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

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.