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Updated: Jun 11, 2026

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
Published on: August 18, 2023
Evolution of a single gene highlights the complexity underlying molecular descriptions of fitness
Matthew I Peña1, Elizabeth Van Itallie, Matthew R Bennett
1Department of Biochemistry and Cell Biology, Rice University, 6100 Main St., MS-140, Houston, Texas 77005, USA.
Abstract:
Evolution by natural selection is the driving force behind the endless variation we see in nature, yet our understanding of how changes at the molecular level give rise to different phenotypes and altered fitness at the population level remains inadequate. The reproductive fitness of an organism is the most basic metric that describes the chance that an organism will succeed or fail in its environment and it depends upon a complex network of inter- and intramolecular interactions. A deeper understanding of the quantitative relationships relating molecular evolution to adaptation, and consequently fitness, can guide our understanding of important issues in biomedicine such as drug resistance and the engineering of new organisms with applications to biotechnology. We have developed the "weak link" approach to determine how changes in molecular structure and function can relate to fitness and evolutionary outcomes. By replacing adenylate kinase (AK), an essential gene, in a thermophile with a homologous AK from a mesophile we have created a maladapted weak link that produces a temperature-sensitive phenotype. The recombinant strain adapts to nonpermissive temperatures through point mutations to the weak link that increase both stability and activity of the enzyme AK at higher temperatures. Here, we propose a fitness function relating enzyme activity to growth rate and use it to create a dynamic model of a population of bacterial cells. Using metabolic control analysis we show that the growth rate exhibits thresholdlike behavior, saturating at high enzyme activity as other reactions in the energy metabolism pathway become rate limiting. The dynamic model accurately recapitulates observed evolutionary outcomes. These findings suggest that in vitro enzyme kinetic data, in combination with metabolic network analysis, can be used to create fitness functions and dynamic models of evolution within simple metabolic systems.
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