MUFINS: multi-formalism interaction network simulator.

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Title: MUFINS: multi-formalism interaction network simulator.
Author(s): Wu, H
Von Kamp, A
Leoncikas, V
Mori, W
Sahin, N
Gevorgyan, A
Linley, C
Grabowski, M
Mannan, AA
Stoy, N
Stewart, GR
Ward, LT
Lewis, DJM
Sroka, J
Matsuno, H
Klamt, S
Westerhoff, HV
McFadden, J
Plant, NJ
Kierzek, AM
Item Type: Journal Article
Abstract: 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.
Publication Date: 29-Aug-2016
Date of Acceptance: 29-Aug-2016
URI: http://hdl.handle.net/10044/1/51937
DOI: https://dx.doi.org/10.1038/npjsba.2016.32
ISSN: 2056-7189
Publisher: Nature Publishing Group
Journal / Book Title: npj Systems Biology and Applications
Volume: 2
Copyright Statement: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/ by/4.0/ © The Author(s) 2016
Publication Status: Published online
Article Number: 16032
Open Access location: https://www.nature.com/articles/npjsba201632?WT.feed_name=subjects_computational-biology-and-bioinformatics
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics



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