An ant colony optimization based algorithm for identifying gene regulatory elements (original) (raw)

Reconstruction of Gene Regulatory Network using Modified Ant-based Algorithm

International Journal of Engineering and Advanced Technology, 2020

Healthcare is a major area of research since few years. Ample amount of biological data getting accumulated daily due to advancement in technologies. Microarray is such technology which captures expressions of thousands of genes at a time. Interactions occur among genes are represented in terms of special networkeknown as Gene Regulatory Network (GRN). It is constructed from Differentially Expressing Genes(DEFs). GRN is a graphical representation containing genes as nodes and regulatory interactions among them as edges. It helps in tracking pathways where usual gene interaction changes leading to malfunctioning of cells and results in illness. Also, now a day’s people are diagnosed with new diseases like dengue, swine flu, Nipah, Corona virus infection for which exact molecular pathways are yet to be invented through GRN. Therefore, in this paper, a nature inspired algorithm is used for reconstruction of GRN using differentially expressing genes.

An Efficient Ant Colony Algorithm for DNA Motif Finding

2014

Finding motifs in gene sequences is one of the most important problems of bioinformatics and belongs to NP-hard type. This paper proposes a new ant colony optimization algorithm based on consensus approach, in which a relax technique is applied to find the location of the motif. The efficiency of the algorithm is evaluated by comparing it with the state-of-the-art algorithms.

Gene network inference using a swarm intelligence framework

Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference - GECCO '09, 2009

In this paper, we present a framework for inferring gene regulatory networks from gene expression time series. A modelbased approach is adopted, according to which the quality of a candidate architecture is evaluated by assessing the ability of the corresponding trained model to reproduce the available dynamics. Candidate architectures are generated in the context of the ant colony optimization (ACO) meta-heuristic and model training is performed using particle swarm optimization (PSO). We propose a novel solution construction heuristic for artificial ants, based on growth and preferential attachment, in order to generate candidate structures that adhere to well-known gene network properties. Preliminary results using an artificial network demonstrate the potential of the framework to infer the underlying network architecture to a promising degree of success.

Motif Finding with Application to the Transcription Factor Binding Sites Problem

International Journal of Computer Applications, 2015

DNA sequencing of different species has resulted in the generation of huge amount of biological data. There is an increasing need to develop computational techniques to search for relevant information in the DNA data. Discovering motifs involves determining short sequence segments which have a high probability of repeated occurrences over many sequences in different species. Motifs are useful in finding transcription factor binding sites, transcriptional regulatory elements and so on. Transcription factor binding sites (TFBSs) is important for understanding the genetic regulatory system. Our method is based on the Ant Colony Optimization (ACO) and Gibbs sampling algorithm to discover DNA motifs (collections of TFBSs) in a set of DNA-sequences. We first applied an ACO algorithm to find a set of better candidate positions for the motif. The resultant positions are given as input to the Gibbs sampler method for calculating score for each sequence. Based on the score, motif for TF binding sites is identified.

Implementation of an ant colony system for DNA sequence optimization

Artificial Life and Robotics, 2009

DNA computation exploits the computational power inherent in molecules for information processing. However, in order to perform the computation correctly, a set of good DNA sequences is crucial. A lot of work has been carried out on designing good DNA sequences to archive a reliable molecular computation. In this article, the ant colony system (ACS) is introduced as a new tool for DNA sequence design. In this approach, the DNA sequence design is modeled as a path-finding problem, which consists of four nodes, to enable the implementation of the ACS. The results of the proposed approach are compared with other methods such as the genetic algorithm.

An ant colony algorithm for multiple sequence alignment in bioinformatics

2003

1 Introduction Swarm intelligence methods are computational techniques inspired by animals such as social insects acting together to solve complex problems. The main application of these techniques has been to combinatorial optimization problems. This paper discusses work-in-progress on the application of swarm intelligence ideas to a bioinformatics prob- lem, viz. aligning multiple protein sequences which are believed to be related.

Gene Regulatory Network Inference Using Prominent Swarm Intelligence Methods

2017

Genes are the basic blue print of life in an organism containing the physiological and behavioral characteristics. A gene regulatory network (GRN) is a set of genes, or parts of genes, that interact with each other to control a specific cell function. GRN inference is the reverse engineering approach to predict the biological network from the gene expression data. Biochemical system theory based S-System is a popular model in GRN inference and the model is defined with its different parameters. The task of S-System based GRN inference is its parameter estimation which is an optimization problem. Several studies employed Particle Swarm Optimization (PSO) and other pioneer optimization techniques to estimate S-System model. In this paper several prominent swarm intelligence (SI) techniques have been studied and adapted for S-System parameter estimation. They are Group Search Optimizer, Grey Wolf Optimizer and PSO. Proficiency of optimization techniques are compared to infer GRN from S...

Learning gene regulatory networks using the bees algorithm

Neural Computing and Applications, 2013

Learning gene regulatory networks under the threshold Boolean network model is presented. To accomplish this, the swarm intelligence technique called the bees algorithm is formulated to learn networks with predefined attractors. The resulting technique is compared with simulated annealing through simulations. The ability of the networks to preserve the attractors when the updating schemes is changed from parallel to sequential is analyzed as well. Results show that Boolean networks are not very robust when the updating scheme is changed. Robust networks were found only for limit cycle length equal to two and specific network topologies. Throughout the simulations, the bees algorithm outperformed simulated annealing, showing the effectiveness of this swarm intelligence technique for this particular application.

Gene regulatory network model identification using artificial bee colony and swarm intelligence

2012 IEEE Congress on Evolutionary Computation, 2012

Gene association/interaction networks have complex structures that provide a better understanding of mechanisms at the molecular level that govern essential processes inside the cell. The interaction mechanisms are conventionally modeled by nonlinear dynamic systems of coupled differential equations (Ssystems) adhering to the power-law formalism. Our implementation adopts an S-system that is rich enough in structure to capture the dynamics of the gene regulatory networks (GRN) of interest. A comparison of three widely used population-based techniques, namely evolutionary algorithms (EAs), local best particle swarm optimization (PSO) with random topology, and artificial bee colony (ABC) are performed in this study to rapidly identify a solution to inverse problem of GRN reconstruction for understanding the dynamics of the underlying system. A simple yet effective modification of the ABC algorithm, shortly ABC* is proposed as well and tested on the GRN problem. Simulation results on two small-size and a medium size hypothetical gene regulatory networks confirms that the proposed ABC* is superior to all other search schemes studied here.