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

研究概要/摘要 我们的目标是破译一个分子水平的事件或属性如何在一个分子内产生异质行为。 人口，以及这种异质性如何导致人口作为一个整体的优势，是不可用的 个人会员。我们建议确定如何亚代基因表达-不仅是个人的 基因，而且整个操纵子含有多个基因与协调功能-创造混合 更适合对各种环境线索做出反应的种群。这一提案， 计算建模和实验测量，产生了我们的努力，在“全细胞”建模的E。 大肠杆菌，这是今年早些时候在《科学》杂志上报道的。急诊大肠杆菌模型预测了许多令人惊讶的 行为;最相关的发现是，E.大肠杆菌的转录速率是 每个细胞周期少于一次-我们称之为“亚代基因表达”的现象。此类表达 可能对单个细菌产生负面影响，但对整个细菌群体有益。 由于细菌无法可靠地预测未来的情况，因此细菌种群必须随时准备 但没有一种细菌能够表达所有的基因来应对任何环境变化。 在任何环境中都能达到足够的水平。相反，我们的工作假设是人口是异质的， 由个体成员组成，每个成员都为少数可能的环境做好了准备。因此，在本发明中， 虽然没有一个细胞能够应付所有的环境，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，殖民地由个体主导，通过亚代表达随机出现。 表达的基因，谁是最适合生存在任何给定的时刻。我们的团队联合收割机结合了 全细胞和基于代理的模型，并一直致力于全细胞群体模拟， 其中数百或数千个单元每个都运行E的实例化。coli模型。我们的目标是：（1） 证实模型预测的基因在亚代表达;（2）计算预测， 实验确定操纵子结构对功能相关基因的亚代表达的影响， 基因对;以及（3）计算预测和实验确定所产生的表型异质性 通过细胞群体中的操纵子分离。我们提案中最具影响力和开拓性的方面是， 我们将揭示操纵子结构在原核基因调控中的一个基本的新作用;我们将 产生一个扩展的全细胞模型，具有以前看不见的复杂性，以及高度创新的新 建模技术;最后，这项工作将是第一个利用新的多尺度仿真平台 它结合了全细胞模型和基于代理的模型，包括最令人兴奋的实验模型。 全细胞和全群体建模的主要潜力的演示：预测大规模的突发事件 这些特性可以帮助我们深入了解复杂的细胞行为。

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.