The issue of genome minimization for a living cell has been the subject of intense study, and raises important theoretical questions and practical opportunities. We have developed an in-silico methodology, based on genome-scale models, to minimize the number of active genes encoding enzymes and transporters in a cell’s metabolic network. In the resultant minimal metabolic networks, all the remaining active genes are essential for keeping the network working to achieve the biomass value predicted by Flux Balance Analysis. We have tested our approach on a set of genome-scale metabolic models of various eukaryotic and prokaryotic organisms, but have focussed on Saccharomyces cerevisiae. The nutrient environments employed have comprised both known and automatically generated sets of nutrients and relative maximum uptake rates. The results generate more than 1000 unique minimal networks for a single condition, demonstrating the complexity of the minimization problem. We then performed a frequency analysis on the affected genes to discover patterns or similarities among the networks and the redundancy of specific pathway-related genes. In addition to this, we also evaluated the networks using a pathway-oriented robustness analysis, to see how the networks respond to random variation in the reaction fluxes. Finally, we have done a cross-species comparison of our algorithm’s results to highlight some of the homologous genes that are retained in the networks of a majority of species. Thus, our work has produced a tool for in silico genome minimization that permits the discovery of mandatory genes in the minimal metabolic networks.

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