Introduction
A gene regulatory network (GRN) is a collection of molecular regulators that interact with each other and other substances in the cell to govern the gene expression levels of messenger ribonucleic acid (mRNA) and proteins which, in turn, determine the function of the cell. Gene regulatory networks also play an important role in morphogenesis and the creation of body structures. The regulator can be ribonucleic acid (RNA), deoxyribonucleic acid (DNA), protein, or any combination of these two or more that form a complex and specific sequence of DNA and a transcription factor to activate that sequence. This interaction can be direct or indirect. Generally, each messenger ribonucleic acid (mRNA) molecule makes specific proteins. In some cases, this protein can be structural and accumulate at the cell membrane or within the cell, giving a particular structural property. In other cases, this protein can be an enzyme.
What Is the Structure of a Gene Regulatory Network?
Gene regulatory networks comprise a few highly connected nodes and many poorly connected nodes nested within a hierarchical regulatory regime. Gene regulatory network approximates a hierarchical scale-free network topology. This structure is believed to evolve due to the preferential attachment of duplicated genes to more highly connected genes.
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There are nodes, which are genes.
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The transcription factor (protein) is the input.
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Gene expression is the output.
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There are arrows in the network which are used to understand the interaction between the network.
How do Gene Regulatory Networks Evolve?
The gene regulatory network evolves mainly by two methods:
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The network topology can be changed by adding or subtracting genes or parts of the network and can be expressed in different contexts.
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The strength of network interaction between genes can be changed, for example, how strongly a transcription factor can bind to the cis-regulatory element.
What Is the Use of Gene Regulatory Networks?
The usage of gene regulatory networks is explained below:
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Gene regulatory networks serve as a map or blueprint of molecular interactions.
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Gene regulatory networks can guide the experimental designs for new experiments. In addition, they can help identify the parts of the molecular system that are not well detected so that dedicated perturbations can be constructed to boost their active responses.
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Subnetworks can also be used for diagnostic, predictive, or prognostic purposes.
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Gene regulatory networks can help understand the change in interactions between different physiological conditions or diseases.
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Gene regulatory networks can be utilized for designing rational drugs.
How Do Gene Regulatory Networks Work?
Gene regulatory networks are made up of thousands of DNA sequences. Inputs are the signaling pathways, and the regulatory proteins are transcription factors. The signaling pathways respond and activate the transcription factor proteins. Then the transcription factor binds to genes and makes mRNA.
How Is the Analysis and Modelling of Gene Regulatory Networks Done?
Different computational methods have been developed for the analysis of gene regulatory networks, such as:
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Boolean Network- In a boolean network, any entity can attain either an active or inactive level, and the level of each entity is updated according to the levels of several entities through a specific boolean system called the system’s state.
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Continuous Network- It is an extension of the boolean network. A continuous model allows a comparison of global state and experimental data and can be more accurate.
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Stochastic Gene Network- It works on small gene expression and synthetic gene networks.
What Is the Control System of Gene Regulatory Networks?
The gene regulatory networks control animal development. They regulate the expression of thousands of genes in the developmental process, which includes:
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The regulatory genome acts as a logical processing system.
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Casualty in the regulatory genome.
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Network substructure.
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Reengineering genomic control systems.
How Can Genetic Variation Affect the Architecture of Gene Regulatory Networks?
Genetic variation within a species has important effects on gene regulatory networks. A change in the coding sequence of a transcription factor can modify the binding site preference and sequence variation in promoters, resulting in the gain or loss of cis-regulatory elements (CREs). Gene regulatory networks are constructed to include sequence differences within a species or across species. An analysis of genetic variation in arabidopsis has revealed that an increase in the number of polymorphisms in the promoter regions of drought and cold-responsive genes compared to genes with other functions suggests the differences in cis-regulatory elements composition that may be involved in local adaptation to environmental stress. Comparing gene regulatory networks between species will likely help in identifying the points in the network where genetic variation contributes to the functional differences in a gene regulatory mechanism.
What Is the Importance of Gene Regulatory Networks?
The importance of gene regulatory networks is as follows:
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It helps in development, hormone secretion, and cellular communication.
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It helps in predicting the adverse effects of the drugs.
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It forms the classification and clustering of genes.
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It helps in studying the interaction between a pathogen and a host.
What Are the Challenges Faced in the Analysis of Gene Regulatory Networks?
The analysis and modeling of gene regulatory networks can be faced with a few difficulties, such as:
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First, it is difficult to predict the regulation of genes in a network.
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The participation of proteins in the metabolic pathways and their interaction is difficult to understand.
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It can be difficult to obtain and represent the knowledge of gene regulatory networks.
Conclusion
Discovering gene regulatory dependencies is fundamental for understanding the mechanism responsible for the proper activity of a cell. As the complexity of the gene regulatory network increases, the need for accurate modeling techniques will also increase. Once constructed, gene regulatory networks can also be used to model the behavior of an organism. In addition to understanding gene regulation as a stand-alone process, other models for regulating other processes need to be created. An accurate and large-scale regulatory network model for medicine and biotechnology can provide a strong incentive for cooperation between experimentalists and computational scientists.
