Modeling, Predicting, and Reprogramming Dynamic Cellular Networks

Lingchong You 0625213建模研究与控制细胞周期进入，细胞生长和进展的细胞网络的实验研究相结合-使用Myc/Rb/E2 F通路，这是哺乳动物癌症生物学的核心。该研究将：（a）将确定性系统模型与内在和实验噪声的统计模型结合起来，再加上代表单个细胞水平上模型组分变化的随机模型;（B）在一系列动态生物途径/网络背景下开发和评估这种建模方法;（c）发展随机模拟及搜寻的贝氏统计方法，以评估有关资料关于细胞水平参数和蛋白质和RNA的不可观察水平的时间演变（以及信息的局限性）;（d）产生用于复杂情况下的高维参数估计和多变量潜在过程估计的统计和高级计算方法，代表动态网络相互作用的高度非线性耦合系统;（e）研究多个层面的反应（f）通过创建和评估一系列合成基因回路，实验性地测试这种响应的模型预测; Myc/Rb/E2 F途径的模块，（g）开发计算工具，以便更广泛地使用一般随机-网络的动态模型和模拟器，这些模型和模拟器将成为预测研究在候选重新布线干预下网络行为变化的基础。这项工作在综合生物工程和统计建模方法以进行复杂系统分析方面具有广泛的应用，并将为进入生物技术的学生提供有价值的培训。领域

Lingchong You0625213The 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.