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.

研究摘要/摘要我们的目标是破译分子级事件或财产如何在人口，这种异质性如何导致整个人口的优势给个别成员。我们建议确定亚代基因表达方式 - 不仅是个体的基因，以及包含具有协调功能的多个基因的整个歌剧 - 创造混合更适合响应各种环境线索的人群。该提议深入整合计算建模和实验测量是由于我们在E的“全细胞”建模中的努力而产生的。大肠杆菌，今年早些时候在科学上报道。大肠杆菌模型预测了许多惊喜行为；最相关的是发现大肠杆菌中明显的大部分基因以速度转录每个细胞周期少一次 - 我们称之为“亚代基因表达”的现象。这样的表达可能会对单个细菌产生负面影响，但会使细菌种群的整体受益。由于细菌无法可靠地预期未来的状况，因此人口必须始终做好准备对于任何环境变化 - 但没有单一细菌能够表达所有反应所需的基因到任何足够级别的环境。相反，我们的工作假设是人口是异质的，每个成员都准备好为少数可能的环境做好准备。那，虽然没有一个单元为所有环境准备好，但总体而言，大多数事件都准备了种群。因此，菌落由个体主导，通过亚代表的表达随机出现表达的基因，他们最适合在任何给定时刻生存。我们的小组在两者中结合了专业知识全细胞和基于代理的模型，一直在努力进行全细胞种群模拟，几百或数千个细胞每个都有大肠杆菌模型的实例化。我们的目标是：（1）确认模型预测的基因在亚期中表达；（2）计算预测和实验确定歌剧结构对功能相关的亚代表的影响基因对；（3）计算预测和实验确定产生的表型异质性通过细胞种群中的歌剧分离。我们提案中最有影响力和最先锋的方面是我们将揭示歌剧结构在原核基因调节中的基本新作用；我们会的产生了以前看不见的复杂性的扩展的全细胞模型，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全细胞和全彩色建模的主要潜力的演示：预测大规模新兴生成对复杂细胞行为的洞察力的特性。