title: Noise and low-level dynamics can coordinate multicomponent bet hedging mechanisms.
abstract: To counter future uncertainty, cells can stochastically express stress response mechanisms to diversify their population and hedge against stress. This approach allows a small subset of the population to survive without the prohibitive cost of constantly expressing resistance machinery at the population level. However, expression of multiple genes in concert is often needed to ensure survival, requiring coordination of infrequent events across many downstream targets. This raises the question of how cells orchestrate the timing of multiple rare events without adding cost. To investigate this, we used a stochastic model to study regulation of downstream target genes by a transcription factor. We compared several upstream regulator profiles, including constant expression, pulsatile dynamics, and noisy expression. We found that pulsatile dynamics and noise are sufficient to coordinate expression of multiple downstream genes. Notably, this is true even when fluctuations in the upstream regulator are far below the dissociation constants of the regulated genes, as with infrequently activated genes. As an example, we simulated the dynamics of the multiple antibiotic resistance activator (MarA) and 40 diverse downstream genes it regulates, determining that low-level dynamics in MarA are sufficient to coordinate expression of resistance mechanisms. We also demonstrated that noise can play a similar coordinating role. Importantly, we found that these benefits are present without a corresponding increase in the population-level cost. Therefore, our model suggests that low-level dynamics or noise in a transcription factor can coordinate expression of multiple stress response mechanisms by engaging them simultaneously without adding to the overall cost.
authors.affiliation: Department of Computer Science, University of Vermont, Burlington, Vermont.
authors.forename: Javier
authors.initials: J
authors.lastname: Garcia-Bernardo
authors.affiliation: School of Engineering, University of Vermont, Burlington, Vermont. Electronic address: mjdunlop@uvm.edu.
authors.forename: Mary J
authors.initials: MJ
authors.lastname: Dunlop
mesh_terms: D003198:Computer Simulation; D004268:DNA-Binding Proteins / Q000378:metabolism; D004926:Escherichia coli / Q000502:physiology; D029968:Escherichia coli Proteins / Q000378:metabolism; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D013269:Stochastic Processes; D013312:Stress, Physiological / Q000502:physiology*; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D004268:DNA-Binding Proteins; D029968:Escherichia coli Proteins; C080002:MarA protein, E coli; D014157:Transcription Factors