Covert Lab

Stanford University

BIOMEDICAL ENGINEERING

Stanford · United States

NIH-funded
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NIH R01 · 2024

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…

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