARKERS OR LABELS ON
THE CELL BUT THAT IS JUST
LITERALLY TAKING A COUPLE YEARS
AND RESEMBLING WHAT WE SHOWED ON
THE LAST MOVIE ON THE LEFT IS
NON-RESPONDER WITH MIXED
ARCHITECTURE.
THE IMMUNE CELLS AND THIS IS
COMPARTMENTALIZED ON THE RIGHT.
THAT HAPPENED TO BE A RESPONDER
AND YOU CAN SEE QUITE A BIT OF
COMPLEXITY.
IN THIS WORK WE ANALYZED THE
SPATIAL IMAGES USING SPATIAL
STATISTICS AND METHODS AND
MACHINE LEARNING.
THE NEXT ONE IS THE RELATIVELY
NEW TECHNOLOGY OF SPATIAL
TRANSCRIPT OMICS.
THIS IS USING PHYSIOME.
THIS IS A WHOLE SLIDE FROM
RECEPTIVE TUMOR.
THIS WAS RECENTLY PUBLISHED
USING MACHINE LEARNING METHODS
OF HOW TO FIND PATTERNS IN THIS
COMPLEX PICTURE SO UP THIS CASE
IT'S NOT SINGLE CELL RESOLUTION
BUT 50 MICRONS UP TO EIGHT, 10
CELLS.
IT HAS TO BE COMPUTATIONALLY
DEINVOLVED.
THIS IS USING THE MODEL FOR THAT
SPECIFIC TRIAL FOR INHIBITOR
CELLULAR CARCINOMA.
SO BASED ON OUR DIGITAL
PATHOLOGY WORK WHERE WE FOUND
SPECIFIC BIOMARKER WHICH WE CALL
THE RISK SCORE AND THAT HAS TO
DO WITH THE PROXIMITY OF CD4 TO
CD 8 CELLS AND MYELOID DEPRIVED
SUPPRESSER CELLS SO WHICH ARE
CLOSER TO WHICH.
YOU CAN SEE THE RISK SCORE AND
IT SEPARATES NON-RESPONDER AND
RESPONDER.
AND WOULD BE PREDICTIVE AND NOT
USEFUL WOULD NOT BE GIVEN TO THE
PATIENT AND THE OTHER WAY
AROUND.
INTERESTINGLY THAT IS WHAT'S
MENTIONED THIS MORNING SPATIAL
ANTROPY TURNS OUT TO BE A USEFUL
METHOD.
IN CONCLUSION, JUST AND
PHARMACOLOGY IS A POWERFUL TOOL
TO ANALYZE CLINICAL DATA AND THE
FIELD IS RIGHT FOR APPLICATIONS
OF QSP MODELS FOR DRUG
DEVELOPMENT AND DESIGN OF
CLINICAL TRIAL AND EMERGENT
TECHNOLOGIES SUCH AS SPATIAL
TRANSCRIPT OMIC AND MULTIPLEX
DIGITAL PATHOLOGY THAT WILL HELP
OUR THIS COMPLEX MODEL.
OF COURSE I'M VERY GRATEFUL TO
THE PEOPLE WHO DID THE WORK.
MY CURRENT LAB AND THEN RECENT
ALUMNI WHO AS YOU CAN SEE MOSTLY
ARE IN INDUSTRY AND THEN
NUMEROUS COLLABORATORS AND
IMMUNOPATHOLOGIST AT HOPKINS AND
BEYOND.
VERY IMPORTANTLY THE MULTISCALE
GRANT AND ANOTHER RO1 AND ALSO
INDUSTRIAL SUPPORT.
THANK YOU VERY MUCH.
>> THANK YOU FOR THE QUESTIONS
OR FOR THE PRESENTATION.
WE'LL KEEP MOVING BECAUSE WE'RE
RUNNING BEHIND SCHEDULE.
THERE'S A QUESTION IN THE WIKI
MAYBE YOU CAN ADDRESS OFFLINE.
WE'LL MOVE TO OUR FINAL SPEAKER
OF THE SESSION.
>> I WAS LAST TO BE FUNDED IN
THE UL 1 SO BASICALLY IN TWO
DAYS MY UL 1 IS OVER.
