Fisher, CP, Plant, NJ orcid.org/0000-0003-4332-8308, Moore, JB orcid.org/0000-0003-4750-1550 et al. (1 more author) (2013) QSSPN: dynamic simulation of molecular interaction networks describing gene regulation, signalling and whole-cell metabolism in human cells. Bioinformatics, 29 (24). pp. 3181-3190. ISSN 1367-4803
Abstract
Motivation: Dynamic simulation of genome-scale molecular interaction networks will enable the mechanistic prediction of genotype–phenotype relationships. Despite advances in quantitative biology, full parameterization of whole-cell models is not yet possible. Simulation methods capable of using available qualitative data are required to develop dynamic whole-cell models through an iterative process of modelling and experimental validation. Results: We formulate quasi-steady state Petri nets (QSSPN), a novel method integrating Petri nets and constraint-based analysis to predict the feasibility of qualitative dynamic behaviours in qualitative models of gene regulation, signalling and whole-cell metabolism. We present the first dynamic simulations including regulatory mechanisms and a genome-scale metabolic network in human cell, using bile acid homeostasis in human hepatocytes as a case study. QSSPN simulations reproduce experimentally determined qualitative dynamic behaviours and permit mechanistic analysis of genotype–phenotype relationships. Availability and implementation: The model and simulation software implemented in Cþþ are available in supplementary material and at http://sysbio3.fhms.surrey.ac.uk/qsspn/. Contact: a.kierzek@surrey.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author 2013. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Sep 2017 11:33 |
Last Modified: | 05 Sep 2017 11:33 |
Status: | Published |
Publisher: | Oxford University Press |
Identification Number: | 10.1093/bioinformatics/btt552 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:118400 |