Modeling and Simulation of Gene Regulatory Network: A Comprehensive Survey

The Gene Regulatory Network (GRN) specifies the series of regulatory interactions between different genes. A target gene is interacted by a signal which is originated from the expression of its regulator gene. A gene is known to be expressed when it synthesizes a protein and the degree of synthesizing the protein determines the level of its expression. The same gene can behave as a 'target' in one state of interaction and a 'regulator' in the next state. If there are many interacting genes in a biological system, a network can be formed out of them where the genes are treated as nodes and interaction between any two genes is treated as an edge. This network is known as Gene Regulatory Network. Simulation of GRN addresses the issue of reconstructing the network on the basis of the expression levels of the interacting genes. Various mathematical tools are used to design the system and different optimization techniques are used to find the optimal design. The process of designing starts with time-dependent (Time-series) and condition-dependent (Steady state) gene expression data, available from micro-array chips. The target gene is activated depending on the collective interactions made to it. The problem can be modeled using Neural Network and application of Fuzzy logic may improve the design. There are two issues to discuss. One is related to uncover the parameters involved in GRN called parameter estimation problem. The other is to predict the network structure step by step while learning the parameters. Applications of meta-heuristic algorithms are proved to be efficient in resolving both the issues.