Skip Navigation


CIT can broadcast your seminar, conference or meeting live to a world-wide audience over the Internet as a real-time streaming video. The event can be recorded and made available for viewers to watch at their convenience as an on-demand video or a downloadable file. CIT can also broadcast NIH-only or HHS-only content.

Predicting the incidence of cancer: Does natural selection pick holographic networks?

Loading video...

169 Views  
   
Air date: Friday, March 11, 2016, 12:00:00 PM
Time displayed is Eastern Time, Washington DC Local
Views: Total views: 169, (43 Live, 126 On-demand)
Category: NIH Director's Seminars
Runtime: 01:16:15
Description: Director's Seminar Series

Maintenance of function in many tissues in adult animals requires controlled somatic stem cell replication. The vast majority of these replication events are uneventful, as there are multiple levels of quality control: DNA damage repair mechanisms, paracrine signaling and the extracellular matrix, the immune system, and likely others that have not been uncovered. Our work has modeled tissue development and homeostasis processes in a variety of tissues: adipose tissue, beta cells in the islets of Langerhans, the development of the overall endocrine pancreas, and liver regeneration after partial hepatectomy. We will discuss levels at which tissue homeostasis can be modeled with large-scale data collection, and some general theoretical features of networks that dynamically can maintain homeostasis. Turning then to the other end of the spectrum of cell replication, cancer, where cells have evaded quality control, we will survey models of cancer incidence, and propose a new dynamic model that predicts population cancer incidence for all types of sporadic cancers with few parameters. This model reveals a startling simplicity in the sum total effect of the multiple levels of cellular replication quality control in all cancer types. We attempt to understand the relative contributions of different repair mechanisms by modeling skin cancer incidence in Xeroderma Pigmentosum subjects.
Debug: Show Debug
NLM Title: Predicting the incidence of cancer : does natural selection pick holographic networks? / Vipul Periwal.
Author: Periwal, Vipul.
National Institutes of Health (U.S.),
Publisher:
Abstract: (CIT): Director's Seminar Series Maintenance of function in many tissues in adult animals requires controlled somatic stem cell replication. The vast majority of these replication events are uneventful, as there are multiple levels of quality control: DNA damage repair mechanisms, paracrine signaling and the extracellular matrix, the immune system, and likely others that have not been uncovered. Our work has modeled tissue development and homeostasis processes in a variety of tissues: adipose tissue, beta cells in the islets of Langerhans, the development of the overall endocrine pancreas, and liver regeneration after partial hepatectomy. We will discuss levels at which tissue homeostasis can be modeled with large-scale data collection, and some general theoretical features of networks that dynamically can maintain homeostasis. Turning then to the other end of the spectrum of cell replication, cancer, where cells have evaded quality control, we will survey models of cancer incidence, and propose a new dynamic model that predicts population cancer incidence for all types of sporadic cancers with few parameters. This model reveals a startling simplicity in the sum total effect of the multiple levels of cellular replication quality control in all cancer types. We attempt to understand the relative contributions of different repair mechanisms by modeling skin cancer incidence in Xeroderma Pigmentosum subjects.
Subjects: Genetic Predisposition to Disease
Homeostasis--genetics
Neoplasms--genetics
Publication Types: Lecture
Webcasts
Download: To download this event, select one of the available bitrates:
[64k]  [150k]  [240k]  [440k]  [740k]  [1040k]  [1240k]  [1440k]  [1840k]    How to download a Videocast
Caption Text: Download Caption File
NLM Classification: QZ 202
NLM ID: 101680580
CIT Live ID: 18678
Permanent link: https://videocast.nih.gov/launch.asp?19544