» Articles » PMID: 17052114

Evolving a Lingua Franca and Associated Software Infrastructure for Computational Systems Biology: the Systems Biology Markup Language (SBML) Project

Overview
Specialty Biology
Date 2006 Oct 21
PMID 17052114
Citations 57
Authors
Affiliations
Soon will be listed here.
Abstract

Biologists are increasingly recognising that computational modelling is crucial for making sense of the vast quantities of complex experimental data that are now being collected. The systems biology field needs agreed-upon information standards if models are to be shared, evaluated and developed cooperatively. Over the last four years, our team has been developing the Systems Biology Markup Language (SBML) in collaboration with an international community of modellers and software developers. SBML has become a de facto standard format for representing formal, quantitative and qualitative models at the level of biochemical reactions and regulatory networks. In this article, we summarise the current and upcoming versions of SBML and our efforts at developing software infrastructure for supporting and broadening its use. We also provide a brief overview of the many SBML-compatible software tools available today.

Citing Articles

Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways.

Feng J, Zhang X, Tian T Int J Mol Sci. 2024; 25(18).

PMID: 39337687 PMC: 11432143. DOI: 10.3390/ijms251810204.


Verifiable biology.

Konur S, Gheorghe M, Krasnogor N J R Soc Interface. 2023; 20(202):20230019.

PMID: 37160165 PMC: 10169095. DOI: 10.1098/rsif.2023.0019.


Fixing molecular complexes in BioPAX standards to enrich interactions and detect redundancies using semantic web technologies.

Juigne C, Dameron O, Moreews F, Gondret F, Becker E Bioinformatics. 2023; 39(5).

PMID: 37097895 PMC: 10168583. DOI: 10.1093/bioinformatics/btad257.


Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas.

de Falco B, Giannino F, Carteni F, Mazzoleni S, Kim D RSC Adv. 2022; 12(39):25528-25548.

PMID: 36199351 PMC: 9449821. DOI: 10.1039/d2ra03326g.


Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer.

Ng R, Lee J, Baloni P, Diener C, Heath J, Su Y Front Oncol. 2022; 12:914594.

PMID: 35875150 PMC: 9303011. DOI: 10.3389/fonc.2022.914594.