Evidence map›Paper›PMID 28725480›Full record

ArticleNPJ systems biology and applications2016

MUFINS: multi-formalism interaction network simulator.

Huihai Wu, Axel von Kamp, Vytautas Leoncikas, Wataru Mori, Nilgun Sahin, Albert Gevorgyan, Catherine Linley, Marek Grabowski, Ahmad A Mannan, Nicholas Stoy and 10 more

Abstract read
In one paragraph

Article in NPJ systems biology and applications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Huihai WuSchool of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
Axel von KampMax Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
Vytautas LeoncikasSchool of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
Wataru MoriGraduate School of Science and Engineering and Faculty of Science, Yamaguchi University, Yoshida, Yamaguchi, Japan.
Nilgun SahinMolecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands.
Albert GevorgyanMedImmune, Cambridge, UK.
Catherine LinleySchool of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.ORCID 0000-0002-7649-8146
Marek GrabowskiInstitute of Informatics, University of Warsaw, Warsaw, Poland.
Ahmad A MannanSchool of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
Nicholas StoySchool of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
Graham R StewartSchool of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
Lara T WardOncology DMPK, AstraZeneca, Alderley Park, Cheshire, UK.
David J M LewisSchool of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
Jacek SrokaInstitute of Informatics, University of Warsaw, Warsaw, Poland.
Hiroshi MatsunoGraduate School of Science and Engineering and Faculty of Science, Yamaguchi University, Yoshida, Yamaguchi, Japan.
Steffen KlamtMax Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
Hans V WesterhoffMolecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands.
Johnjoe McFaddenSchool of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
Nicholas J PlantSchool of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.ORCID 0000-0003-4332-8308
Andrzej M KierzekSchool of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.

Funding

Biotechnology and Biological Sciences Research Council BB/C008219/1Biotechnology and Biological Sciences Research Council BB/D019079/1Biotechnology and Biological Sciences Research Council BB/F003528/1Biotechnology and Biological Sciences Research Council BB/F003536/1Biotechnology and Biological Sciences Research Council BB/F003544/1Biotechnology and Biological Sciences Research Council BB/F003552/1Biotechnology and Biological Sciences Research Council BB/I00453X/1Biotechnology and Biological Sciences Research Council BB/I004688/1Biotechnology and Biological Sciences Research Council BB/I004696/1Biotechnology and Biological Sciences Research Council BB/I00470X/1
6 · The paper itself

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.

Identifiers

PMID28725480
PMCPMC5516860

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.