The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Modeling and Simulation of Genetic Regulatory Systems: A Literature Review
Abstract
In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.
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Cites
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Cited by
Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling
Gardner, T. S., di Bernardo, D., Lorenz, D., & Collins, J. J. (2003) · Science · doi:10.1126/science.1081900 · Cited by 1,117
Gene regulatory network inference: Data integration in dynamic models—A review
Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., & Guthke, R. (2009) · Biosystems · doi:10.1016/j.biosystems.2008.12.004 · Cited by 755
Bonneau, R., Reiss, D. J., Shannon, P., Facciotti, M., Hood, L., Baliga, N. S., & Thorsson, V. (2006) · Genome Biology · doi:10.1186/gb-2006-7-5-r36 · Cited by 510
Qualitative simulation of genetic regulatory networks using piecewise-linear models
DEJONG, H., GOUZE, J., HERNANDEZ, C., PAGE, M., SARI, T., & GEISELMANN, J. (2004) · Bulletin of Mathematical Biology · doi:10.1016/j.bulm.2003.08.010 · Cited by 386
Self-citationReprogramming cell fates: reconciling rarity with robustness
Huang, S. (2009) · BioEssays · doi:10.1002/bies.200800189 · Cited by 320
Metadata
- Authors
- de Jong, H.
- Venue
- Journal of Computational Biology
- Publisher
- SAGE Publications
- Published
- 2002-01
- Volume
- 9
- Issue
- 1
- Pages
- 67-103
- ISSN
- 1066-5277 (ISSN-L)
- 1557-8666
- OpenAlex
- W2058221907
- Cited by
- 2,663
- References
- 8
- FWCI
- 26.67
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
