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

Wandy, J. , Niu, M., Giurghita, D., Daly, R. , Rogers, S. and Husmeier, D. (2018) ShinyKGode: an interactive application for ODE parameter inference using gradient matching. Bioinformatics, 34(13), pp. 2314-2315. (doi: 10.1093/bioinformatics/bty089) (PMID:29490021) (PMCID:PMC6022662)

157270.pdf - Published Version
Available under License Creative Commons Attribution.



Motivation: Mathematical 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. Results: ShinyKGode 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 visualised alongside diagnostic plots to assess convergence. Availability and Implementation: The 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

Item Type:Articles
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk and Rogers, Dr Simon and Wandy, Dr Joe and Giurghita, Miss Diana and Daly, Dr Ronan
Authors: Wandy, J., Niu, M., Giurghita, D., Daly, R., Rogers, S., and Husmeier, D.
College/School:College of Medical Veterinary and Life Sciences
College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Bioinformatics
Publisher:Oxford University Press
ISSN (Online):1460-2059
Published Online:27 February 2018
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in Bioinformatics 34(13):2314-2315
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
633291Computational inference in systems biologyDirk HusmeierEngineering and Physical Sciences Research Council (EPSRC)EP/L020319/1M&S - STATISTICS
623593Institutional Strategic Support Fund (ISSF)Anna DominiczakWellcome Trust (WELLCOTR)105614/Z/14/ZRI CARDIOVASCULAR & MEDICAL SCIENCES