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An Efficient Implementation of Flux Variability Analysis for Metabolic Networks

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Artificial Life and Evolutionary Computation (WIVACE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1780))

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Abstract

Flux Variability Analysis (FVA) is an important method to analyze the range of fluxes of a metabolic network. FVA consists in performing a large number of independent optimization problems, to obtain the maximum and minimum flux through each reaction in the network. Although several strategies to make the computation more efficient have been proposed, the computation time of an FVA can still be limiting. We present a two-step procedure to accelerate the FVA computational time that exploits the large presence within metabolic networks of sets of reactions that necessarily have an identical optimal flux value or only differ by a multiplication constant. The first step identifies such sets of reactions. The second step computes the maximum and minimum flux value for just one element of each of set, reducing the total number of optimization problems compared to the classical FVA. We show that, when applied to any metabolic network model included in the BiGG database, our FVA algorithm reduces the total number of optimization problems of about 35\(\%\), and the computation time of FVA of about 30%.

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Authors and Affiliations

  1. Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy

    Bruno G. Galuzzi & Chiara Damiani

  2. SYSBIO Centre of Systems Biology/ ISBE.IT, Milan, Italy

    Bruno G. Galuzzi & Chiara Damiani

Authors
  1. Bruno G. Galuzzi
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  2. Chiara Damiani
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Correspondence to Bruno G. Galuzzi .

Editor information

Editors and Affiliations

  1. University of Cassino and Southern Lazio, Cassino, Italy

    Claudio De Stefano

  2. University of Cassino and Southern Lazio, Gaeta, Italy

    Francesco Fontanella

  3. Universidade Nova de Lisboa, Lisbon, Portugal

    Leonardo Vanneschi

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Galuzzi, B.G., Damiani, C. (2023). An Efficient Implementation of Flux Variability Analysis for Metabolic Networks. In: De Stefano, C., Fontanella, F., Vanneschi, L. (eds) Artificial Life and Evolutionary Computation. WIVACE 2022. Communications in Computer and Information Science, vol 1780. Springer, Cham. https://doi.org/10.1007/978-3-031-31183-3_5

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  • DOI: https://doi.org/10.1007/978-3-031-31183-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31182-6

  • Online ISBN: 978-3-031-31183-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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