Editing Gene regulatory network (section)

==Bacterial regulatory networks==
Regulatory networks allow [[bacteria]] to adapt to almost every environmental niche on earth.<ref name= FillouxAAM>{{cite book |veditors = [[Alain Filloux|Filloux AA]]| year=2012 | title=Bacterial Regulatory Networks | publisher=[[Caister Academic Press]] | isbn= 978-1-908230-03-4}}</ref><ref name= GrossRBeierD>{{cite book | veditors = Gross R, Beier D | year=2012 | title=Two-Component Systems in Bacteria | publisher=[[Caister Academic Press]] | isbn= 978-1-908230-08-9}}</ref> A network of interactions among diverse types of molecules including DNA, RNA, proteins and metabolites, is utilised by the bacteria to achieve regulation of gene expression. In bacteria, the principal function of regulatory networks is to control the response to environmental changes, for example nutritional status and environmental stress.<ref name= RequenaJM>{{cite book | veditors = Requena JM | year=2012 | title=Stress Response in Microbiology | publisher=[[Caister Academic Press]] | isbn= 978-1-908230-04-1}}</ref> A complex organization of networks permits the microorganism to coordinate and integrate multiple environmental signals.<ref name="FillouxAAM"/>

One example stress is when the environment suddenly becomes poor of nutrients. This triggers a complex adaptation process in [[bacteria]], such as ''[[Escherichia coli|E. coli]].'' After this environmental change, thousands of genes change expression level. However, these changes are predictable from the topology and logic of the gene network<ref name=":3">{{cite journal | vauthors = Almeida BL, Bahrudeen MN, Chauhan V, Dash S, Kandavalli V, Häkkinen A, Lloyd-Price J, Palma CS, Baptista IS, Gupta A, Kesseli J | display-authors = 6 | title = The transcription factor network of E. coli steers global responses to shifts in RNAP concentration | journal = Nucleic Acids Research | volume = 50 | issue = 12 | pages = 6801–6819 | date = June 2022 | pmid = 35748858 | pmc = 9262627 | doi = 10.1093/nar/gkac540 }}</ref> that is reported in [[RegulonDB]]. Specifically, on average, the response strength of a gene was predictable from the difference between the numbers of activating and repressing input transcription factors of that gene.<ref name=":3" />