ArticleProceedings of the National Academy of Sciences of the United States of America2013
Optimal control of gene expression for fast proteome adaptation to environmental change.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
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- Optimal control of ribosome population for gene expression under periodic nutrient intake.Journal of the Royal Society, Interface · 2024Article
- Emergent Lag Phase in Flux-Regulation Models of Bacterial Growth.Bulletin of mathematical biology · 2023Article
- Shaping bacterial gene expression by physiological and proteome allocation constraints.Nature reviews. Microbiology · 2023Review
- Computational Analysis of the Ligand-Binding Sites of the Molecular Chaperone OppA fromInternational journal of molecular sciences · 2023Article
- Optimal transcriptional regulation of dynamic bacterial responses to sudden drug exposures.Biophysical journal · 2022Article
- Molecular phenotyping approaches for the detection and monitoring of carbapenem-resistant Enterobacteriaceae by mass spectrometry.Journal of mass spectrometry and advances in the clinical lab · 2022Review
- Time-Optimal Adaptation in Metabolic Network Models.Frontiers in molecular biosciences · 2022Article
- High-Pressure-Induced Sublethal Injuries of Food Pathogens-Microscopic Assessment.Foods (Basel, Switzerland) · 2021Article
- A simple regulatory architecture allows learning the statistical structure of a changing environment.eLife · 2021Article
- LED control of gene expression in a nanobiosystem composed of metallic nanoparticles and a genetically modified E. coli strain.Journal of nanobiotechnology · 2021Article
- Coupled microbiome analyses highlights relative functional roles of bacteria in a bivalve hatchery.Environmental microbiome · 2021Article
- Morphological and physiological changes in Lentilactobacillus hilgardii cells after cold plasma treatment.Scientific reports · 2020Article
- Using optimal control to understand complex metabolic pathways.BMC bioinformatics · 2020Article
- Abiotic and past climatic conditions drive protein abundance variation among natural populations of the caddisfly Crunoecia irrorata.Scientific reports · 2020Article
- 3-Hydroxybutyrate Derived from Poly-3-Hydroxybutyrate Mobilization Alleviates Protein Aggregation in Heat-Stressed Herbaspirillum seropedicae SmR1.Applied and environmental microbiology · 2020Article
- Robot Cookies - Plant Cell Packs as an Automated High-Throughput Screening Platform Based on Transient Expression.Frontiers in bioengineering and biotechnology · 2020Article
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2 authors.
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Abstract
Bacterial populations growing in a changing world must adjust their proteome composition in response to alterations in the environment. Rapid proteome responses to growth medium changes are expected to increase the average growth rate and fitness value of these populations. Little is known about the dynamics of proteome change, e.g., whether bacteria use optimal strategies of gene expression for rapid proteome adjustments and if there are lower bounds to the time of proteome adaptation in response to growth medium changes. To begin answering these types of questions, we modeled growing bacteria as stoichiometrically coupled networks of metabolic pathways. These are balanced during steady-state growth in a constant environment but are initially unbalanced after rapid medium shifts due to a shortage of enzymes required at higher concentrations in the new environment. We identified an optimal strategy for rapid proteome adjustment in the absence of protein degradation and found a lower bound to the time of proteome adaptation after medium shifts. This minimal time is determined by the ratio between the Kullback-Leibler distance from the pre- to the postshift proteome and the postshift steady-state growth rate. The dynamics of optimally controlled proteome adaptation has a simple analytical solution. We used detailed numerical modeling to demonstrate that realistic bacterial control systems can emulate this optimal strategy for rapid proteome adaptation. Our results may provide a conceptual link between the physiology and population genetics of growing bacteria.
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