Editing Gene regulatory network (section)

== Overview ==

At one level, biological cells can be thought of as "partially mixed bags" of biological chemicals – in the discussion of gene regulatory networks, these chemicals are mostly the [[messenger RNA]]s (mRNAs) and [[protein]]s that arise from gene expression. These mRNA and proteins interact with each other with various degrees of specificity. Some diffuse around the cell. Others are bound to [[cell membrane]]s, interacting with molecules in the environment. Still others pass through cell membranes and mediate long range signals to other cells in a multi-cellular organism. These molecules and their interactions comprise a ''gene regulatory network''. 

[[Image:DG Network in Hybrid Rice.png|thumb|right|540px|Example of a regulatory network]]

The nodes of this network can represent genes, proteins, mRNAs, protein/protein complexes or cellular processes. Nodes that are depicted as lying along vertical lines are associated with the cell/environment interfaces, while the others are free-floating and can [[diffusion|diffuse]]. Edges between nodes represent interactions between the nodes, that can correspond to individual molecular reactions between DNA, mRNA, miRNA, proteins or molecular processes through which the products of one gene affect those of another, though the lack of experimentally obtained information often implies that some reactions are not modeled at such a fine level of detail. These interactions can be inductive (usually represented by arrowheads or the + sign), with an increase in the concentration of one leading to an increase in the other, inhibitory (represented with filled circles, blunt arrows or the minus sign), with an increase in one leading to a decrease in the other, or dual, when depending on the circumstances the regulator can activate or inhibit the target node. The nodes can regulate themselves directly or indirectly, creating feedback loops, which form cyclic chains of dependencies in the topological network. The network structure is an abstraction of the system's molecular or chemical dynamics, describing the manifold ways in which one substance affects all the others to which it is connected. In practice, such GRNs are inferred from the biological literature on a given system and represent a distillation of the collective knowledge about a set of related biochemical reactions. To speed up the manual curation of GRNs, some recent efforts try to use [[text mining]], curated databases, network inference from massive data, model checking and other information extraction technologies for this purpose.<ref>{{cite journal | vauthors = Leitner F, Krallinger M, Tripathi S, Kuiper M, Lægreid A, Valencia A | title = Mining cis-regulatory transcription networks from literature. | journal = Proceedings of BioLINK SIG 2013 | date = July 2013 | pages = 5–12 }}</ref>

Genes can be viewed as nodes in the network, with input being proteins such as [[transcription factor]]s, and outputs being the level of [[gene expression]]. The value of the node depends on a function which depends on the value of its regulators in previous time steps (in the [[Boolean network]] described below these are [[Boolean functions]], typically AND, OR, and NOT). These functions have been interpreted as performing a kind of information processing within the cell, which determines cellular behavior. The basic drivers within cells are concentrations of some proteins, which determine both spatial (location within the cell or tissue) and temporal (cell cycle or developmental stage) coordinates of the cell, as a kind of "cellular memory". The gene networks are only beginning to be understood, and it is a next step for biology to attempt to deduce the functions for each gene "node", to help understand [[systems biology|the behavior of the system]] in increasing levels of complexity, from gene to signaling pathway, cell or tissue level.<ref name="pmid28186191">{{cite journal | vauthors = Azpeitia E, Muñoz S, González-Tokman D, Martínez-Sánchez ME, Weinstein N, Naldi A, Álvarez-Buylla ER, Rosenblueth DA, Mendoza L | display-authors = 6 | title = The combination of the functionalities of feedback circuits is determinant for the attractors' number and size in pathway-like Boolean networks | journal = Scientific Reports | volume = 7 | pages = 42023 | date = February 2017 | pmid = 28186191 | pmc = 5301197 | doi = 10.1038/srep42023 | bibcode = 2017NatSR...742023A }}</ref>

[[Mathematical model]]s of GRNs have been developed to capture the behavior of the system being modeled, and in some cases generate predictions corresponding with experimental observations. In some other cases, models have proven to make accurate novel predictions, which can be tested experimentally, thus suggesting new approaches to explore in an experiment that sometimes wouldn't be considered in the design of the protocol of an experimental laboratory. Modeling techniques include [[differential equation]]s (ODEs), Boolean networks, [[Petri net]]s, [[Bayesian network]]s, graphical [[Gaussian network model]]s, [[Stochastic]], and [[Process calculus|Process Calculi]].<ref>{{cite journal | vauthors = Banf M, Rhee SY | title = Computational inference of gene regulatory networks: Approaches, limitations and opportunities | journal = Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms | volume = 1860 | issue = 1 | pages = 41–52 | date = January 2017 | pmid = 27641093 | doi = 10.1016/j.bbagrm.2016.09.003 | doi-access = free }}</ref> Conversely, techniques have been proposed for generating models of GRNs that best explain a set of [[time series]] observations. Recently it has been shown that [[ChIP_sequencing|ChIP-seq]] signal of histone modification are more correlated with transcription factor motifs at promoters in comparison to RNA level.<ref name="pmid23770639">{{cite journal | vauthors = Kumar V, Muratani M, Rayan NA, Kraus P, Lufkin T, Ng HH, Prabhakar S | title = Uniform, optimal signal processing of mapped deep-sequencing data | journal = Nature Biotechnology | volume = 31 | issue = 7 | pages = 615–622 | date = July 2013 | pmid = 23770639 | doi = 10.1038/nbt.2596 | doi-access = free }}</ref> Hence it is proposed that time-series histone modification ChIP-seq could provide more reliable inference of gene-regulatory networks in comparison to methods based on expression levels.