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This study introduces a data-driven method for inferring biochemical reaction networks from species concentration data. It uses sparse Bayesian inference to identify potential biological systems and their parameters.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biochemistry

Background:

  • Understanding biological system dynamics requires identifying governing biochemical reactions.
  • Inferring these reaction networks from time-series data is a significant challenge in systems biology.

Purpose of the Study:

  • To develop a data-driven method for inferring biochemical reaction systems from observed species concentrations over time.
  • To provide robust and interpretable reaction networks with uncertainty estimates.

Main Methods:

  • Formulating the inference problem as a regression over a mass-action constrained reaction space.
  • Employing sparse Bayesian inference with the regularized horseshoe prior.
  • Utilizing time-series data of species concentrations.

Main Results:

  • Successful inference of underlying biochemical reaction networks from observational data.
  • Generation of interpretable networks with associated parameter uncertainty estimates.
  • Demonstration on two examples showcasing improved accuracy and information gain.

Conclusions:

  • The developed method offers a powerful approach for discovering governing reactions in biological systems.
  • The inferred networks and uncertainty estimates guide further experimental investigation by biologists.
  • This data-driven strategy enhances the understanding of complex biological dynamics.