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Inferring Pathological Metabolic Patterns in Breast Cancer Tissue from Genome-Scale Models

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Machine Learning, Optimization, and Data Science (LOD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13810))

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Abstract

We will consider genome-scale metabolic models that attempt to describe the metabolism of human cells focusing on breast cells. The model has two versions related to the presence or absence of a specific breast tumor. The aim will be to mine these genome-scale models as a multi-objective optimization problem in order to maximize biomass production and minimize the reactions whose enzymes that catalyze them may have undergone mutations (oncometabolite), causing cancer cells to proliferate. This study discovered characteristic pathological patterns for the breast cancer genome-scale model used. This work presents an in silico BioCAD methodology to investigate and compare the metabolic pathways of breast tissue in the presence of a tumor in contrast to those of healthy tissue. A large number of genome-scale metabolic model simulations have been carried out to explore the solution spaces of genetic configurations and metabolic reactions. An evolutionary algorithm is employed to guide the search for possible solutions, and a multi-objective optimization principle is used to identify the best candidate solutions.

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Author information

Authors and Affiliations

  1. Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy

    Matteo N. Amaradio, Giorgio Jansen & Giuseppe Nicosia

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

    Giorgio Jansen

  3. School of Computing, Newcastle University, Newcastle Upon Tyne, UK

    Varun Ojha

  4. Fondazione Instituto Nazionale Di Genetica Molecolare, Milan, Italy

    Jole Costanza

  5. Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

Authors
  1. Matteo N. Amaradio
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  2. Giorgio Jansen
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  3. Varun Ojha
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  4. Jole Costanza
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  5. Giuseppe Di Fatta
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  6. Giuseppe Nicosia
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Corresponding author

Correspondence to Giuseppe Nicosia .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

Appendix

Appendix

Fig. 6.
figure 6

Genome-scale metabolic model of breast cancer. A graphical representation of the S-matrix is presented, in which on the x-axis are the reactions, and on the y-axis are the metabolites. Each point on the S matrix represents values of S other than 0.

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Fig. 7.
figure 7

Control test for the breast genome-scale metabolic model. The fluxes of all reactions are minimized while maximizing the biomass. Circles indicate the non-dominated points of the Pareto front; all other candidate solutions are the feasible points.

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Fig. 8.
figure 8

Breast genome-scale metabolic model. Minimization of the sum of flows for the reactions considered while maximizing the biomass. Circles indicate the non-dominated points of the Pareto front; all other candidate solutions are the feasible points.

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Amaradio, M.N., Jansen, G., Ojha, V., Costanza, J., Di Fatta, G., Nicosia, G. (2023). Inferring Pathological Metabolic Patterns in Breast Cancer Tissue from Genome-Scale Models. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13810. Springer, Cham. https://doi.org/10.1007/978-3-031-25599-1_43

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  • DOI: https://doi.org/10.1007/978-3-031-25599-1_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25598-4

  • Online ISBN: 978-3-031-25599-1

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