Path2Models: large-scale generation of computational models from biochemical pathway maps

Büchel, Finja; Rodríguez, Nicolás; Swainston, Neil; Wrzodek, Clemens; Czauderna, Tobias; Keller, Roland; Mittag, Florian; Schubert, Michaël; Glonț, Mihai; Golebiewski, Martin; Iersel, Martijn P. van; Keating, Sarah M.; Rall, Matthias; Wybrow, Michael; Hermjakob, Henning; Hucka, Michael; Kell, Douglas B.; Müller, Wolfgang G.; Mendes, Pedro; Zell, Andreas; Chaouiya, Claudine; Sáez-Rodríguez, Julio; Schreiber, Falk; Laibe, Camille; Dräger, Andreas; Novère, Nicolas Le

doi:10.1186/1752-0509-7-116

Cited by 163 publications

(159 citation statements)

References 82 publications

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“…The definition of rate laws requires additional information or assumptions, e.g., that a reaction follows the law of mass action kinetics. Some efforts have been launched to generate logic and numerical models from pathway maps [55]. For instance, the ongoing work on automated translation of SBGN and CellDesigner formats into logical models may help to bridge the quantitative and qualitative applications of disease maps.…”

Section: Construction Of Executable Mathematical Models From Disease mentioning

confidence: 99%

Community-driven roadmap for integrated disease maps

Ostaszewski¹,

Gebel²,

Kuperstein³

et al. 2018

Preprint

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The Disease Maps Project builds upon a network of scientific and clinical groups that exchange best practices, share information, and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation, or serve as a scaffold for the generation of predictive mathematical models.This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and web-tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations in order to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.

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Section: Construction Of Executable Mathematical Models From Disease mentioning

confidence: 99%

Community-driven roadmap for integrated disease maps

Ostaszewski¹,

Gebel²,

Kuperstein³

et al. 2018

Preprint

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“…Currently, BioModels contains over 1,200 models reported in the literature and 140,000 models automatically generated from pathway resources using a Path2Models project, a tool developed by Büchel et al [55].…”

Section: Biomodelsmentioning

confidence: 99%

Metabolic pathway databases and model repositories

et al. 2018

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Background: The number of biological Knowledge bases/databases storing metabolic pathway information and models has been growing rapidly. These resources are diverse in the type of information/data, the analytical tools, and objectives. Here we present a review of the most popular metabolic pathway databases and model repositories, focusing on their scope, content including reactions, enzymes, compounds, and genes, and applicability. The review aims to help researchers choose a suitable database or model repository according to the information and data required, by providing an insight look of each pathway resource. Results: Four pathways databases and three model repositories were selected on the basis of popularity and diversity. Our review showed that the pathway resources vary in many aspects, such as their scope, content, access to data and the tools. In addition, inconsistencies have been observed in nomenclature and representation of database entities. The three model repositories reviewed do not offer a brief description of the models' characteristics such as simulation conditions. Conclusions: The inconsistencies among the databases in representing their contents may hamper the maximal use of the knowledge accumulated in these databases in particular and the area of systems biology at large. Therefore, it is strongly recommended that the database creators and the metabolic network models developers should follow international standards for the nomenclature of reactions and metabolites. Besides, computationally generated models that could be obtained from model repositories should be utilized with manual curations as they lack some important components that are necessary for full functionality of the models.Keywords: metabolic pathway; database; model repository Author summary: Four metabolic pathway databases and three model repositories were reviewed with regard to their scope, content, and applicability. Despite their innumerable use in the fields of systems biology and metabolic engineering, these pathway databases and model repositories are not in harmony with each other due to the inconsistencies in the way they represent their contents. Besides, the automatically generated metabolic models that can be obtained from the model repositories are not accurate enough for further scientific usage without additional manual curation. Therefore, international standards such as IUBMB principles should be strictly obeyed in creating such metabolic pathway resources.

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“…These recent models were reconstructed considering element and charge balanced reactions, thermodynamic consistency and gene-reaction-protein associations. All these features have been found to be critical to improve the accuracy of the prediction of cellular [32], Pathway Tools [42], SuBliMinaL [85], MBA [39], GLAMM [4], RAVEN [1], Path2Models [9] Strain design Knockout MOMA [78], ROOM [81], OptKnock [10], RobustKnock [86], OptSwap [95], OptGene [66], GDLS [53], EMILiO [93], OptFlux [73] Amplification FSEOF [15], FVSEOF with GR [65] Knockout, knockdown, amplification…”

Section: Genome-scale Metabolic Networkmentioning

confidence: 99%

Applications of genome-scale metabolic network model in metabolic engineering

Kim

et al. 2015

Journal of Industrial Microbiology and Biotechnology

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Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.

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Path2Models: large-scale generation of computational models from biochemical pathway maps

Cited by 163 publications

References 82 publications

Community-driven roadmap for integrated disease maps

Community-driven roadmap for integrated disease maps

Metabolic pathway databases and model repositories

Applications of genome-scale metabolic network model in metabolic engineering

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