Building Model Prototypes from Time-Course Data

Alan Veliz-Cuba, Stephen Voss, David Murrugarra

Research output: Contribution to journalArticlepeer-review

Abstract

A primary challenge in building predictive models from temporal data is selecting the appropriate model topology and the regulatory functions that describe the data. In this paper we introduce a method for building model prototypes. The method takes as input a collection of time course data. After network inference, we use our toolbox to simulate the model as a stochastic Boolean model. Our method provides a model that can qualitatively reproduce the patterns of the original data and can further be used for model analysis, making predictions, and designing interventions. We applied our method to a time-course, gene-expression data that were collected during salamander tail regeneration under control and intervention conditions. The inferred model captures important regulations that were previously validated in the research literature and gives novel interactions for future testing. The toolbox for inference and simulations is freely available at github.com/alanavc/prototype-model.

Original languageEnglish
Pages (from-to)107-120
Number of pages14
JournalLetters in Biomathematics
Volume9
Issue number1
StatePublished - Feb 15 2022

Bibliographical note

Funding Information:
A. VC. was partially supported by the Simons Foundation (grant 516088). S. R. V. was partially supported by NIH grant R24OD010435. D. M. was partially supported by a Collaboration grant (850896) from the Simons Foundation. The authors thank the referees for their insightful comments that have improved the manuscript.

Publisher Copyright:
© 2022, Intercollegiate Biomathematics Alliance. All rights reserved.

Keywords

  • Boolean networks
  • Network inference
  • stochastic simulations
  • time course data

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Applied Mathematics

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