QSB: A Principled Mapping of Regulatory Networks to Asynchronous Circuit Models for Stochastic Analysis

微阵列和其他新技术现在为我们提供了大量关于基因如何相互作用以执行复杂生物功能的数据。为了对遗传系统进行推理，必须采取系统生物学的观点，必须开发新的模型和有效的分析方法。电气工程师有丰富的经验建模和分析电子电路和系统。麦克亚当斯和夏皮罗在1995年发表在《科学》杂志上的一篇论文中，从电子电路的角度看待基因网络，并取得了令人鼓舞的结果。因此，正如在人类基因组测序中一样，工程师和系统生物学家之间的合作可能对功能基因组学的成功极为有益。这笔拨款将继续资助PI和加州大学伯克利分校亚当·阿金教授的动态基因组学实验室之间的新合作。PI使用随机异步电路模型对噬菌体病毒进行了建模。随机模型似乎是必不可少的，因为这种病毒采取的生存策略具有随机成分，这可能是该病毒和其他物种进化生存的关键。Arkin博士基于化学主方程和蒙特卡罗模拟的原始模型需要在超级计算机上运行大量时间，而新的随机异步电路模型在个人电脑上不到一分钟就能产生可比的结果。Phage_案例研究导致了从具有动力学速率和临界浓度的反应到随机异步电路模型的抽象方法的发展。在此抽象之后，有效的马尔可夫链分析方法可以应用于对系统行为的推理。这项工作的第一个主要目标是将这种方法应用于其他表现出随机行为的系统，如大肠杆菌膜系统或枯草芽孢杆菌应激反应网络。目标是一个完整的方法，有效地分析遗传调控网络和它的示范几个例子系统。在研究过程中开发的所有模型和工具将通过网络提供给其他机构的研究和教学。利用计算机对生物系统进行有效分析，有望帮助我们了解疾病的起因，并开发治疗疾病的药物。

Microarrays and other new technologies are now giving us vast amounts of data on how genes interact to perform complex biological functions. In order to reason about genetic systems, a systems biology perspective must be taken in which new models and efficient analysis methods must be developed. Electrical engineers have vast experience modeling and analyzing electronic circuits and systems. McAdams and Shapiro in their 1995 Science paper took an electronic circuit view of a genetic network with encouraging results. Therefore, as in the sequencing of the human genome, collaborations between engineers and systems biologists may be extremely beneficial to the success of functional genomics. This grant will fund continuation of a new collaboration between the PI and Professor Adam Arkin's Laboratory for Dynamical Genomics at UC Berkeley. The PI has modeled the Phage_ virus using a stochastic asynchronous circuit model. A stochastic model appears to be essential as the survival strategy taken by this virus has a random component which may be key in the evolutionary survival of this and other species. Dr. Arkin's original model based on the chemical master equation and Monte Carlo simulation required substantial runtime on a supercomputer while the new stochastic asynchronous circuit model produces comparable results in under a minute on a PC. The Phage_ case study has led to the development of an abstraction methodology from reactions with kinetic rates and critical concentrations to a stochastic asynchronous circuit model. After this abstraction, efficient Markov chain analysis methods can be applied to reason about the systems behavior. The first major goal of this work will be to apply this methodology to other systems that exhibit stochastic behavior, such as the E. Coli Fim system or the B. Subtilis stress response network. The goal is a complete methodology for the efficient analysis of genetic regulatory networks and its demonstration on several example systems. All models and tools developed in the course of this research will be made available via the web for research and teaching at other institutions. The efficient analysis of biological systems in silico has the promise of helping our understanding of the causes of disease and our development of drugs to treat them.