Trade-offs between the instantaneous growth rate and long-term fitness: Consequences for microbial physiology and predictive computational models

Microbial systems biology has made enormous advances in relating microbial physiology to the underlying biochemistry and molecular biology. By meticulously studying model microorganisms, in particular Escherichia coli and Saccharomyces cerevisiae, increasingly comprehensive computational models predict metabolic fluxes, protein expression, and growth. The modeling rationale is that cells are constrained by a limited pool of resources that they allocate optimally to maximize fitness. As a consequence, the expression of particular proteins is at the expense of others, causing trade-offs between cellular objectives such as instantaneous growth, stress tolerance, and capacity to adapt to new environments. While current computational models are remarkably predictive for E. coli and S. cerevisiae when grown in laboratory environments, this may not hold for other growth conditions and other microorganisms. In this contribution, we therefore discuss the relationship between the instantaneous growth rate, limited resources, and long-term fitness. We discuss uses and limitations of current computational models, in particular for rapidly changing and adverse environments, and propose to classify microbial growth strategies based on Grimes's CSR framework.

Automated summary

Microbial systems biology has made significant progress in understanding the relationship between microbial physiology and biochemistry. By studying model microorganisms, computational models have been developed to predict metabolic activities, protein expression, and growth. However, these models may not be applicable to different growth conditions and microorganisms. This article discusses the relationship between growth rate, limited resources, and long-term fitness, as well as the limitations of current computational models, particularly in rapidly changing and adverse environments. The authors propose a classification of microbial growth strategies based on Grimes's CSR framework.