Models and Algorithms for Beta-Barrel Membrane Proteins and Stochastic Networks

β-桶膜蛋白和随机网络的模型和算法

• 批准号: 10395949
• 负责人: Jie Liang
• 金额: $ 46.55万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10395949
• 关键词: Address; Algorithms; Biological; Biotechnology; Buffers; Chemicals; Communicable Diseases; Computational algorithm; Computer Analysis; Computer Models; Critical Pathways; DNA; Detection; Development; Embryonic Development; Energy Metabolism; Engineering; Equation; Event; Exotoxins; Formulation; Foundations; Geometry; Gram-Negative Bacteria; Heterogeneity; Induction of Apoptosis; Knowledge; Lead; Membrane; Membrane Proteins; Methods; Mitochondria; Modeling; National Institute of General Medical Sciences; Phase; Phenotype; Probability; Process; Property; Proteins; RNA; Reaction; Research; Sampling; Structure; System; Techniques; Theoretical model; Time; Toy; base; beta barrel; cell behavior; combinatorial; computerized tools; design; interest; nanodevice; non-genetic; novel; predictive tools; simulation; single molecule; stem cell differentiation; theories; therapeutic target; toliprolol; tool

Summary Our project address fundamental problems of structures and mechanisms of protein molecules and their interaction networks. At the protein level, we will continue our NIGMS supported studies and focus on A) -Barrel Membrane Proteins (MPs). We will develop models and computational tools for predicting their structure, understanding their mechanism, and formulating design of novel MPs. At the network level, we will explore a new research direction. We will B) develop models and exact algorithms by solving the discrete chemical master equation (dCME) to compute exact probability landscape and discrete probability flux of networks for studying stochastic control of cellular behavior. Furthermore, we will examine how phenotype switching arise in networks, starting from commonly occurring network motifs and comprehensively characterize the universe of their multistabilities. In Project A), we will focus on MPs found in the outer membrane of gram-negative bacteria, eukaryotic mitochondria, and in exotoxins. MPs are involved in fundamental processes such as transport, translocation, energy metabolism, and apoptosis induction. They are also important therapeutic targets against infectious diseases. In addition, there are significant engineering interests in developing MPs as bionanopores for single molecule detection and other biotech applications. Despite recent progress, our knowledge of MPs is limited: only a few dozens of structures of non-homologous MPs are known. Importantly, we lack general understanding of the organizing principles of MPs and their functioning mechanism. We propose to develop models and algorithms for A1) predicting structures of MPs, A2) deciphering the mechanism of gating in OmpG, and A3) designing novel MPs with desired geometry and stability towards broad biotech applications. Our approach will be based on the reduced state model we developed, the MHIP empirical potential function we obtained through extensive combinatorial analysis, the (m)DiSGro loop structure prediction and sampling algorithms, with significant new development. In Project B), we will focus on the fundamental problem of constructing exact stochastic probability landscape of networks of interacting molecules. Many important biological reactions involve only a small copy number of molecules. Stochasticity arising from such low copy events as well as rare events are important for fundamental processes such as embryonic development, stem cell differentiation and nongenetic heterogeneity. While the discrete chemical master equation (dCME) provides a generate framework for understanding stochasticity in mesocopic systems, many foundational problems remain. Despite significant progress, the exact time- evolving probability landscapes for many networks of interests are computationally inaccessible, except for a few simple toy problems (e.g. those with <4 nodes). One has to rely on Gillespie simulation or approximation of Langevin/Fokker-Planck formulations, with errors largely unexamined. We propose to develop B1) theoretical model and tools for computing probability fluxes on discrete state space at arbitrary microstate and for arbitrary reactions, so passageways, transient states, and critical paths important for characterizing phenotypical switches can be identified. We will also carried out computational analysis to decipher B2) common mechanisms of stochastic switching, as well as comprehensive mapping of phase diagrams of emerging multistabilities in the most widely encountered common biological motifs. Our approach will be based on our recent algorithm and theoretical development of multi-finite buffer network structure analysis, the corner simplex optimal state enumeration algorithm, and the ACME method for exact computation of time-evolving probability landscape, as well as error-bound analysis based our quotient matrix and the technique of stochastic ordering.

概括 我们的项目解决蛋白质分子结构和机制的基本问题 以及它们的相互作用网络，我们将继续支持 NIGMS。 研究并重点关注 A) -桶膜蛋白 (MPs) 我们将开发模型和 用于预测其结构、理解其机制的计算工具，以及 在网络层面，我们将探索一项新的研究。 我们将 B) 通过解决离散化学问题来开发模型和精确算法。 主方程（dCME）计算精确的概率景观和离散概率 用于研究细胞行为的随机控制的网络通量此外，我们将 从常见的情况开始，研究网络中表型转换是如何发生的 网络主题并全面表征其多稳定性的宇宙。 在项目 A) 中，我们将重点关注革兰氏阴性菌外膜中发现的 MP， 真核线粒体和外毒素中的 MP 参与诸如此类的基本过程。 如运输、易位、能量代谢和细胞凋亡诱导。 此外，还有重要的传染病治疗靶点。 开发 MP 作为生物纳米孔用于单分子检测和 尽管最近取得了进展，但我们对 MP 的了解仍然有限：只有一个。 已知的非同源 MP 结构只有几十种，重要的是，我们缺乏一般性的结构。 了解MP 的组织原则及其运作机制。 建议开发模型和算法用于 A1) 预测 MPs 的结构，A2) 破译 OmpG 中的门控机制，以及 A3) 设计具有所需的新型 MP 广泛的生物技术应用的几何形状和稳定性。 我们的方法将基于我们开发的简化状态模型，即 MHIP 经验模型 我们通过广泛的组合分析获得了势函数，(m)DiSGro 循环 结构预测和采样算法，有重大新进展。 在项目 B) 中，我们将重点关注构造精确随机的基本问题 相互作用分子网络的概率图景 许多重要的生物反应。 仅涉及由此类低拷贝事件引起的少量分子的随机性。 以及罕见事件对于胚胎发育等基本过程很重要， 干细胞分化和非遗传异质性而离散化学大师。 方程（dCME）为理解介观随机性提供了一个生成框架 尽管取得了重大进展，但确切的时间仍然存在。 许多兴趣网络的不断演变的概率景观在计算上是无法访问的， 除了一些简单的玩具问题（例如节点数少于 4 个的问题）之外，我们必须依赖 Gillespie。 Langevin/Fokker-Planck 公式的模拟或近似，误差很大 我们建议开发 B1) 计算概率的理论模型和工具。 任意微观状态和任意反应的离散状态空间上的通量，所以 对于表征表型很重要的通道、瞬态和关键路径 我们还将进行计算分析来破译 B2) 随机切换的常见机制以及相位的综合映射 最广泛遇到的常见生物基序中出现的多重稳定性图。 我们的方法将基于我们最近的算法和多重有限的理论发展 缓冲网络结构分析，角单纯形最优状态枚举算法，以及 用于精确计算时间演化概率景观的 ACME 方法，以及 基于我们的商矩阵和随机排序技术的误差界限分析。