Metabolic trade-offs and the maintenance of the fittest and the flattest.
ABSTRACT How is diversity maintained? Environmental heterogeneity is considered to be important, yet diversity in seemingly homogeneous environments is nonetheless observed. This, it is assumed, must either be owing to weak selection, mutational input or a fitness advantage to genotypes when rare. Here we demonstrate the possibility of a new general mechanism of stable diversity maintenance, one that stems from metabolic and physiological trade-offs. The model requires that such trade-offs translate into a fitness landscape in which the most fit has unfit near-mutational neighbours, and a lower fitness peak also exists that is more mutationally robust. The 'survival of the fittest' applies at low mutation rates, giving way to 'survival of the flattest' at high mutation rates. However, as a consequence of quasispecies-level negative frequency-dependent selection and differences in mutational robustness we observe a transition zone in which both fittest and flattest coexist. Although diversity maintenance is possible for simple organisms in simple environments, the more trade-offs there are, the wider the maintenance zone becomes. The principle may be applied to lineages within a species or species within a community, potentially explaining why competitive exclusion need not be observed in homogeneous environments. This principle predicts the enigmatic richness of metabolic strategies in clonal bacteria and questions the safety of lethal mutagenesis as an antimicrobial treatment.
SourceAvailable from: S. Lan Smith
[Show abstract] [Hide abstract]
ABSTRACT: Constraint-based modeling is largely used in computational studies of metabolism. We propose here a novel approach that aims to identify ensembles of flux distributions that comply with one or more target phenotype(s). The methodology has been tested on a small-scale model of yeast energy metabolism. The target phenotypes describe the differential pattern of ethanol production and O2 consumption observed in "Crabtree-positive" and "Crabtree-negative" yeasts in changing environment (i.e., when the upper limit of glucose uptake is varied). The ensembles were obtained either by selection among sampled flux distributions or by means of a search heuristic (genetic algorithm). The former approach provided indication about the probability to observe a given phenotype, but the resulting ensembles could not be unambiguously partitioned into "Crabtree-positive" and "Crabtree-negative" clusters. On the contrary well-separated clusters were obtained with the latter method. The cluster analysis further allowed identification of distinct groups within each target phenotype. The method may thus prove useful in characterizing the design principles underlying metabolic plasticity arising from evolving physio-pathological or developmental constraints.Natural Computing 09/2014; DOI:10.1007/s11047-014-9439-4 · 0.54 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: A growing body of literature documents the pressing need to develop soil biogeochemistry models that more accurately reflect contemporary understanding of soil processes and better capture soil carbon (C) responses to environmental perturbations. Models that explicitly represent microbial activity offer inroads to improve representations of soil biogeochemical processes, but have yet to consider relationships between litter quality, functional differences in microbial physiology, and the physical protection of microbial byproducts in forming stable soil organic matter (SOM). To address these limitations, we introduce the MIcrobialMIneral Carbon Stabilization (MIMICS) model, and evaluate it by comparing site-level soil C projections with observations from a long-term litter decomposition study and soil warming experiment. In MIMICS, the turnover of litter and SOM pools is governed by temperature-sensitive MichaelisMenten kinetics and the activity of two physiologically distinct microbial functional types. The production of microbial residues through microbial turnover provides inputs to SOM pools that are considered physically or chemically protected. Soil clay content determines the physical protection of SOM in different soil environments. MIMICS adequately simulates the mean rate of leaf litter decomposition observed at temperate and boreal forest sites, and captures observed effects of litter quality on decomposition rates. Moreover, MIMICS better captures the response of SOM pools to experimental warming, with rapid SOM losses but declining temperature sensitivity to long-term warming, compared with a more conventional model structure. MIMICS incorporates current microbial theory to explore the mechanisms by which litter C is converted to stable SOM, and to improve predictions of soil C responses to environmental change.Biogeosciences 01/2014; 11(14-14):3899-3917. DOI:10.5194/bg-11-3899-2014 · 3.75 Impact Factor