WE NODE -- KNOW THE CENTER OF
THE BIOLOGICAL SYSTEMS AND THE
CELL COMMUNICATE WITH OTHER
GROUPS AND PROPERTIES OF THE
TISSUES.
10 YEARS AGO THIS IS OFTEN A
BIASSED APPROACH.
BEFORE THIS TECHNOLOGY MANY KNOW
YOU HAVE THE CELL TYPE.
YOU CAN GET ALL THE CELLS BASED
ON THE TISSUE AND ALL THE GENES.
SO BASICALLY THE BEAUTIFUL
MATRIX IN THE VARIABLE
STRUCTURES.
AND THEN THE CELLS CAN GO AS
MANY AS A MILLION AND THE GENES
OF COURSE YOU CAN RECOVER ALMOST
ALL THE GENES IN THE HUMAN BODY
BY 25,000.
WHAT'S THE MAJOR EFFORT NOW?
IT IS CALCIFICATION.
YOU CAN SEE MANY EFFORT AND IN
MANY IN THE AREA COMBLASSIFY AL
THE CELLS.
THE DATA IS HIGH DIMENSION.
YOU CUT A PIECE OF TISSUE AND
FIX IT AND IT.
SEQUENCE IT.
THE QUESTION IS HOW CAN YOU FROM
STATIC DATA LOOK INTO MORE
MECHANISTIC LANGUAGE AND WE
RESULT TO C AND AT THE TIME X.
THAT'S THE LANGUAGE WE USE AND
BASICALLY THAT'S THE MECHANISM
WHEN YOU NEED THIS.
ONE OF THE FOCUS IN OUR UL 1
GRANT AND IS ABOUT HOW TO DO
THIS.
TODAY I'M GOING TO SHOW
EXAMPLES.
SO THE APPROACH FOR DOING THIS
IS THIS CLASSIC LANGUAGE IN
STATIC THINKING.
SCALES, CLUSTER, LINEAGE,
TRANSITION.
THAT'S THE LANGUAGE.
AND THIS IS A PARTICLE ATTRACTER
TRANSITION PASS AND USING THIS
LEARNING THIS IS SUPERVISED
LEARNING THIS ALSO IS
UNSUPERVISED LEARNING BUT YOU
HAVE MORE EXPLICIT MATHEMATICAL
STRUCTURE AND CAN HAVE A MORE
INTERPRETABLE MODELS.
FOR PEOPLE WHO KNOW ATTRACTERS
CAN SHAKE IT TO MAKE IT STABLE
TO OTHER ATTRACTERS BUT IN
BETWEEN THE TRANSITION STATE HOW
DO CONSTRUCT THIS?
THIS IS BASED ON RANDOM WORK AND
LET'S NOT GET INTO THE DETAILS.
WHAT'S THE OUTCOME?
THE COMPUTATIONAL OUTCOME OF THE
MODEL IS DYNAMIC OF THE CELLS.
AND THERE'S INDIVIDUAL CELLS AND
YOU CAN PROJECT THE CELL ON THE
LANDSCAPE.
AND YOU CAN COMPUTE THE
TRANSITIONS AND QUANTIFY THE
CELLS AND 29% PROBABILITY AND
YOU CAN FIND IN TRANSITION
GENES.
I DON'T WANT TO GET IN BIOLOGY
BUT IT'S WHAT THE TOOL CAN GIVE
YOU AND MORE COMPLEX CELL
LINEAGES.
AND WE CAN QUANTIFY THAT AND
CONFER IT AND DISCOVER NEW CELL
INFORMATION.
THAT'S OUR WORK AND WE PUBLISHED
THE WORK AND A GROUP OF
RESEARCHERS IN DUKE USED THE
WORK AND IT'S OUR ENTROPY IN OUR
PAPER AND CAN COMPUTE.
IT DOES NOT COMMIT UNTIL THEY
RELATE.
THIS IS NOA FUNDING AND THE TOOL
IS VERY USEFUL AND THIS IS VERY
SENSITIVE TO THE VARIOUS.
IT'S VERY IMPORTANT TO THE OVER
ALL RESPONSE OF THE LUNG
TISSUES.
