Large-scale generation of computational models from biochemical pathway maps

TL;DR

This paper presents an automated approach to generate large collections of biochemical pathway models from various data sources, significantly accelerating systems biology modeling efforts.

Contribution

It introduces a pipeline that automatically creates diverse mathematical models from pathway data, making them publicly accessible in standard formats.

Findings

Over 140,000 models generated and available online

Models include kinetic, logical, and constraint-based types

Models are encoded in SBML and visualized as SBGN maps

Abstract

Background: Systems biology projects and omics technologies have led to a growing number of biochemical pathway reconstructions. However, mathematical models are still most often created de novo, based on reading the literature and processing pathway data manually. Results: To increase the efficiency with which such models can be created, we automatically generated mathematical models from pathway representations using a suite of freely available software. We produced models that combine data from KEGG PATHWAY, BioCarta, MetaCyc and SABIO-RK; According to the source data, three types of models are provided: kinetic, logical and constraint-based. All models are encoded using SBML Core and Qual packages, and available through BioModels Database. Each model contains the list of participants, the interactions, and the relevant mathematical constructs, but, in most cases, no meaningful parameter values. Most models are also available as easy to understand graphical SBGN maps. Conclusions: to date, the project has resulted in more than 140000 models freely available. We believe this resource can tremendously accelerate the development of mathematical models by providing initial starting points ready for parametrization.