Integrative Thermodynamics Strategies in Microbial Metabolism
Ebenhoeh, O.; Bekker, M.
Show abstract
Microbial metabolism is intricately governed by thermodynamic constraints that dictate energetic efficiency, growth dynamics, and metabolic pathway selection. Previous research has primarily examined these principles under carbon-limited conditions, demonstrating how microbes optimize their proteomic resources to balance metabolic efficiency and growth rates. This study extends this thermodynamic framework to explore microbial metabolism under various non-carbon nutrient limitations (e.g., nitrogen, phosphorus, sulfur). By integrating literature data from a range of species it is shown that growth under anabolic nutrient limitations consistently results in more negative Gibbs free energy ({Delta}G) values for the Net Catabolic Reaction (NCR), when normalized per unit of biomass formed, compared to carbon-limited scenarios. The findings suggest three, potentially complementary hypotheses: (1) Proteome Allocation Hypothesis: microbes favor faster enzymes to reduce proteome fraction used for catabolism, thus freeing proteome resources for additional nutrient transporters; (2) Coupled Transport Contribution Hypothesis: The more negative {Delta}G of the NCR may in part stem from the increased reliance on ATP-coupled or energetically driven transport mechanisms for nutrient uptake under limitation; (3) Bioenergetic Efficiency Hypothesis: microbes prefer pathways with more negative {Delta}G to enhance cellular energy status, such as membrane potentials or ATP/ADP ratio, to support nutrient uptake under anabolic limitations. This integrative thermodynamic analysis broadens the understanding of microbial adaptation strategies and provides valuable insights for biotechnological applications in metabolic engineering and fermentation process optimization.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth 96%
- Enhanced production of heterologous proteins by a synthetic microbial community: Conditions and trade-offs 96%
- Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding 96%
Similar papers in this journal
- A study of a diauxic growth experiment using an expanded dynamic flux balance framework 96%
- Genome-scale metabolic model of the diatom Thalassiosira pseudonana highlights the importance of nitrogen and sulfur metabolism in redox balance 94%
- Understanding biochemical design principles with ensembles of canonical non-linear models 93%
Similar papers in this journal
- Unveiling abundance-dependent metabolic phenotypes of microbial communities 95%
- Kinetics-based Inference of Environment-Dependent Microbial Interactions and Their Dynamic Variation 95%
- Reconstruction and analysis of thermodynamically-constrained models reveal metabolic responses of a deep-sea bacterium to temperature perturbations 94%
Similar papers in this journal
- Coupling Flux Balance Analysis with Reactive Transport Modeling through Machine Learning for Rapid and Stable Simulation of Microbial Metabolic Switching 94%
- MultIscale MultiObjective Systems Analysis (MIMOSA): an advanced metabolic modeling framework for complex systems 93%
- Pseudomonas aeruginosa reverse diauxie is an optimized, resource utilization strategy 93%
Similar papers in this journal
- PAOX1 expression in mixed-substrate continuous cultures of Komagataella phaffii (Pichia pastoris) is completely determined by methanol consumption regardless of the secondary carbon source. 95%
- Effective Biophysical Modeling of Cell Free Transcription and Translation Processes 93%
- Enhancing CO2-valorization using Clostridium autoethanogenum for sustainable fuel and chemicals production 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.