ONCE YOU HAVE THE ATTRACTERS AND
IF YOU WANT TO LOOK AT THE GENE
REGULATIONS DOWN STREAM YOU CAN
CONSTRUCT THE TOOLS TO CONSTRUCT
DATA SPECIFIC G.I. ENDS AND IN
PARTICULAR THIS CAN GIVE YOU
QUANTITATIVE INFORMATION ABOUT
WHICH DIRECTION, WHICH GENE
DIRECTION.
REMEMBER THE 25,000 POSSIBLE
GENES IN HUMAN.
WHICH GENE COULD BE THE MOST
LIKELY TO MAKE A TRANSITION?
KNOW LET'S SAY CAN WE MAKE THE
INFORMATION MORE SPATIAL?
BASICALLY, WE ALL KNOW CELLS
TALK TO EACH OTHER THROUGH
RECEPTORS.
CAN WE USE THIS DATA TO
CONSTRUCT, YES?
WE COME UP WITH A SIMPLE KINETIC
MODEL AND USE THE INFORMATION
AND REGULATORS, YOU HAVE MANY
SAMPLES WITHIN THE CLUSTER AND
HAVE MANY CELLS YOU CAN USE AS A
WAY TO DO TESTINGS.
YOU CAN COMPUTE THE PROBABILITY
OF THE COMMUNICATION.
THEN WE REALIZED WELL, THIS IS
COMPREHENSIVE AND YOU HAVE TO
EXPLORE ALL THE POSSIBLE
RECEPTOR PAIRS.
WE LOOKED AT THE DATABASE AND
TRIED TO MAKE IT MORE
COMPREHENSIVE AND THERE WERE
2,021 PAIRS IN 2021.
I DON'T KNOW THE COINCIDENCE
THERE.
THEN WE PUBLISHED THIS CELL CHAT
AND WITHIN TWO YEARS THE
CITATIONS ARE MORE THAN 1100.
IT'S RANKED HIGH IN THE
JOURNALS.
IT'S TO OUR SURPRISE.
LET ME SHOW YOU WHAT THE TOOLS
CAN DO FOR YOU?
WE DESIGN THE EXPERIMENTS FOR
MANY PEOPLE AND WE CARE ABOUT
THE AGING.
SO IT'S MORE LIKE A 70-YEAR-OLD
MAN AND THE NEW MOUSE AND CUT
THE WOUND AND WATCH AND THE AGE
OF THE MOUSE IS WORSE THE
YOUNGER ONES.
THEN YOU CAN STARTED LOOKING AT
COMMUNICATIONS.
BASICALLY IF YOU LOOK AT THE
WOUND, THE COMPOSITION DIDN'T
CHANGE MUCH HOWEVER, IF YOU LOOK
AT THE TWO COMMUNICATIONS,
BASICALLY THESE ARE THE CELL
TYPES AND THE COMMUNICATIONS.
WHAT DID YOU OBSERVE?
THE CELL TALK A LOT.
THE OUTCOMINGS ARE GOOD.
SO WHY, WE NEED MORE FUNCTIONAL
STUDIES AND MANY OF THE OLD FOLK
I CONSIDER OLD NOW WE WORK HARD
AND IN PARTICULAR IN SPORTS WE
TRY TO DO A LOT BUT THE OUTCOME
IS JUST NOT AS GOOD.
ANOTHER APPLICATION IS TO DATA
TO DO THE SAME THING.
IT'S THE BCC AND THE CONTROL AND
HERE ARE THE COMMUNICATIONS.
OF COURSE IT'S NOT A SURPRISE
IT'S IMMUNE RELATED AND VERY
ACTIVE.
THAT'S ONE KIND OF YOU CAN DO IN
PARTICULAR TO COMPARE WITH A
PERTURBED DISEASE.
THERE'S MANY COVID RELATED
RESEARCH DURING THE PAST TWO
YEARS.
THEY USED THE TOOLS TO PLOT.
OKAY.
THIS IS A FAST EVOLVING AREA.
WE START THE BUSINESS IN 2019.
THIS RESULT IS IN THE
PRELIMINARY RESULT OF THE GRANT.
WE RECENTLY READ THE REVIEWS.
THIS IS NOT A TYPICAL RECEPTOR
THERE'S MORE MOLECULES.
