Stochastic models for intracellular networks

随着许多基因组计划的完成，人们对理解基因组内编码的分子如何相互作用以定义细胞的各种功能网络越来越感兴趣。这些细胞内网络可分为基因调控网络、蛋白质相互作用网络或代谢网络。这些网络旨在描述基因组与其环境之间的全部反应或分子相互作用，最终定义生物体的生长和发育。在细胞水平上，化学动力学很可能是由每个细胞只有几个拷贝的调节分子的作用所控制的。因此，这些组分的分子波动可能决定反应网络的动力学。这些分子波动似乎具有显著的后果，观察到的细胞中分子物种的发育速度、形态和浓度的巨大变化往往导致表型结果的随机化和非遗传群体异质性。由于这些波动可能对细胞的生理产生深远的影响，因此有必要建立细胞内反应网络的随机模型并进行统计分析。这种整合分子反应的网络往往涉及许多不同的分子种类，因此提出了一个复杂的分析问题。为了预测和模拟的目的，必须减少问题的模型和计算复杂性，同时仍然捕获网络的所有基本特征和潜在行为。我的研究计划的主要目标是为这种细胞内网络的随机模型开发模型简化技术。

With the completion of numerous genome projects there is an increasing interest in understanding how molecules encoded within the genomes interact to define various functional networks of the cell. These intra-cellular networks may be classified as  gene regulatory networks, protein interaction networks, or metabolic networks. Together these networks aim to describe the full set of reactions or molecular interactions between a genome and its environment, ultimately defining the growth and development of an organism.At the level of the cell, the chemical dynamics may well be dominated by the action of regulatory molecules that are present at levels of only a few copies per cell. Therefore, the molecular fluctuations of these components may determine the dynamics of the reaction network. These molecular fluctuations appear to have significant consequences, the observed large variation in rates of development, morphology and concentration of molecular species in a cell often lead to a randomization of phenotypic outcomes and non-genetic population heterogeneity. Since these fluctuations may have profound effects on the physiology of the cell, a stochastic model for the intra-cellular reaction networks and a statistical analysis appear to be necessary.Such networks of integrated molecular reactions tend to involve  many different molecular species, thus posing a complex analytical problem. For prediction and simulation purposes it is essential to reduce both the model and computational complexity of the problem, while still capturing all of the essential characteristics and potential behavior of the network. The primary goal of my research program is to develop model reduction techniques for stochastic models for such intra-cellular networks.