Editing Gene regulatory network (section)

=== Continuous networks ===

Continuous network models of GRNs are an extension of the Boolean networks described above. Nodes still represent genes and connections between them regulatory influences on gene expression. Genes in biological systems display a continuous range of activity levels and it has been argued that using a continuous representation captures several properties of gene regulatory networks not present in the Boolean model.<ref>{{cite journal | vauthors = Vohradsky J | title = Neural model of the genetic network | journal = The Journal of Biological Chemistry | volume = 276 | issue = 39 | pages = 36168–36173 | date = September 2001 | pmid = 11395518 | doi = 10.1074/jbc.M104391200 | doi-access = free }}

</ref> Formally most of these approaches are similar to an [[artificial neural network]], as inputs to a node are summed up and the result serves as input to a [[sigmoid function]], e.g.,<ref>{{cite journal | vauthors = Geard N, Wiles J | title = A gene network model for developing cell lineages | journal = Artificial Life | volume = 11 | issue = 3 | pages = 249–267 | year = 2005 | pmid = 16053570 | doi = 10.1162/1064546054407202 | s2cid = 8664677 | citeseerx = 10.1.1.1.4742 }}</ref> but proteins do often control gene expression in a synergistic, i.e. non-linear, way.<ref>{{cite web |vauthors=Schilstra MJ, Bolouri H |title=Modelling the Regulation of Gene Expression in Genetic Regulatory Networks |date=2 January 2002 |publisher=Biocomputation group, University of Hertfordshire |url=http://strc.herts.ac.uk/bio/maria/NetBuilder/Theory/NetBuilderModelling.htm |url-status=dead |archive-url=https://web.archive.org/web/20071013022705/http://strc.herts.ac.uk/bio/maria/NetBuilder/Theory/NetBuilderModelling.htm |archive-date=13 October 2007 |df=dmy }}</ref> However, there is now a continuous network model<ref>{{cite conference |vauthors=Knabe JF, Nehaniv CL, Schilstra MJ, Quick T |title=Evolving Biological Clocks using Genetic Regulatory Networks |book-title=Proceedings of the Artificial Life X Conference (Alife 10) |pages=15–21 |publisher=MIT Press |year=2006 |citeseerx = 10.1.1.72.5016 }}</ref> that allows grouping of inputs to a node thus realizing another level of regulation. This model is formally closer to a higher order [[recurrent neural network]]. The same model has also been used to mimic the evolution of [[cellular differentiation]]<ref>{{cite conference |vauthors=Knabe JF, Nehaniv CL, Schilstra MJ |title=Evolutionary Robustness of Differentiation in Genetic Regulatory Networks |book-title=Proceedings of the 7th German Workshop on Artificial Life 2006 (GWAL-7) |pages=75–84 |publisher=[[Akademische Verlagsgesellschaft AKA]] |year=2006 |location=Berlin |citeseerx = 10.1.1.71.8768 }}</ref> and even multicellular [[morphogenesis]].<ref>{{cite conference |vauthors=Knabe JF, Schilstra MJ, Nehaniv CL |title=Evolution and Morphogenesis of Differentiated Multicellular Organisms: Autonomously Generated Diffusion Gradients for Positional Information |book-title=Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems |publisher=MIT Press |year=2008 |url=http://panmental.de/papers/FlagPottsGRNALife11.pdf }}</ref>