THEN WE WORKED OUT SOMETHING
CALLED THE NEURON CHAT PUBLISHED
ABOUT TWO MONTHS AGO.
AND NOW LET'S LOOK AT THE
EMERGING BEHAVIOR BETWEEN CELLS.
WHEN WE FIRST START THE UL 1 WE
HAVE THE DATA, ONE GENE, TWO
GENES SPANNING DATA.
WE HAVE SINGLE CELL DATA AND A
LOT OF GENES BUT NO SPACE.
THERE'S BEAUTIFUL SPATIAL
INFORMATION.
CAN YOU MAP THEM?
YOU THIS HAS 25,000 GENES FOR
HUMANS AND THIS COMES FROM THE
SAME PIECE OF TISSUE IN
PRINCIPLE I CAN MATCH IT.
OF COURSE YOU DON'T HAVE ALL
25,000.
IF YOU HAVE 100, CAN YOU DO IT.
MATHEMATICALLY YOU CAN
DEFINITELY DO IT AND DO
SOMETHING YOU MENTIONED I DON'T
WANT TO GET IN THE DETAILS
SOMETHING ABOUT ULTIMATE
TRANSPORT.
ONE OF THE METHOD AND CAN
CONNECT THE DATA SET.
THERE'S SPATIAL IMAGES.
WE CAN MATCH IT.
WE CAN START THE CELL-CELL
COMMUNICATIONS IN THE MORE
SPATIALLY CONCENTRATED WAY.
OUR CELLS COMMUNICATION ARE
ALWAYS CONSTRAINED BY SPACE.
NOW, THE NEW TECHNOLOGY COMES
UP.
THAT'S CELLS ON THE PIECE OF THE
TISSUE AND ALL THE GENES.
SINGLE CELL RESOLUTION SOMETIMES
OR WITH THAT AND WE SAY OKAY,
LET'S ANALYZE THE DATA STATS IN
THE TRANSITIONS.
WE USE THE SAME TECHNIQUE
DISCUSSED IN THE FIRST PART OF
THE TALK, I DON'T WANT TO GET
INTO DETAILS.
LET'S USE BRAIN AS AN EXAMPLE.
THIS THE BRAIN DATA.
WE CAN CONSTRUCT THE ARROW.
THIS IS BASICALLY BORROWED ALL
THE VELOCITY AND ACCELERATION
CONCEPT TO GENE EXPRESSION
DOMAINS.
CAN YOU DO COMMUNICATION IN
SPACE DIRECTLY?
YOU CAN.
ONE WAY IS WE PUBLISHED AND YOU
USE LIGAND RECEPTORS AND USE
ULTIMATE TRANSPORT TO CONNECT
THEM.
HAVE TO MAKE SURE YOU CAN HAVE
TWO OR THREE LIGANDS WITH TWO
RECEPTORS AND WITH ALL THAT YOU
HAVE DEVELOPED NEW OPTIMAL
TRANSPORT AND WITH THAT THERE'S
A LOT OF ATTENTION AND WE CAN DO
A SPATIAL DATA MER FISH AND
OTHER TYPE.
DON'T WORRY, THERE'S NO COFFEE
OUTSIDE I CHECKED.
YOU CANNOT HAVE COFFEE UNTIL
TOMORROW.
>> NO, UPSTAIRS ONLY COOKIE.
I HAD THE FIRST PIECE ABOUT 20
LEFT, OKAY.
AND THIS IS THE METHOD WE
PUBLISHED THE FIRST TWO METHOD
AND SOME OF THOSE METHOD COMING
UP.
IN FIRST SALES OF COMMUNICATION
IN SPACE IS COMING.
OF COURSE CELLS OF COMMUNICATION
YOU DO ON PURPOSE FOR OUR SUPER
VISED LEARNING IT'S HOW YOU DO
IT.
YOU USE THE TOOLS EXCEPT FOR
IMAGE AT THE ONE SPOT.
YOU HAVE THREE NUMBERS.
THE THREE AND HERE IS ONE GENE
OF 25,000.
THE HIGH DIMENSION IMAGES AND
HOW TO ANALYZE THE PATTERNS.
WE USE THE DEEP NET WORK
APPROACH AND ITERATIVE LEARNINGS
AND TO CONSTRUCT THE PATTERNS.
