Relationship between fitness and heterogeneity in exponentially growing microbial populations
| Autor: | Anna Paola Muntoni, Alfredo Braunstein, Andrea Pagnani, Daniele De Martino, Andrea De Martino |
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| Přispěvatelé: | European Commission |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Bacteria
Statistical Mechanics (cond-mat.stat-mech) Entropy Molecular Networks (q-bio.MN) Biophysics FOS: Physical sciences Disordered Systems and Neural Networks (cond-mat.dis-nn) Condensed Matter - Disordered Systems and Neural Networks Quantitative Biology - Quantitative Methods Phenotype Biological Physics (physics.bio-ph) FOS: Biological sciences Escherichia coli Quantitative Biology - Molecular Networks Physics - Biological Physics Metabolic Networks and Pathways Condensed Matter - Statistical Mechanics Quantitative Methods (q-bio.QM) |
| Popis: | Despite major environmental and genetic differences, microbial metabolic networks are known to generate consistent physiological outcomes across vastly different organisms. This remarkable robustness suggests that, at least in bacteria, metabolic activity may be guided by universal principles. The constrained optimization of evolutionarily motivated objective functions, such as the growth rate, has emerged as the key theoretical assumption for the study of bacterial metabolism. While conceptually and practically useful in many situations, the idea that certain functions are optimized is hard to validate in data. Moreover, it is not always clear how optimality can be reconciled with the high degree of single-cell variability observed in experiments within microbial populations. To shed light on these issues, we develop an inverse modeling framework that connects the fitness of a population of cells (represented by the mean single-cell growth rate) to the underlying metabolic variability through the maximum entropy inference of the distribution of metabolic phenotypes from data. While no clear objective function emerges, we find that, as the medium gets richer, the fitness and inferred variability for Escherichia coli populations follow and slowly approach the theoretically optimal bound defined by minimal reduction of variability at given fitness. These results suggest that bacterial metabolism may be crucially shaped by a population-level trade-off between growth and heterogeneity. A.B., A.D.M., A.P., and A.P.M. acknowledge financial support from Marie Skłodowska-Curie, grant agreement no. 734439(INFERNET). |
| Databáze: | OpenAIRE |
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