Determining the Properties of Gene Regulatory Networks from Expression Data

Determining the Properties of Gene Regulatory Networks from Expression Data

Larry S. Liebovitch (Florida Atlantic University, USA), Lina A. Shehadeh (University of Miami, USA), Viktor K. Jirsa (Florida Atlantic University, USA), Marc-Thorsten Hütt (Jacobs University, Germany) and Carsten Marr (Helmholtz Zentrum München, Germany)
DOI: 10.4018/978-1-60566-685-3.ch017
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The expression of genes depends on the physical structure of DNA, how the function of DNA is regulated by the transcription factors expressed by other genes, RNA regulation, such as that through RNA interference, and protein signals mediated by protein-protein interaction networks. We illustrate different approaches to determining information about the network of gene regulation from experimental data. First, we show that we can use statistical information of the mRNA expression values to determine the global topological properties of the gene regulatory network. Second, we show that analyzing the changes in expression due to mutations or different environmental conditions can give us information on the relative importance of the different mechanisms involved in gene regulation.
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All living things contain a “memory” of the past that explicitly defines their species and implicitly reflects the evolutionary events that led to their species. Typically, this memory is encoded in deoxyribonucleic acid, DNA, although it may also be encoded in ribonucleic acid, RNA (for retroviruses), or in epigenetic coding (such as methylation of DNA), or in three dimensional structures (such as the protein confirmations of prions). Each organism uses this memory as a blueprint to design and maintain itself. But it is not like a blueprint that we use to build buildings which is a smaller symbolic picture of the building. Rather, it is more like a computer code, which when executed generates structures that have a very different form than the code itself. But it is unlike the computer codes that we currently construct. Our computer codes execute their instructions in a preset order. However, which instructions living things execute are chosen by a multilevel cacophony of highly interacting networks.

The Central Dogma of molecular biology (Crick, 1958) was that genetic expression is a one way street from the transcription of DNA into mRNA, and then the translation of mRNA into protein. But we are now beginning to appreciate that multiple processes, both forward and backward, control and edit how the instructions of DNA are executed into the proteins that form the structure and function of cells. In this chapter we explore how networks control DNA expression, from within DNA (depending on the physical structure of DNA and the regulation that one gene exerts on another), and from outside of DNA (depending on the editing of mRNA and protein regulatory networks). We show how understanding the physics of networks can be used to devise methods of analysis that reveal the global and local organization of these networks.

Key Terms in this Chapter

Probability Density Function (PDF): The probability that values between x and x + dx are found with probability p(x).

Transcription Factor: A protein expressed by a gene that binds to DNA and regulates the expression of that gene or other genes.

Network: A set of nodes and links between them. In gene regulatory networks, the nodes are genes and the links between them are the DNA, RNA, and protein interactions between the genes.

Transcription Regulatory Network (TRN): The network of gene interactions mediated by the transcription factors expressed by genes that regulate other genes.

Degree Distribution: The statistical distribution of the number of nodes that connect to each node in a network.

Motif: A network of interactions between genes that occurs more often than those expected of purely random connections.

Sub-Net: All of the genes connected by transcription factors downstream from a given gene.

Gene Regulatory Network: The network of interactions that define how genes regulate each other through DNA, RNA, and protein interactions.

Connectivity Matrix: This matrix, wij, defines the strengths of the links between nodes i and j in a network.

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