THE PREVIOUS SESSION JUST TALKED
ABOUT THE LAST SLIDE AND YOU
HAVE SPACE FLOW AND YOU CAN LOOK
AT SPATIAL TEMPORAL MAP.
I WANT TO GET THE DEFINITION
WHAT IS THE SPATIAL TEMPORAL
MAP?
IN TUE TOUGHLY YOU SHOULD BE
ABLE TO SEE THIS.
THIS IS PUBLISHED SO THEN WHAT'S
THE BIOLOGICAL QUESTIONS OF THE
UL 1.
IT'S ABOUT WHY THE HAIR AT THE
EDGE FORM BUT NOT IN THE MIDDLE?
DOING THE PAST FIVE YEARS WE
COLLECTED LIKE CRAZY OF COURSE
WE ALSO DID EIGHT OF
COMPUTATIONS AND NOW WE'RE GOING
BACK TO DATA SETS WITH 1 MILLION
CELLS AND INDIVIDUAL GENES AND
MAKE THE MOUSE MUTANT GO CRAZY.
SO ONE OUTCOME OF THAT IS -- TWO
MINUTES.
WE HAVE A PAPER TO SHOW THE
SENESCE OF THE CELL, IT'S VERY
IMPORTANT TOO.
THE SENESCE OF CELLS NUMBERS
INCREASE WHEN IT GETS OLDER.
WHEN IT GETS OLDER THERE'S MORE
SENESCENT CELLS.
WE FOUND SENESCENCE OF CELLS
COULD STIMULATE SOME IMPACT STEM
CELL FOR RENEW.
GOOD NEWS WAS WE'RE BETTER THAN
SOME OF THE YOUNG FOLKS HERE.
SO WAIT, HOW DO YOU DO ALL THIS?
AT THE END IT'S EVERYTHING,
EVERYWHERE ALL TOGETHER.
ALL AT ONCE.
YOU WANT ALL THE CELLS.
YOU WANT ALL THE GENES.
PROTEINS TOO.
OKAY, WE DID MODELLING.
WE INCORPORATED AND WANT TO
PUBLISH THIS.
IT BECOMES IMPOSSIBLE.
EVERY TIME WE THINK OUR MODEL IS
READY WE FIND NEW GENES.
WE'RE LOOKING AT THE
INTERACTIONS.
THE OUTCOME.
WE PUBLISHED 15 PAPERS DURING
THE PAST FIVE YEARS.
AND ALMOST ALL ARE ON GITHUB AND
NOW IT'S TIME FOR HIGH
DIMENSION.
IT'S AGE FOR HIGH DIMENSIONAL
DATA.
AND IT CAN MAKE DATA GREAT
AGAIN.
WITHOUT HIGH DIMENSIONAL DATA
YOU CANNOT LOOK AT WITHOUT
MULTISCALE MODELLING AND I'D
LIKE TO THANK YOU AND MY
COLLABORATORS AND ALL WHO DID
ALL THE WORK.
>> THANK YOU.
EXCELLENT PRESENTATION.
WE'RE RUNNING A LITTLE OVER ON
TIME AND ENCOURAGE YOU TO FIND
ALL OUR SPEAKERS UPSTAIRS.
BEFORE WE GO UP FOR SNACKS,
THERE'S A PREMEETING EXERCISE ON
THE WIKI FOR A WORD CLOUD ABOUT
YOUR FAVORITE TYPE OF MODEL.
FILL THAT OUT SO HERBERT CAN
MAKE A WORD CLOUD SO WE CAN
CHECK IT OUT TOMORROW.
GREAT.
ANY OTHER ANNOUNCEMENTS BEFORE
WE ADJOURN?
AFTER SNACK IS THE POSTER
SESSION SO WE ENCOURAGE YOU TO
GO TALK TO THEM THERE.
AND MEET THE EARLY CAREER
INVESTIGATORS OR ECIs.
MAKE SURE YOU FIND THEIR POSTERS
AND CHAT WITH THEM.
AS YOU GO GET YOUR SNACKS WE
WANT TO THANK MATHEMATICAL
BIOLOGY FOR SNACKS AND THANK YOU
TO THE SPEAKERS AND PRESENTERS
TODAY.