Article ; Online: Optimal metabolic strategies for microbial growth in stationary random environments.
2023 Volume 20, Issue 3
Abstract: In order to grow in any given environment, bacteria need to collect information about the medium composition and implement suitable growth strategies by adjusting their regulatory and metabolic degrees of freedom. In the standard sense, optimal strategy ... ...
| Abstract | In order to grow in any given environment, bacteria need to collect information about the medium composition and implement suitable growth strategies by adjusting their regulatory and metabolic degrees of freedom. In the standard sense, optimal strategy selection is achieved when bacteria grow at the fastest rate possible in that medium. While this view of optimality is well suited for cells that have perfect knowledge about their surroundings (e.g. nutrient levels), things are more involved in uncertain or fluctuating conditions, especially when changes occur over timescales comparable to (or faster than) those required to organize a response. Information theory however provides recipes for how cells can choose the optimal growth strategy under uncertainty about the stress levels they will face. Here we analyse the theoretically optimal scenarios for a coarse-grained, experiment-inspired model of bacterial metabolism for growth in a medium described by the (static) probability density of a single variable (the 'stress level'). We show that heterogeneity in growth rates consistently emerges as the optimal response when the environment is sufficiently complex and/or when perfect adjustment of metabolic degrees of freedom is not possible (e.g. due to limited resources). In addition, outcomes close to those achievable with unlimited resources are often attained effectively with a modest amount of fine tuning. In other terms, heterogeneous population structures in complex media may be rather robust with respect to the resources available to probe the environment and adjust reaction rates. |
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| MeSH term(s) | Bacteria ; Models, Theoretical |
| Language | English |
| Publishing date | 2023-03-21 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 2133216-2 |
| ISSN | 1478-3975 ; 1478-3967 |
| ISSN (online) | 1478-3975 |
| ISSN | 1478-3967 |
| DOI | 10.1088/1478-3975/acc1bc |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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