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ShinyKGode: an interactive application for ODE parameter inference using gradient matching.

Joe Wandy1, Mu Niu2, Diana Giurghita3

  • 1Glasgow Polyomics, University of Glasgow, Glasgow, UK.

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|March 1, 2018
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Summary
This summary is machine-generated.

ShinyKGode is a new web application for fast parameter inference in ordinary differential equations (ODEs) using gradient matching. It simplifies complex mathematical modeling for biological systems, making parameter estimation more accessible.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Mathematical modeling using ordinary differential equations (ODEs) is crucial for understanding biological system dynamics.
  • Inferring kinetic parameters for ODE models from data is essential but often challenging.
  • Existing gradient matching methods can be complex and require expert knowledge.

Purpose of the Study:

  • To introduce ShinyKGode, an interactive web application for efficient parameter inference in ODE models.
  • To provide an accessible tool for gradient matching-based parameter estimation.
  • To facilitate the analysis of biological systems and pathways.

Main Methods:

  • Utilizes gradient matching to infer parameters without computationally intensive ODE integration.
  • Accepts user-defined models in Systems Biology Markup Language (SBML) format.
  • Includes pre-defined ODE benchmark models for ease of use and testing.

Main Results:

  • ShinyKGode enables parameter inference on both simulated and observed data.
  • Visualizations of inferred parameters and diagnostic plots aid in assessing convergence.
  • The application offers a user-friendly interface for complex parameter estimation tasks.

Conclusions:

  • ShinyKGode democratizes parameter inference for ODE models in systems biology.
  • The tool enhances the speed and accessibility of analyzing biological dynamics.
  • It supports researchers in uncovering kinetic parameters crucial for biological understanding.