CAREER:Stochastic Modeling, Analysis and Simulation of Gene Networks

后基因组研究的一个中心主题是对基因调控中复杂的动态相互作用的剖析和理解。最近的技术进步使生物学家能够在全基因组尺度上研究单个细胞中的基因表达和调控，这揭示了基因表达的信使核糖核酸和蛋白质的数量是一个随机过程。虽然这些实验研究为基因调控提供了有价值的见解，但基于随机方法的计算方法对于理解动态的基因相互作用是必不可少的，因为从实验结果的直观解释不能完全揭示基因网络的动态。然而，目前的随机方法没有提供完善的建模技术、分析方法和高效的仿真算法。这项研究采用了一种基于模块的方法来建模大型基因网络，类似于分析电子电路所使用的方法，旨在开发用于分析和模拟基因网络的计算工具。研究人员开展了以下三个方面的研究工作：1)根据实验结果推导出基因表达的一般随机模型和估计模型参数的统计方法；2)刻画基因网络中的一些常见模块，并提出一种基于模块的基因网络分析方法；3)设计高效的大型基因网络的随机模拟方法。这个项目中采用的计算方法可能会对生物学研究产生重大影响，因为它将阐明与基因调控中的随机性有关的许多悬而未决的问题。在这个项目中开发的计算工具将在与药物和合成基因电路设计相关的生物学研究以及癌症等疾病的调查中得到大量应用。本研究提出的方法也可用于模拟通信网络，设计或优化网络协议。

A central theme in post-genomic research is the dissection and understanding of the complex dynamical interaction involved in gene regulation. Recent advances in technology have enabled biologists to investigate gene expression and regulation in single cells on a whole-genome scale, which reveals that the amount of mRNA and protein expressed from a gene is a stochastic process. While such experimental studies have provided valuable insights on gene regulation, a computational approach based on stochastic methods is essential to understanding dynamic gene interaction, since intuitive interpretations from experimental results cannot fully expose the dynamics of a gene network. However, the current stochastic approach does not provide sophisticated modeling techniques, analytical methods and efficient simulation algorithms. This research employs a module-based approach to modeling large gene networks, similar to that used in analyzing electronic circuits, and aims to develop computational tools for analyzing and simulating gene networks. The investigator pursues the following three research thrusts: 1) derive a general stochastic model for gene expression and statistical methods for estimating model parameters based on experimental results, 2) characterize some common modules in gene networks and develop a module-based approach to analyzing gene networks, and 3) devise efficient stochastic simulation methods for large gene networks. The computational approach employed in this project will potentially have major impact on biological research, since it will shed light on many unanswered questions related to the stochasticity in gene regulation. The computational tools developed in this project will find numerous applications in biological research related to the design of drugs and synthetic gene circuits, as well as the investigation of diseases, such as cancer. The methods devised in this research can also be employed to simulate communication networks and design or optimize network protocols.