  Correct inference of genetic regulations inside a cell is one of the greatest
challenges in post genomic era for the biologist and researchers. Several
intelligent techniques and models were already proposed to identify the
regulatory relations among genes from the biological database like time series
microarray data. Recurrent Neural Network (RNN) is one of the most popular and
simple approach to model the dynamics as well as to infer correct dependencies
among genes. In this paper, Bat Algorithm (BA) is applied to optimize the model
parameters of RNN model of Gene Regulatory Network (GRN). Initially the
proposed method is tested against small artificial network without any noise
and the efficiency is observed in term of number of iteration, number of
population and BA optimization parameters. The model is also validated in
presence of different level of random noise for the small artificial network
and that proved its ability to infer the correct inferences in presence of
noise like real world dataset. In the next phase of this research, BA based RNN
is applied to real world benchmark time series microarray dataset of E. coli.
The results prove that it can able to identify the maximum number of true
positive regulation but also include some false positive regulations.
Therefore, BA is very suitable for identifying biological plausible GRN with
the help RNN model.
