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

Diana Giurghita,Joe Wandy,Simon Rogers,Jonathan Wren,Mu Niu,Rónán Daly,Dirk Husmeier

doi:10.1093/bioinformatics/bty089

Abstract

MotivationMathematical modelling based on ordinary differential equations (ODEs) is widely used to describe the dynamics of biological systems, particularly in systems and pathway biology. Often the kinetic parameters of these ODE systems are unknown and have to be inferred from the data. Approximate parameter inference methods based on gradient matching (which do not require performing computationally expensive numerical integration of the ODEs) have been getting popular in recent years, but many implementations are difficult to run without expert knowledge. Here, we introduce ShinyKGode, an interactive web application to perform fast parameter inference on ODEs using gradient matching.ResultsShinyKGode can be used to infer ODE parameters on simulated and observed data using gradient matching. Users can easily load their own models in Systems Biology Markup Language format, and a set of pre-defined ODE benchmark models are provided in the application. Inferred parameters are visualized alongside diagnostic plots to assess convergence.Availability and implementationThe R package for ShinyKGode can be installed through the Comprehensive R Archive Network (CRAN). Installation instructions, as well as tutorial videos and source code are available at https://joewandy.github.io/shinyKGode.Supplementary information Supplementary data are available at Bioinformatics online.

Highlights

Mathematical modelling using ordinary differential equations (ODEs) is commonly used in systems and pathway biology
We introduce an interactive application, ShinyKGode, built upon the KGode package, which provides a user-friendly interface to perform gradient matching and to visualize its results
Parameter estimation in our application relies on the KGode package, which implements fast parameter inference using gradient matching (Niu et al, 2016, 2017)

Summary

Introduction

Mathematical modelling using ordinary differential equations (ODEs) is commonly used in systems and pathway biology. In this modelling paradigm, ODE parameters are often unknown and the aim of inference is to estimate parameters of the dynamical system. ODE parameters are often unknown and the aim of inference is to estimate parameters of the dynamical system This involves numerically solving the system of ODEs at each step of the inference procedure and evaluating how well the inferred parameters match the data. Approximate methods based on gradient matching, which allow estimating the unknown ODE parameters without the need for numerical integration, have been gaining popularity.

Methods and implementation

Conclusion