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Fragile robustness: principles and practice
[Thesis]. Manchester, UK: The University of Manchester; 2012.
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Abstract
Selective robustness is a key feature of biochemical networks, conferring a fitnessbenefit to organisms living in dynamic environments. The (in-)sensitivity of a network toexternal perturbations results from the interaction between network dynamics, design, andenzyme kinetics. In this work, we focus on the subtle interplay between robustness andfragility. We describe a quantitative method for defining the fragility and robustness ofsystem fluxes and metabolite concentrations to perturbations in enzyme activity. We findthat for many mathematical models of metabolic pathways, the robustness is captured bya broad distribution of the robustness coefficients and demonstrate that, unlike fragility,robustness is not a conserved process.Using a combination of existing in silico models and novel sets of models, designedto allow specific network features of interest to be studied in isolation, we examine theeffect of various network properties on the robustness of such pathways. We discuss thequestion of how to measure, in a meaningful way, the robustness of a pathway as a whole,defining several summary metrics which, in combination, can be used to compare therobustness of different pathways. We show that networking increases robustness, but thatrobustness is affected differently by varying aspects of complexity. The effect of systemcontrol loops on robustness is analysed and we find that, in general, the addition of suchregulation increases pathway robustness.The evolution of flux robustness is also examined. We show that robustness in metabolicpathways is unlikely to simply be a by-product of selection for other pathway traits, highlightingseveral trade-offs that result from the evolution of robust systems.Finally, we extend our definition of robustness, defining robustness coefficients forcellular properties other than flux or metabolite concentration, and to perturbations otherthan changes in enzyme activity. Using the effect of benzoic acid on glycolysis as a casestudy, we show how such robustness coefficients can be used to give novel insights fromexperimental data.