Path2Models: large-scale generation of computational models from biochemical pathway maps
- Creators
- Büchel, Finja
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Hucka, Michael
Abstract
Background: Systems biology projects and omics technologies have led to a growing number of biochemical pathway models and reconstructions. However, the majority of these models are still created de novo, based on literature mining and the manual processing of pathway data. Results: To increase the efficiency of model creation, the Path2Models project has automatically generated mathematical models from pathway representations using a suite of freely available software. Data sources include KEGG, BioCarta, MetaCyc and SABIO-RK. Depending on the source data, three types of models are provided: kinetic, logical and constraint-based. Models from over 2 600 organisms are encoded consistently in SBML, and are made freely available through BioModels Database at http://www.ebi.ac.uk/biomodels-main/path2models. Each model contains the list of participants, their interactions, the relevant mathematical constructs, and initial parameter values. Most models are also available as easy-to-understand graphical SBGN maps. Conclusions: To date, the project has resulted in more than 140 000 freely available models. Such a resource can tremendously accelerate the development of mathematical models by providing initial starting models for simulation and analysis, which can be subsequently curated and further parameterized.
Additional Information
© 2013 Büchel et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Received: 19 July 2013. Accepted: 23 October 2013. Published: 1 November 2013. NS and PM acknowledge support from the European Union FP7 project UNICELLSYS (grant number: 201142). MvI and PM received financial aid from the EU project BioPreDyn (ECFP7-KBBE-2011-5 Grant number 289434). MH and SK acknowledge support from the US National Institute of General Medical Sciences (grant number GM070923). CW, FB, FM, RK, AD, and AZ are grateful for financial support by the Federal Ministry of Education and Research (BMBF, Germany) in the projects Virtual Liver Network (grant number 0315756) and National Genome Research Network (NGFN-Plus, grant number 01GS08134). AD thanks the EU for funding his Marie Curie International Outgoing Fellowship within FP7 (project AMBiCon, 332020). PM acknowledges support from the US National Institute of General Medical Sciences (grant number GM080219) and the BBSRC (grant number BB/J019259/1). MvI, FB, FM, MS, NR received dedicated support from the EMBL-EBI. NS also thanks Ben Morris of the University of North Carolina at Chapel Hill for generously and freely making available his code for converting the NCBI Taxonomy flat files into a Newick tree, which was used to generate the phylogenetic tree of genome-scale models. MG and CL acknowledge support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115156. All authors would like to dedicate this paper to the memory of Isabel Rojas, creator of the SABIO-RK database, who passed away in July 2013. Authors' contributions: AD coordinated the work done by CW, FB, FM, RK, MR at ZBIT, contributed to JSBML including Layout and qual package, implemented an algorithm for unit derivation, and generated kinetic laws, parameters and units ab initio. AZ supervised the researchers at ZBIT. FB, FM and MvI contributed to the development of the SBML qual implementation. FB further contributed to the translation of signaling models to SBML and augmented them with additional information from BioCarta. CW contributed the source KGML models, implemented the metabolic and signaling conversions from KGML to SBML and generated the initial SBML models. He contributed to the implementation of multiple SBML extensions that are used within the scope of the manuscript. MR and RK implemented the SABIO-RK search. NS generated the genome-scale metabolic models, responded to reviewers' comments and edited the manuscript. PM and DBK assisted with the generation of genome-scale metabolic models. TC, MW and FS dealt with the representation of the models as SBGN PD maps and their automatic layout. MS generated SBGN AF-ML and graphical renderings of the SBML qual models. CC contributed to the discussions on SBML qual usage. NR contributed to the development of JSBML. CL, MG and NR contributed to BioModels Database. SK finalized the SBML qual specification. MH contributed to JSBML and the SBML qual specification. JSR helped to initiate the project, and contributed to the discussions on SBML qual usage. NS had the original idea to automate the creation of models from pathways. NLN initiated and coordinated the project and the manuscript. All authors contributed to the writing of the manuscript. All authors read and approved the final manuscript.Attached Files
Published - 1752-0509-7-116.pdf
Supplemental Material - 1752-0509-7-116-s1.xls
Supplemental Material - 1752-0509-7-116-s2.zip
Files
Additional details
- PMCID
- PMC4228421
- Eprint ID
- 43160
- Resolver ID
- CaltechAUTHORS:20131224-081718618
- 201142
- European Union
- 289434
- European Union
- GM070923
- NIH
- 0315756
- Federal Ministry of Education and Research (BMBF) Project Virtual Liver Network
- 01GS08134
- NIH
- 332020
- Marie Curie Fellowship
- GM080219
- NIH
- BB/J019259/1
- Biotechnology and Biological Sciences Research Council (BBSRC)
- European Molecular Biology Laboratory (EMBL)
- 115156
- Innovative Medicines Initiative Joint Undertaking
- Created
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2013-12-24Created from EPrint's datestamp field
- Updated
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2021-11-10Created from EPrint's last_modified field