Multi-scale, model-driven exploration of sub-generational gene expression in bacteria: individual consequences, population benefits

细菌亚代基因表达的多尺度、模型驱动探索：个体后果、群体效益

基本信息

批准号：
10654847
负责人：
Markus W Covert
金额：
$ 54.84万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-22 至 2025-06-30
项目状态：
未结题

项目摘要

Research Summary/Abstract Our goal is to decipher how a molecular-level event or property can create heterogeneous behavior within a population, and how this heterogeneity leads to advantages for the population as a whole that are not available to individual members. We propose to determine how sub-generational gene expression - not only of individual genes, but also of entire operons containing multiple genes with coordinated functions - creates mixed populations that are more fit to respond to various environmental cues. This proposal, which deeply integrates computational modeling and experimental measurement, arose out of our efforts in “whole-cell” modeling of E. coli, which were reported in Science earlier this year. The E. coli model has predicted a number of surprising behaviors; most relevant is the finding that a clear majority of the genes in E. coli are transcribed at a rate of less than once per cell cycle - a phenomenon we call “sub-generational gene expression”. Such expression can have negative consequences for individual bacteria, but benefits the bacterial population as a whole. Because bacteria are unable to reliably anticipate future conditions, the population must always be prepared for any environmental change - but no single bacterium is able to express all of the genes required to respond to any environment at sufficient levels. Instead, our working hypothesis is that the population is heterogeneous, comprised of individual members who are each prepared for a small number of possible environments. Thus, while no single cell is ready for all environments, as a whole the population is prepared for most eventualities. The colony is thus dominated by individuals, emerging stochastically via expression of sub-generationally expressed genes, who are the most fit to survive at any given moment. Our groups combine expertise in both whole-cell and agent-based models, and have been working towards whole-cell population simulations, in which hundreds or thousands of cells each run an instantiation of the E. coli model. Our Aims are to: (1) confirm that model-predicted genes are expressed sub-generationally; (2) computationally predict and experimentally determine the effect of operon structure on sub-generational expression of functionally related gene pairs; and (3) computationally predict and experimentally determine the phenotypic heterogeneity created by operon separation in cell populations. The most impactful and pioneering aspects of our proposal are that we will uncover a fundamental new role for operon structure in prokaryotic gene regulation; that we will produce an expanded whole-cell model of previously unseen complexity, as well as highly innovative new modeling technology; and finally, that this work will be the first to utilize a novel multi-scale simulation platform that combines whole-cell models with agent-based models, including the most exciting experimental demonstration of whole-cell and whole-colony modeling’s major potential: predicting large-scale emergent properties to generate insights into complex cellular behaviors.

研究总结/摘要我们的目标是破译分子级事件或属性如何在人口，以及这种异质性如何为整个人口带来无法获得的优势我们建议确定亚代基因的表达方式——而不仅仅是个体。基因，而且还包含具有协调功能的多个基因的整个操纵子 - 创建混合该提案深度整合了更适合应对各种环境因素的人群。计算建模和实验测量源于我们对大肠杆菌“全细胞”建模的努力。今年早些时候《科学》杂志报道了大肠杆菌模型，它预测了一些令人惊讶的结果。行为；最相关的是发现大肠杆菌中绝大多数基因的转录速度为每个细胞周期少于一次——这种现象我们称之为“亚代基因表达”。可能对单个细菌产生负面影响，但对整个细菌群体有利。由于细菌无法可靠地预测未来的情况，因此人们必须始终做好准备对于任何环境变化 - 但没有任何一种细菌能够表达响应所需的所有基因相反，我们的工作假设是人口是异质的，由各个成员组成，每个成员都为少数可能的环境做好了准备。虽然没有一个细胞能够应对所有环境，但作为一个整体，整个群体已经为大多数可能发生的情况做好了准备。因此，群体由个体主导，通过亚代的表达随机出现。我们的团队结合了两者的专业知识，表达了在任何特定时刻最适合生存的基因。全细胞和基于代理的模型，并一直致力于全细胞群体模拟其中数百或数千个细胞各自运行大肠杆菌模型的实例，我们的目标是：(1) 确认模型预测的基因在子代中表达；（2）通过计算预测和实验确定操纵子结构对功能相关的亚代表达的影响基因对；以及（3）通过计算预测和实验确定所产生的表型异质性我们的提议中最具影响力和开创性的方面是：我们将揭示操纵子结构在原核基因调控中的基本新作用；产生前所未见的复杂性的扩展全细胞模型，以及高度创新的新模型建模技术；最后，这项工作将是第一个利用新颖的多尺度模拟平台的工作将全细胞模型与基于代理的模型相结合，包括最令人兴奋的实验展示全细胞和全集落建模的主要潜力：预测大规模突发事件属性来深入了解复杂的细胞行为。