The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Complexity and modularity of intracellular networks: a systematic approach for modelling and simulation.
Open accessAbstract
Assembly of quantitative models of large complex networks brings about several challenges. One of them is the combinatorial complexity, where relatively few signalling molecules can combine to form thousands or millions of distinct chemical species. A receptor that has several separate phosphorylation sites can exist in hundreds of different states, many of which must be accounted for individually when simulating the time course of signalling. When assembly of protein complexes is being included, the number of distinct molecular species can easily increase by a few orders of magnitude. Validation, visualisation and understanding the network can become intractable. Another challenge appears when the modeller needs to recast or grow a model. Keeping track of changes and adding new elements present a significant difficulty. An approach to solve these challenges within the virtual cell (VCell) is described. Using (i) automatic extraction from pathway databases of model components (http://vcell.org/biopax) and (ii) rules of interactions that serve as reaction network generators (http://vcell.org/bionetgen), a way is provided for semi-automatic generation of quantitative mathematical models that also facilitates the reuse of model elements. In this approach, kinetic models of large, complex networks can be assembled from separately constructed modules, either directly or via rules. To implement this approach, the strength of several related technologies is combined: the BioPAX ontology, the BioNetGen rule-based description of molecular interactions and the VCell modelling and simulation framework.
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Metadata
- Authors
- Blinov, M. L., Ruebenacker, O., & Moraru, I. I.
- Affiliations
- UConn Health (US)
- Venue
- IET Systems Biology
- Published
- 2008-09-15
- Volume
- 2
- Issue
- 5
- Pages
- 363-368
- ISSN
- 1751-8849 (ISSN-L)
- 1751-8857
- PMC
- PMC2675916
- OpenAlex
- W1995948702
- Cited by
- 25
- References
- 27
- Citations by year
- 2024: 2 · 2022: 2 · 2021: 3 · 2020: 1 · 2019: 1 · 2017: 1 · 2016: 1 · 2015: 2 · 2014: 1 · 2013: 2 · 2012: 1
- FWCI
- 1.10
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IET Systems Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5u54rr022232-05)
- National Institute of General Medical Sciences (R01GM076570)
- National Center for Research Resources (U54-RR022232)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- combinatorial complexity, reaction network generation, BioPAX, BioNetGen, Virtual Cell, semi-automatic model generation, model reuse, protein complex formation, kinetic model, pathway databases
- MeSH terms
- Computer Simulation, Gene Expression Regulation, Models, Biological, Models, Statistical, Signal Transduction, Proteome
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