Reconstruction of Genome-Scale Metabolic Models with Concomitant Constraint-Based Modelling for Flux Prediction – a Case Study of Syngas Consuming Hydrogenophaga pseudoflava

Abstract: Metabolic modelling coupled with flux-balance analysis (FBA) has become a popular tool in systems biology for quantitative predictions of metabolic processes in silico, and as an aid in metabolic engineering. Drawing upon gene-protein-reaction associations deducible from information on the genome-level, so-called genome-scale metabolic models (GEMs) are unequalled in their scope as they attempt to encapsulate the entire reactome of a species or cell type. GEMs are conceived through a process of metabolic network reconstruction, the methodology of which was investigated, summarized, and distilled into distinct chronological steps. To substantiate these findings, and as a proof of concept, a case study was performed with the objective to reconstruct and curate a draft GEM of Hydrogenophaga pseudoflava strain DSM 1084. Ultimately, the purpose prompting acquisition of such a GEM is to predict and evaluate the biocapabilities of this bacterium in silico, particularly for syngas fermentation, when grown in lithoautotrophic (on CO2 + H2) and carboxydotrophic (on CO alone) conditions. Exploiting the KEGG database using the MATLAB toolbox RAVEN allowed for network reconstruction. Subsequent manual curation set out to have the model accommodate the wide heterotrophic substrate range exhibited by H. pseudoflava, correct reaction directionalities and add an artificial biomass reaction. These efforts eventually culminated in the first ever reported GEM of H. pseudoflava, HPseGEM, consisting of 1537 reactions, 1679 metabolites, and 915 genes.