Gene regulatory network
Some proteins though serve only to activate other genes, and these are the transcription factors that are the main players in regulatory networks or cascades.
[2] This process, which we associate with wine-making, is how the yeast cell makes its living, gaining energy to multiply, which under normal circumstances would enhance its survival prospects.
Less understood is the mechanism of epigenetics by which chromatin modification may provide cellular memory by blocking or allowing transcription.
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.
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.
[6] 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-seq signal of histone modification are more correlated with transcription factor motifs at promoters in comparison to RNA level.
[7] 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.
This suggests that the Hippo signaling pathway operates as a conserved regulatory module that can be used for multiple functions depending on context.
The second way networks can evolve is by changing the strength of interactions between nodes, such as how strongly a transcription factor may bind to a cis-regulatory element.
[15] The enriched motifs have been proposed to follow convergent evolution, suggesting they are "optimal designs" for certain regulatory purposes.
[21] Following the convergent evolution hypothesis, the enrichment of feed-forward loops would be an adaptation for fast response and noise resistance.
A recent research found that yeast grown in an environment of constant glucose developed mutations in glucose signaling pathways and growth regulation pathway, suggesting regulatory components responding to environmental changes are dispensable under constant environment.
[23] In other words, gene regulatory networks can evolve to a similar structure without the specific selection on the proposed input-output behavior.
De novo evolution of coherent type 1 feed-forward loops has been demonstrated computationally in response to selection for their hypothesized function of filtering out a short spurious signal, supporting adaptive evolution, but for non-idealized noise, a dynamics-based system of feed-forward regulation with different topology was instead favored.
[26][27] 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.
[28] A complex organization of networks permits the microorganism to coordinate and integrate multiple environmental signals.
[29] It is common to model such a network with a set of coupled ordinary differential equations (ODEs) or SDEs, describing the reaction kinetics of the constituent parts.
are usually chosen as low-order polynomials or Hill functions that serve as an ansatz for the real molecular dynamics.
, one obtains (possibly several) concentration profiles of proteins and mRNAs that are theoretically sustainable (though not necessarily stable).
The following example illustrates how a Boolean network can model a GRN together with its gene products (the outputs) and the substances from the environment that affect it (the inputs).
[36] However, there is now a continuous network model[37] that allows grouping of inputs to a node thus realizing another level of regulation.
Thus, many authors are now using the stochastic formalism, after the work by Arkin et al.[42] Works on single gene expression[43] and small synthetic genetic networks,[44][45] such as the genetic toggle switch of Tim Gardner and Jim Collins, provided additional experimental data on the phenotypic variability and the stochastic nature of gene expression.
The first versions of stochastic models of gene expression involved only instantaneous reactions and were driven by the Gillespie algorithm.
In other recent work, multiscale models of gene regulatory networks have been developed that focus on synthetic biology applications.
Simulations have been used that model all biomolecular interactions in transcription, translation, regulation, and induction of gene regulatory networks, guiding the design of synthetic systems.
Also, artificial neural networks omit using a hidden layer so that they can be interpreted, losing the ability to model higher order correlations in the data.
Gene regulatory network (GRN) plays a vital role to understand the disease mechanism across these three different multiple sclerosis classes.
