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Modelling Asthma Patients’ Responsiveness to Treatment Using Feature Selection and Evolutionary Computation

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Applications of Evolutionary Computation (EvoApplications 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12694))

Abstract

For several medical treatments, it is possible to observe transcriptional variations in gene expressions between responders and non-responders. Modelling the correlation between such variations and the patient’s response to drugs as a system of Ordinary Differential Equations could be invaluable to improve the efficacy of treatments and would represent an important step towards personalized medicine. Two main obstacles lie on this path: (i) the number of genes is too large to straightforwardly analyze their interactions; (ii) defining the correct parameters for the mathematical models of gene interaction is a complex optimization problem, even when a limited number of genes is involved. In this paper, we propose a novel approach to creating mathematical models able to explain patients’ response to treatment from transcriptional variations. The approach is based on: (i) a feature selection algorithm, set to identify a minimal set of gene expressions that are highly correlated with treatment outcome, (ii) a state-of-the-art evolutionary optimizer, Covariance Matrix Adaptation Evolution Strategy, applied to finding the parameters of the mathematical model characterizing the relationship between gene expressions and patient responsiveness. The proposed methodology is tested on real-world data describing responsiveness of asthma patients to Omalizumab, a humanized monoclonal antibody that binds to immunoglobulin E. In this case study, the presented approach is shown able to identify 5 genes (out of 28,402) that are transcriptionally relevant to predict treatment outcomes, and to deliver a compact mathematical model that is able to explain the interaction between the different genes involved.

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Notes

  1. 1.

    https://github.com/steppenwolf0/modelingEvolutionaryComputation.

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Author information

Authors and Affiliations

  1. Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Universiteitsweg 99, 3584, CG Utrecht, The Netherlands

    Alejandro Lopez-Rincon, Daphne S. Roozendaal, Hilde M. Spierenburg, Asta L. Holm, Renee Metcalf, Paula Perez-Pardo & Aletta D. Kraneveld

  2. UMR 518 MIA, INRAE, Université Paris-Saclay, Paris, France

    Alberto Tonda

Authors
  1. Alejandro Lopez-Rincon
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  2. Daphne S. Roozendaal
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  3. Hilde M. Spierenburg
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  4. Asta L. Holm
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  5. Renee Metcalf
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  6. Paula Perez-Pardo
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  7. Aletta D. Kraneveld
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  8. Alberto Tonda
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Corresponding author

Correspondence to Alejandro Lopez-Rincon .

Editor information

Editors and Affiliations

  1. ETSIIT-CITIC, University of Granada, Granada, Spain

    Pedro A. Castillo

  2. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

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Lopez-Rincon, A. et al. (2021). Modelling Asthma Patients’ Responsiveness to Treatment Using Feature Selection and Evolutionary Computation. In: Castillo, P.A., Jiménez Laredo, J.L. (eds) Applications of Evolutionary Computation. EvoApplications 2021. Lecture Notes in Computer Science(), vol 12694. Springer, Cham. https://doi.org/10.1007/978-3-030-72699-7_23

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  • DOI: https://doi.org/10.1007/978-3-030-72699-7_23

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  • Online ISBN: 978-3-030-72699-7

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