Gene Regulatory Network Identification from Gene Expression Time Series Data Using Swarm Intelligence

A Gene Regulatory Network (GRN) usually is modelled as a directed graph, where the nodes represent genes and the directed arc from a given node i to node j represents the causal influence of gene i over gene j. The causal influence represented by an arc is enumerated by a signed weight associated with that arc. In this article, we model GRN by a recurrent fuzzy neural network, and attempt to identify the signed weights from the time response data of the gene micro-array. A cost function has been constructed to describe the weight identification as an optimization problem, and Particle Swarm Optimization algorithm has been used to optimize the cost function. The fuzzy membership distribution used to model network weights enhances search efficiency and hence computational overhead in the identification problem. Because of the nonlinearity in causal relationship between genes, there exist multiple solutions to the weight identification problem of GRN. In order to cater for the theoretical best solution, the identification problem has been decoupled into two sub-problems: i) determination of the existence/non-existence about the causal influence, and ii) determination of the sign and magnitude of the influence between any two genes of the network. The solutions obtained from these two sub-problems are then combined to accurately identify the both non-existing connections, and the sign and magnitude of weights to existing connections. Computer simulation reveals that the proposed realization outperforms the most recently reported work in this field in detecting the sign and magnitude and also the structure of the overall network.

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