4 research outputs found
Modeling adaptive dynamics in microbial populations with applications to the evolution of cellular resource allocation trade-offs
Adaptive evolution is the process by which natural selection, acting on variation
within a population, promotes the survival of individuals that are more successful
at reproducing and contributing to future generations. Evolutionary processes in
microbes occur at the intersection of population genetics, natural selection, and
underlying mechanistic constraints, to give rise to the repertoire of adaptation
observed in nature. Understanding microbial adaptive evolution is of critical importance
for human health for example, through the emergence of pathogenicity
and antibiotic resistance. Moreover, the stability and function of natural and
artificial ecosystems is contingent on the evolving interactions between microbes,
and between microbes and the environment.
We present a modelling framework, based on the theory of adaptive dynamics, to
investigate how cellular resource allocation trade-offs affect the adaptation process.
We used resource-consumer theory, which explicitly models the interactions
between cells and their environment, together with matrix models of structured
populations, to implement phenotype-determined cellular strategies of resource
allocation between mutually exclusive processes. We then analyse the outcome of
competitions between different phenotypes across environmental and competitive
conditions.
We applied our methods to the evolution of strategies (phenotypes) for resource
allocation between two competing cellular process in microbial populations growing
in chemostat-like environments. We calculated the adaptively stable strategies
for several models and showed how state-structured population models can
be mapped to simpler chemostat models on invariant manifolds. We then extended
our analysis to the case where a limiting nutrient may be utilized using
two alternative metabolic pathways. We described how the total population fitness
of a metabolic strategy can be constructed from the individual decisions
of its constituent members. We developed numerical methods to simulate and
analyse general models of adaptive dynamics using principles from graph theory
and discrete Markov processes. The methods were used to explore the evolution
of nutrient use strategies for microbial populations growing on two and three
substitutable nutrients. We highlight the importance of the ancestral phenotype
in channelling the adaptation process, which, together with the choice of
the mutational kernel, influences the adaptively stable strategies and modes of
co-existence. In a related finding, we show how some phenotypes are adaptively
stable only in the presence of a competitor lineage that modifies the environment
in a manner that permits another phenotype to invade. Our methods also reveal
instances where historical contingency and chance have an important effect on
determining the stable nutrient use strategies. Finally, we demonstrate the existence
of adaptively stable periodic solutions whereby, under some conditions,
phenotype successions are cyclical.
Our work builds on the foundation of adaptive dynamics theory to provide a
general framework for analysing models of microbial adaptation. We focused on
understanding the implications of underlying constraints and cellular resource
allocation trade-offs in the context of adaptive evolution
Predicting metabolic adaptation from networks of mutational paths
The structure and dynamics of microbial communities reflect trade-offs in the ability to use different resources. Here, Josephides and Swain incorporate metabolic trade-offs into an eco-evolutionary model to predict networks of mutational paths and the evolutionary outcomes for microbial communities
Modeling the evolution of a classic genetic switch
Abstract
Background
The regulatory network underlying the yeast galactose-use pathway has emerged as a model system for the study of regulatory network evolution. Evidence has recently been provided for adaptive evolution in this network following a whole genome duplication event. An ancestral gene encoding a bi-functional galactokinase and co-inducer protein molecule has become subfunctionalized as paralogous genes (GAL1 and GAL3) in Saccharomyces cerevisiae, with most fitness gains being attributable to changes in cis- regulatory elements. However, the quantitative functional implications of the evolutionary changes in this regulatory network remain unexplored.
Results
We develop a modeling framework to examine the evolution of the GAL regulatory network. This enables us to translate molecular changes in the regulatory network to changes in quantitative network function. We computationally reconstruct an inferred ancestral version of the network and trace the evolutionary paths in the lineage leading to S. cerevisiae. We explore the evolutionary landscape of possible regulatory networks and find that the operation of intermediate networks leading to S. cerevisiae differs substantially depending on the order in which evolutionary changes accumulate; in particular, we systematically explore evolutionary paths and find that some network features cannot be optimized simultaneously.
Conclusions
We find that a computational modeling approach can be used to analyze the evolution of a well-studied regulatory network. Our results are consistent with several experimental studies of the evolutionary of the GAL regulatory network, including increased fitness in Saccharomyces due to duplication and adaptive regulatory divergence. The conceptual and computational tools that we have developed may be applicable in further studies of regulatory network evolution