(562g) Combining Experimental Data with Sampling Know-How: Large-Scale Kinetic Modeling of Chlamydomonas Reinhardtii

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Genome-scale kinetic metabolic modeling is considered challenging and even infeasible because of poorly known kinetics and the presumed computational limitations associated with solving systems of nonlinear differential equations; we present an approach that constructs truly kinetic models using experimentally-derived information while taking cues from sampling-based methodologies (such as Metabolic Control Analysis and Ensemble Modeling). Recognizing that kinetic parameters are inherently uncertain (e.g. in vitro vs. in vivo enzyme behavior) and intracellular concentrations typically unknown, especially for less-characterized species, parameters are allowed to fluctuate randomly in order to create ensembles of kinetic models that are not at all dependent on flux-derived steady-states. Rather, the resulting model ensembles are allowed to develop to their respective steady-states, which represent the possible cell behaviors allowable within the bounds of known thermodynamics, the known spaces of parametrized enzyme kinetics, and cell biology. The ensembles solve without extreme time-burden. We applied this methodology to the model algae Chlamydomonas reinhardtii, of interest to groups working toward improved yields of biofuels and other value-added products. The model system comprised 4 organeller compartments and 469 reactions and incorporated known inhibition kinetics for highly regulated cycles (e.g. Calvin, TCA) and other control points. Ensembles comprised 500-1000 models, and reproduced known trends in energy states, uptake/evolution of extracellular species, and intracellular concentration shifts under nutrient deprivation and genomic edits. Subsequent studies focused genomic changes affecting the Calvin cycle enzymes toward increased biomass accumulation, per the interest of experimentalist collaborators.