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MUFINS: multi-formalism interaction network simulator.
Wu, Huihai; von Kamp, Axel; Leoncikas, Vytautas; Mori, Wataru; Sahin, Nilgun; Gevorgyan, Albert; Linley, Catherine; Grabowski, Marek; Mannan, Ahmad A; Stoy, Nicholas; Stewart, Graham R; Ward, Lara T; Lewis, David J M; Sroka, Jacek; Matsuno, Hiroshi; Klamt, Steffen; Westerhoff, Hans V; McFadden, Johnjoe; Plant, Nicholas J; Kierzek, Andrzej M.
Afiliação
  • Wu H; School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
  • von Kamp A; Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
  • Leoncikas V; School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
  • Mori W; Graduate School of Science and Engineering and Faculty of Science, Yamaguchi University, Yoshida, Yamaguchi, Japan.
  • Sahin N; Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands.
  • Gevorgyan A; MedImmune, Cambridge, UK.
  • Linley C; School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
  • Grabowski M; Institute of Informatics, University of Warsaw, Warsaw, Poland.
  • Mannan AA; School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
  • Stoy N; School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
  • Stewart GR; School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
  • Ward LT; Oncology DMPK, AstraZeneca, Alderley Park, Cheshire, UK.
  • Lewis DJM; School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
  • Sroka J; Institute of Informatics, University of Warsaw, Warsaw, Poland.
  • Matsuno H; Graduate School of Science and Engineering and Faculty of Science, Yamaguchi University, Yoshida, Yamaguchi, Japan.
  • Klamt S; Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
  • Westerhoff HV; Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands.
  • McFadden J; Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.
  • Plant NJ; Synthetic Systems Biology, Netherlands Institute for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands.
  • Kierzek AM; School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
NPJ Syst Biol Appl ; 2: 16032, 2016.
Article em En | MEDLINE | ID: mdl-28725480
Systems Biology has established numerous approaches for mechanistic modeling of molecular networks in the cell and a legacy of models. The current frontier is the integration of models expressed in different formalisms to address the multi-scale biological system organization challenge. We present MUFINS (MUlti-Formalism Interaction Network Simulator) software, implementing a unique set of approaches for multi-formalism simulation of interaction networks. We extend the constraint-based modeling (CBM) framework by incorporation of linear inhibition constraints, enabling for the first time linear modeling of networks simultaneously describing gene regulation, signaling and whole-cell metabolism at steady state. We present a use case where a logical hypergraph model of a regulatory network is expressed by linear constraints and integrated with a Genome-Scale Metabolic Network (GSMN) of mouse macrophage. We experimentally validate predictions, demonstrating application of our software in an iterative cycle of hypothesis generation, validation and model refinement. MUFINS incorporates an extended version of our Quasi-Steady State Petri Net approach to integrate dynamic models with CBM, which we demonstrate through a dynamic model of cortisol signaling integrated with the human Recon2 GSMN and a model of nutrient dynamics in physiological compartments. Finally, we implement a number of methods for deriving metabolic states from ~omics data, including our new variant of the iMAT congruency approach. We compare our approach with iMAT through the analysis of 262 individual tumor transcriptomes, recovering features of metabolic reprogramming in cancer. The software provides graphics user interface with network visualization, which facilitates use by researchers who are not experienced in coding and mathematical modeling environments.

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2016 Tipo de documento: Article