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Automated multi-level modeling of biological systems considering physico-chemical constraints

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Path2Models: large-scale generation of computational models from biochemical pathway maps
Publication . Büchel, Finja; Rodriguez, Nicolas; Swainston, Neil; Wrzodek, Clemens; Czauderna, Tobias; Keller, Roland; Mittag, Florian; Schubert, Michael; Glont, Mihai; Golebiewski, Martin; van Iersel, Martijn; Keating, Sarah; Rall, Matthias; Wybrow, Michael; Hermjakob, Henning; Hucka, Michael; Kell, Douglas B; Müller, Wolfgang; Mendes, Pedro; Zell, Andreas; Chaouiya, Claudine; Saez-Rodriguez, Julio; Schreiber, Falk; Laibe, Camille; Dräger, Andreas; Le Novère, Nicolas
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.
SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools
Publication . Chaouiya, Claudine; Bérenguier, Duncan; Keating, Sarah M; Naldi, Aurélien; van Iersel, Martijn P; Rodriguez, Nicolas; Dräger, Andreas; Büchel, Finja; Cokelaer, Thomas; Kowal, Bryan; Wicks, Benjamin; Gonçalves, Emanuel; Dorier, Julien; Page, Michel; Monteiro, Pedro T; von Kamp, Axel; Xenarios, Ioannis; de Jong, Hidde; Hucka, Michael; Klamt, Steffen; Thieffry, Denis; Le Novère, Nicolas; Saez-Rodriguez, Julio; Helikar, Tomáš
Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing.

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European Commission

Funding programme

FP7

Funding Award Number

332020

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