Reverse engineering of gene regulatory networks based on S-systems and Bat algorithm

Abstract

The correct inference of gene regulatory networks for the understanding of the intricacies of the complex biological regulations remains an intriguing task for researchers. With the availability of large dimensional microarray data, relationships among thousands of genes can be simultaneously extracted. Among the prevalent models of reverse engineering genetic networks, S-system is considered to be an efficient mathematical tool. In this paper, Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters. A decoupled S-system has been implemented to reduce the complexity of the algorithm. Initially, the proposed method has been successfully tested on an artificial network with and without the presence of noise. Based on the fact that a real-life genetic network is sparsely connected, a novel Accumulative Cardinality based decoupled S-system has been proposed. The cardinality has been varied from zero up to a maximum value, and this model has been implemented for the reconstruction of the DNA SOS repair network of Escherichia coli. The obtained results have shown significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods.

Elephant swarm water search algorithm for global optimization

“…Moreover, we perform comparison among ESWSA and other algorithms and statistical analysis of the simulation results. For comparison purpose, we have selected some wellknown optimization methods, namely BA [16,45,46], CS [17,47,48], FPA [18,49] and PSO [15,43,44]. For all algorithms, population and maximum iteration number are set to 50 and 2000, respectively.…”

“…2. For BA, loudness decreasing factor (a), pulse rate decreasing factor (c), minimum and maximum frequency are set to 0.9, 0.9, 0 and 1, respectively, based on earlier work [16,45,46]. 3.…”

Infer Gene Regulatory Network Based on the Novel Classifiers Fusion

“…Transcriptional regulation is a basis of many crucial molecular processes such as oscillator, differentiation and homeostasis, and the correct inference of gene regulatory networks (GRN) is a helpful and essential task to understand the intricacies of the complex biological regulations and gain insights into biological processes of interest in systems biology for researchers [1][2][3]. With the availability of large dimensional microarray data and lots of true regulation relationships of biology processes which have been verified by biology experiments, relationships among thousands of genes could be inferred simultaneously [4][5][6][7][8].…”

Recurrent Neural Network Based Modeling of Gene Regulatory Network Using Bat Algorithm

“…A Dynamic Bayesian network [6,7] makes conditional probabilistic transitions between network states that merge the features of Hidden Markov model to include the feedback. S-system [8][9][10][11][12][13] is also a popular model of Biochemical System Theory, represents a GRN as a set of differential equation with power law function. Neural Network [14,15] along with GA was also proposed to infer GRN successfully.…”