Collaborative Research: Multistability in Biological Networks

描述（由申请人提供）：多稳态是指系统在几个离散的、交替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。多稳态现象是现代生物医学研究的热点。研究，发现在所有规模，从分子和细胞，组织，有机体，人口和生态系统。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信号通路的作用），人们往往面临着巨大的困难，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。我们建议开发新的理论，并显着扩展现有的理论，以了解在生物相互作用网络的多稳态，并确定生物相互作用网络的关键特征，引起多稳态行为。此外，我们将创建实现我们现有的各种多稳态相关算法的软件，以及任何新发现的方法，从而形成这项工作。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通过实验验证的预测。 拟议工作的长期目标是分析多稳定性作为一个基本的理论概念，贯穿生物复杂性的水平，并开发理论，计算和实验工具，以了解在具体的生物相互作用网络的多稳定性。 了解基因调控网络和信号通路的多稳定性将在研究肿瘤发生过程中的关键细胞过程中发挥重要作用。对多稳态的分析也将有利于细胞分化（与组织工程相关）、病毒感染（例如HIV的休眠状态）和免疫系统的研究。我们的软件工具和实验遗传系统将提供给生物医学研究界。

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.