Pinto J, Costa RS, Alexandre L, Ramos J, Oliveira R. SBML2HYB: a Python interface for SBML compatible hybrid modeling.
Bioinformatics 2023;
39:6994184. [PMID:
36661327 PMCID:
PMC9889961 DOI:
10.1093/bioinformatics/btad044]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 01/03/2023] [Accepted: 01/19/2023] [Indexed: 01/21/2023] Open
Abstract
SUMMARY
Here, we present sbml2hyb, an easy-to-use standalone Python tool that facilitates the conversion of existing mechanistic models of biological systems in Systems Biology Markup Language (SBML) into hybrid semiparametric models that combine mechanistic functions with machine learning (ML). The so-formed hybrid models can be trained and stored back in databases in SBML format. The tool supports a user-friendly export interface with an internal format validator. Two case studies illustrate the use of the sbml2hyb tool. Additionally, we describe HMOD, a new model format designed to support and facilitate hybrid models building. It aggregates the mechanistic model information with the ML information and follows as close as possible the SBML rules. We expect the sbml2hyb tool and HMOD to greatly facilitate the widespread usage of hybrid modeling techniques for biological systems analysis.
AVAILABILITY AND IMPLEMENTATION
The Python interface, source code and the example models used for the case studies are accessible at: https://github.com/r-costa/sbml2hyb.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
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