权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

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
项目类别：
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

项目摘要

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.

总结