SBML2HYB: a Python interface for SBML compatible hybrid modeling

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.

Automated summary

Here, we introduce sbml2hyb, a Python tool that converts mechanistic models in SBML format into hybrid semiparametric models combining mechanistic functions with machine learning. The tool allows training and storage of the hybrid models in databases in SBML format, and includes an export interface with format validation. Two case studies demonstrate the use of sbml2hyb. Additionally, we present HMOD, a new model format that consolidates mechanistic model information with machine learning information following the SBML rules. We anticipate that sbml2hyb and HMOD will greatly facilitate the widespread adoption of hybrid modeling techniques for biological systems analysis.