Cellular processes like metabolism, responses to the actions or surroundings and reproduction of cells are controlled by proteins. Genes are responsible for the synthesis of a protein. Some genes synthesize proteins which control the rate at which other genes synthesize protein and form the network of interactions between the genes named as Gene Regulatory Networks (GRNs). GRNs are the control systems which represents the causal relationships between genes, protein-protein interactions, etc. They provide a very useful contribution to cellular biology, mechanics of various harmful diseases like cancer, help in drug discovery and impact of those drugs on the individuals. Large amount of microarray gene expression datasets are available that can be used to analyse the relationships between the genes. These datasets are imprecise and uncertain because of the noisy and missing values in gene expression datasets. Fuzzy logic based models are capable of handling uncertainty of data which provide the valuable contribution in the inference of GRNs. To address this most challenging area of cellular biology, this paper reviews various fuzzy logic based techniques to infer GRNs from microarray gene expression datasets. The main objective of this review paper is to present, analyse and compare contributions given by researchers in this field.

Fuzzy Logic, Genetic Regulatory Networks, Microarray gene expression data, Clustering, GRN Inference

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