Genome-scale Metabolic Reaction Modeling: a New Approach to Geomicrobial Kinetics

Monday, 15 December 2014
Shannon Elizabeth McKernan1, Benjamin Shapiro1 and Qusheng Jin2, (1)University of Oregon, Eugene, OR, United States, (2)University of Oregon, Department of Geological Sciences, Eugene, OR, United States
Geomicrobial rates, rates of microbial metabolism in natural environments, are a key parameter of theoretical and practical problems in geobiology and biogeochemistry. Both laboratory- and field-based approaches have been applied to study rates of geomicrobial processes. Laboratory-based approaches analyze geomicrobial kinetics by incubating environmental samples under controlled laboratory conditions. Field methods quantify geomicrobial rates by observing the progress of geomicrobial processes. To take advantage of recent development in biogeochemical modeling and genome-scale metabolic modeling, we suggest that geomicrobial rates can also be predicted by simulating metabolic reaction networks of microbes.

To predict geomicrobial rates, we developed a genome-scale metabolic model that describes enzyme reaction networks of microbial metabolism, and simulated the network model by accounting for the kinetics and thermodynamics of enzyme reactions. The model is simulated numerically to solve cellular enzyme abundance and hence metabolic rates under the constraints of cellular physiology. The new modeling approach differs from flux balance analysis of system biology in that it accounts for the thermodynamics and kinetics of enzymatic reactions. It builds on subcellular metabolic reaction networks, and hence also differs from classical biogeochemical reaction modeling.

We applied the new approach to Methanosarcina acetivorans, an anaerobic, marine methanogen capable of disproportionating acetate to carbon dioxide and methane. The input of the new model includes (1) enzyme reaction network of acetoclastic methanogenesis, and (2) representative geochemical conditions of freshwater sedimentary environments. The output of the simulation includes the proteomics, metabolomics, and energy and matter fluxes of M. acetivorans. Our simulation results demonstrate the predictive power of the new modeling approach. Specifically, the results illustrate how methanogenesis rates vary with acetate concentrations and the energy available in the environment, and how M. acetivorans regulate the enzymes of methanogenesis under different biogeochemical conditions.