Collaborative Research: CDI-Type II: Advanced Theory and Computational Methods for Modular Analysis and Design of Complex Gene Networks

本研究的目的是发展复杂基因网络的模块化分解和分析的理论和先进的计算工具。 要克服的关键挑战是，生物网络是高维耦合随机非线性系统的不确定组件。 该方法是利用计算和实验系统生物学、随机动态非线性网络和控制理论的工具和专门知识，将网络系统地分解为简单的模块，这些模块的动态特性可以被孤立地理解，然后与网络的行为联系起来。（B）确定每个模块的基本特征，以说明其在网络行为中的作用;（c）为模块网络的分析和综合构建提供分析和计算基础。 系统的方法明确说明了网络动态，随机性和生物网络的不确定性。 该框架的开发是通过精心挑选的生物实验进行指导和验证的。这项研究有可能提供新的工具，帮助科学家对复杂的生物网络进行逆向工程，从而更深入地了解生物功能。 这种理解是合理设计治疗方法的关键一步。 研究和教育活动紧密结合，培养了一批善于在多学科研究中运用计算思维的科学家和工程师。 通过暑期实习、校园项目和特殊的机构伙伴关系，从代表性不足的群体中招募妇女和学生，是调查人员实现多样性战略的核心。

The objective of this research is to develop the theory and advanced computational tools for a modular decomposition and analysis of complex gene networks. The key challenge to overcome is that biological networks are high-dimensional coupled stochastic nonlinear systems with uncertain components. The approach is to employ tools and expertise from computational and experimental systems biology, stochastic dynamic nonlinear networks, and control theory to systematically decompose the network into simple modules whose dynamic properties can be understood in isolation and then related to the behavior of the network.The research has the potential to provide a framework that (a) enables the decomposition of complex biological networks into modules; (b) identifies the essential characteristics of each module necessary to account for its role in the network's behavior; and (c) provides the analytical and computational foundation for the analysis and synthetic construction of networks of modules. The systematic approach explicitly accounts for network dynamics, the stochastic nature, and uncertainty of biological networks. Development of the framework is guided by and validated through carefully selected biological experiments.The research has the potential to provide new tools to help scientists reverse-engineer complex biological networks, leading to a deeper understanding of biological function. Such understanding is a key step in the rational design of therapies. Research and educational activities are tightly integrated to train a diverse cadre of scientists and engineers who are adept at employing computational thinking in multi-disciplinary research. Recruitment of women and students from under-represented groups through summer internships, campus programs, and special institutional partnerships is central to the investigators' strategy for achieving diversity.