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Analysing the Expressiveness of Metabolic Networks Representations

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Artificial Life and Evolutionary Computation (WIVACE 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1977))

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Abstract

In this work, we explore the expressiveness of three graph-based representations of metabolic networks. We consider Abstract Metabolic Networks (AMNs), metabolic-Directed Acyclic Graphs (m-DAGs) and Reaction Graphs (RGs). These representations form a hierarchical view of the metabolism, AMNs being the most abstract, m-DAGs serving as the intermediate, and RGs being the most detailed. We evaluate their expressiveness for a case study comprising 331 Vertebrates and by using the Weisfeiler-Lehman graph kernel to perform the comparison. The results show that AMNs are not able to discern the various taxonomic groups at the Class level, while m-DAGs and RGs clearly distinguish Mammals, Fishes and Birds. When focusing on Mammals at the Order level, only m-DAGs are partially able to identify some of the taxonomic groups. Moreover, m-DAGs are able to distinguish Primates at the Infraorder level of taxonomy. Based on the obtained results, it emerges that m-DAGs are a good compromise between the amount of network information and the computational effort needed to obtain reliable patterns on the taxonomic clustering of the different organisms.

This work was partially supported by DAIS - Ca’ Foscari University of Venice within the IRIDE program and Grant PID2021-126114NB-C44 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”.

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Author information

Authors and Affiliations

  1. Mathematics and Computer Science Department, University of the Balearic Islands, Palma, Spain

    Irene García, Mercè Llabrés & Pere Palmer-Rodríguez

  2. Department of Environmental Science, Computer Science and Statistics, University Ca’ Foscari of Venice, Venice, Italy

    Bessem Chouaia & Marta Simeoni

  3. European Centre for Living Technology (ECLT), Venice, Italy

    Marta Simeoni

Authors
  1. Irene García
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  2. Bessem Chouaia
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  3. Mercè Llabrés
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  4. Pere Palmer-Rodríguez
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  5. Marta Simeoni
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Corresponding author

Correspondence to Marta Simeoni .

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Editors and Affiliations

  1. University of Modena and Reggio Emilia, Modena, Italy

    Marco Villani

  2. University of Parma, Parma, Italy

    Stefano Cagnoni

  3. University of Modena and Reggio Emilia, Modena, Italy

    Roberto Serra

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García, I., Chouaia, B., Llabrés, M., Palmer-Rodríguez, P., Simeoni, M. (2024). Analysing the Expressiveness of Metabolic Networks Representations. In: Villani, M., Cagnoni, S., Serra, R. (eds) Artificial Life and Evolutionary Computation. WIVACE 2023. Communications in Computer and Information Science, vol 1977. Springer, Cham. https://doi.org/10.1007/978-3-031-57430-6_7

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  • DOI: https://doi.org/10.1007/978-3-031-57430-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57429-0

  • Online ISBN: 978-3-031-57430-6

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