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Growth Dependent Computation of Chokepoints in Metabolic Networks

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Computational Methods in Systems Biology (CMSB 2020)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12314))

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Abstract

Bacterial infections are among the major causes of mortality in the world. Despite the social and economical burden produced by bacteria, the number of new drugs to combat them increases very slowly due to the cost and time to develop them. Thus, innovative approaches to identify efficiently drug targets are required. In the absence of genetic information, chokepoint reactions represent appealing drug targets since their inhibition might involve an important metabolic damage. In contrast to the standard definition of chokepoints, which is purely structural, this paper makes use of the dynamical information of the model to compute chokepoints. This novel approach can provide a more realistic set of chokepoints. The dependence of the number of chokepoints on the growth rate is assessed on a number of metabolic networks. A software tool has been implemented to facilitate the computation of growth dependent chokepoints by the practitioners.

This work was supported by the Spanish Ministry of Science, Innovation and Universities [ref. Medrese-RTI2018-098543-B-I00], and by the Medical Research Council, UK, MR/N501864/1.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, Spain

    Alexandru Oarga & Jorge Júlvez

  2. Department of Medicine, University of Cambridge, Cambridge, UK

    Bridget Bannerman

Authors
  1. Alexandru Oarga
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  2. Bridget Bannerman
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  3. Jorge Júlvez
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Corresponding author

Correspondence to Jorge Júlvez .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Oxford, Oxford, UK

    Alessandro Abate

  2. Department of Computer and Information Science, University of Konstanz, Konstanz, Germany

    Tatjana Petrov

  3. Department of Computer Science, Saarland University, Saarbrücken, Germany

    Verena Wolf

Appendices

A Appendix

The software tool findCPcli developed in this work consists of a command line application that, given an input model provided by the user, computes the sizes of the sets of non-reversible reactions, reversible reactions, dead reactions and chokepoint reactions for different values of \(\gamma \). The results are saved in a spreadsheet file with a format similar to the one presented in Table 3.

The tool findCPcli is distributed as a Python package and requires Python 3.5 or a higher version. The source can be found at github.com/findCP/findCPcli. findCPcli can be installed with the pip package management tool:

pip install findCPcli

Once installed, the results for a given SBML model can be computed running:

findCPcli -i 

where:

  • is the path of the input SBML model file to be used. The supported file formats are .xml, .json and .yml.

  • is the path of the spreadsheet file that will be saved with the results computed on the model. The available file formats for the spreadsheet file are .xls, .xlsx and .ods.

When the above command is executed, the command line application will inform about the task that will be computed. If the task finishes successfully and the spreadsheet file has been saved, the application will inform about it and will end the execution.

Further information about the operations provided by the application can be found by executing: findCPcli -h.

B Appendix

Table 3 reports the sizes of the sets of reversible, non-reversible, dead and chokepoint reactions for several constraint-based models of the Biomodels repository [3]. All the results were computed by the tool findCPcli. The maximum CPU time was 82.776 s to compute the results of model MODEL1507180017 in an Intel Core i5-9300H CPU @ 2.40 GHz \(\times \) 8.

Table 3. Sizes of the sets of reversible, non-reversible, dead and chokepoint reactions for several constraint-based models.
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Oarga, A., Bannerman, B., Júlvez, J. (2020). Growth Dependent Computation of Chokepoints in Metabolic Networks. In: Abate, A., Petrov, T., Wolf, V. (eds) Computational Methods in Systems Biology. CMSB 2020. Lecture Notes in Computer Science(), vol 12314. Springer, Cham. https://doi.org/10.1007/978-3-030-60327-4_6

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  • DOI: https://doi.org/10.1007/978-3-030-60327-4_6

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  • Online ISBN: 978-3-030-60327-4

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