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Metabolic Circuit Design Automation by Multi-objective BioCAD

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Machine Learning, Optimization, and Big Data (MOD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10122))

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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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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Catania, Catania, Italy

    Andrea Patané, Piero Conca, Giovanni Carapezza, Andrea Santoro & Giuseppe Nicosia

  2. Consiglio Nazionale delle Ricerche (CNR), Catania, Italy

    Piero Conca & Andrea Santoro

  3. Istituto Italiano di Tecnologia (IIT), Milan, Italy

    Andrea Santoro & Jole Costanza

Authors
  1. Andrea Patané
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  2. Piero Conca
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  3. Giovanni Carapezza
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  4. Andrea Santoro
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  5. Jole Costanza
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  6. Giuseppe Nicosia
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Corresponding author

Correspondence to Piero Conca .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. Semantic Technology Laboratory, National Research Council (CNR), Catania, Italy

    Piero Conca

  3. Dipartimento di Sociologia e Metodi della Ricerca Sociale, Università di Catania, Catania, Italy

    Giovanni Giuffrida

  4. Department of Mathematics and Computer Science, University of Catania, Catania, Italy

    Giuseppe Nicosia

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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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  • DOI: https://doi.org/10.1007/978-3-319-51469-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51468-0

  • Online ISBN: 978-3-319-51469-7

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