Abstract
We present a thorough in silico analysis and optimization of the genome-scale metabolic model of the mycolic acid pathway in M. tuberculosis. We apply and further extend meGDMO to account for finer sensitivity analysis and post-processing analysis, thanks to the combination of statistical evaluation of strains robustness, and clustering analysis to map the phenotype-genotype relationship among Pareto optimal strains. In the first analysis scenario, we find 12 Pareto-optimal single gene set knockout, which completely shut down the pathway, hence critically reducing the pathogenicity of M. tuberculosis; as well as 34 genotypically different strains in which the production of mycolic acid is severely reduced.
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Patané, A., Conca, P., Carapezza, G., Santoro, A., Costanza, J., Nicosia, G. (2016). Metabolic Circuit Design Automation by Multi-objective BioCAD. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_3
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