Abstract
In this chapter, we propose a new model for gene regulatory networks (GRN). The model incorporates more biological detail than other approaches, and is based on an artificial genome from which several products like genes, mRNA, miRNA, noncoding RNA, and proteins are extracted and connected, giving rise to a heterogeneous directed graph. We study the dynamics of the networks thus obtained, along with their topology (using degree distributions). Some considerations are made about the biological meaning of the outcome of the simulations.
TopBackground
Several models for Gene Regulatory Networks have been proposed in recent years. Because the biological processes involved in gene regulation are so highly complicated, the majority of these makes the assumption that the control of gene expression resides only in the regulation of gene transcription. Moreover, this may also be due to the nature of the most widely available microarray data (Geard, 2004; D’haeseleer, 2000). This overview is not meant to be exhaustive and we only briefly mention some of the known models. For a more extensive review and in-depth descriptions see de Jong (2002), Hasty (2001), Goncalves (2007), D’haeseleer (2000) and Geard (2004). We can classify the models that will be discussed here according to the following aspects: variables such as product concentrations are discrete, continuous or mixed; time is discrete and the update of the variables is either synchronous or asynchronous (there are, however, cases where time is continuous); space is discrete, continuous or absent (see Figure 1).
Key Terms in this Chapter
Complex Systems: are said to be systems of interacting components with no central control that display emergent global properties not present at the components level.
Consensus Sequence: is a nucleotide sequence composed of the most frequently observed base at each position among several observed sequences.
One-Mode Projection: a one-mode projection of a n-partite network is the condensation of the representation of the network by representing and connecting nodes of only one type.
Degree Distributions: the probability distribution of degrees (number of edges between a particular node and the others) in a graph.
Unipartite: graphs with only one type of vertexes as opposed to n-partite graphs.
N - partite: graphs whose set of vertices is divided into n subsets, forming such a partition that no two vertices belonging to the same subset are adjacent.
Undirected: in an undirected graph both ends of an arc are equivalent.
Skewed Distribution: a distribution is said to be skewed when one of its tails is longer than the other.