Building Model Prototypes from Time-Course Data

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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

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

Bibliographical note

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


  • 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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