Building Model Prototypes from Time-Course Data

  • Alan Veliz-Cuba University of Dayton
  • Stephen Randal Voss University of Kentucky
  • David Murrugarra
Keywords: Network inference, Boolean networks, time course data, stochastic simulations

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.

Published
2022-08-27
How to Cite
Veliz-CubaAlan, VossStephen Randal, and David Murrugarra. 2022. “Building Model Prototypes from Time-Course Data”. Letters in Biomathematics 9 (1), 107–120. https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/535.
Section
Research

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