Current approaches to gene regulatory network modelling
By Thomas Schlitt, Alvis Brazma. BMC Bioinformatics, 2007
351 citations recorded by the source corpus.
Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.
Subfields: Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/67fc060379e9fe6a14b6a5b96f50b956dca74b0c
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In Genetics
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Citations in the corpus
- AGRIS: the Arabidopsis Gene Regulatory Information Server, an update (2010, background): “A first step toward elucidating the dynamic behavior of gene regulatory networks involves compiling the parts lists, formed by the transcription factors (TFs), all the gene regulatory regions and the corresponding interactions (1).”
- The mathematics of tanning (2009, neutral): “Clusters of co-regulated genes lead to new phase-specific control elements As a basis of our large-scale regulatory network, we sought out to construct a parts-list (as defined in [14]) of statistically significant cell cycle components such as a large number of co-regulated gene clusters and their ”
- Hive plots - rational approach to visualizing networks (2012, background): “transcription and regulation [4], protein interactions [5, 6], metabolic pathways [7, 8], genetic basis of”
- Consistency of community detection in networks under degree-corrected stochastic block models (2011, background): “The applications are many and varied, including social networks [31, 37], gene regulatory networks [33], recommender systems and security monitoring.”
- Systems medicine: the future of medical genomics and healthcare (2009, neutral): “protein-protein and protein-DNA interactions [97-100].”