Modeling, Predicting, and Reprogramming Dynamic Cellular Networks

Duke University, Durham NC

Investigators

Abstract

Lingchong You 0625213 The modeling research interfaces with experimental studies of cellular networks controlling cell cycle entry, cell growth and progression - using the Myc/Rb/E2F pathway which is central to mammalian cancer biology. The research will: (a) integrate deterministic systems models with statistical models of intrinsic and experimental noise, coupled with stochastic models representing variation in model components at the level of individual cells; (b) develop and evaluate such modeling approaches in a range of dynamic biological pathway/network contexts; (c) develop Bayesian statistical methods of stochastic simulation and search to evaluate the information (and the limitations on information) about cellular level parameters and temporal evolution of unobservable levels of proteins and RNAs; (d) generate statistical and advanced computational methods for high-dimensional parameter estimation and multivariate latent process estimation in complex, highly non-linear coupled systems representing dynamic network interactions; (e) study responses at multiple levels (genes, proteins, pathways, cells) to the effects of knocking-out specific pathway components via genetic mutations and siRNA methods; (f) experimentally test such model predictions of responses by creating and evaluating a range of synthetic gene circuits;modules of the Myc/Rb/E2F pathway and, (g) develop computational tools to enable the broader use of general stochastic-dynamic models and simulators of networks that will underlie predictive studies of changes in network behavior under candidate rewiring interventions. This work has broad application in synthesizing bioengineering and statistical modeling approaches to complex systems analysis and will provide valuable training for students entering the biotechnology field.