Modeling, Predicting, and Reprogramming Dynamic Cellular Networks

灵冲YO0625213模型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-使用对哺乳动物癌症生物学至关重要的Myc/Rb/E2F途径。这项研究将：(A)将确定性系统模型与固有噪声和实验噪声的统计模型相结合，再加上在单个细胞水平上表示模型分量变化的随机模型；(B)在一系列动态生物路径/网络环境中开发和评估这种建模方法；(C)发展随机模拟和搜索的贝叶斯统计方法，以评估关于细胞水平参数和不可观测的蛋白质和RNA水平的时间演化的信息(以及信息的限制)；(D)为代表动态网络相互作用的复杂、高度非线性耦合系统的高维参数估计和多变量潜在过程估计产生统计和高级计算方法；(E)研究通过基因突变和siRNA方法在多个层面(基因、蛋白质、途径、细胞)对敲除特定途径成分的影响的反应；(F)通过建立和评估一系列合成基因电路，对这种反应的模型预测进行实验测试；Myc/Rb/E2F途径的模块和(G)开发计算工具，以使网络的一般随机动态模型和模拟器能够更广泛地使用，这些模型和模拟器将作为候选重新布线干预下网络行为变化的预测研究的基础。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领域的学生提供有价值的培训。

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.