Collaborative Research: Multistability in Biological Networks

描述（由申请人提供）：多稳定性是指系统在几个离散的、可选择的稳定稳态下运行的能力，是生物动力学最重要的特征之一。多稳定性现象是现代生物医学关注的焦点。从分子和细胞到组织、生物体、种群和生态系统，研究在所有尺度上都存在。为了理解其中一些相互作用所起的作用（例如基因调控网络或细胞中的信号通路的作用），在试图解释生物相互作用网络节点之间的正反馈和负反馈、非线性相互作用和其他复杂信号的影响时，人们往往面临很大的困难。我们建议发展新的理论，并显著扩展现有理论，以理解生物相互作用网络中的多稳定性，并确定生物相互作用网络产生多稳定行为的关键特征。此外，我们将创建软件来实现我们现有的各种多稳定性相关算法，以及任何新发现的方法，这些方法都是由这项工作产生的。为了指导和补充我们的理论工作，我们将研究我们的数学技术是否会导致可以实验验证的预测。这项工作的长期目标是将多重稳定性作为一个基本的理论概念来分析，它贯穿了生物复杂性的各个层面，并开发理论、计算和实验工具来理解具体生物相互作用网络中的多重稳定性。

DESCRIPTION (provided by applicant): Multistability refers to the capacity of a system to operate at several discrete, alternative stable steady-states, and is one of the most important features of biological dynamics. The phenomenon of multistability, which is the focus of much modern biomedical. research, is found at all scales, from the molecular and cellular, to tissues, organisms, populations, and ecosystems. In order to understand the role played by some of these interactions (for example the role of a gene regulatory network or a signaling pathway in a cell), one often faces great difficulties in trying to interpret the effect of positive and negative feedbacks, nonlinear interactions, and other complex signaling between the nodes of the biological interaction network. We propose to develop new theory and to significantly expand existing theory for understanding multistability in biological interaction networks, and identifying the key features of biological interaction networks that give rise to multistable behavior. Also, we will create software that implements our various existing multistability-related algorithms, and any newly discovered methods that result form this work. To guide and complement our theoretical work, we will investigate whether our mathematical techniques lead to predictions which can be validated experimentally. The long-term goal of the proposed work is to analyze multistability as a fundamental theoretical concept that traverses levels of biological complexity, and to develop theoretical, computational, and experimental tools to understand multistability in concrete biological interaction networks. Understanding multistability in gene regulatory networks and signaling pathways will play an important role in the study of key cellular processes deregulated during carcinogenesis. The analysis of multistability will also benefit research in cellular differentiation (relevant to tissue engineering), viral infections (e.g. HIV's dormant state), and the immune system. Our software tools and our experimental genetic systems will be made available to the biomedical research community.