Introducing ACO+ for Modeling Gene Regulatory Networks from Microarray Data

Revealing the fundamental cellular progression through systems biology is a long felt task and application of computational models in Inferring Regulations among the genes is a suggested approach to be optimized. Various models have been used to extract the probable structure and dynamics of such ne...

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Bibliographic Details
Published in2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR) pp. 1 - 7
Main Authors Jahan, Ishrat, Showkat, Dilruba
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2018
Subjects
Ant colony optimization
Biological system modeling
Computational modeling
Data models
Gene Regulatory Network
Heuristic algorithms
Linear Time Variant Model
Microarray Data
Noise
Nonlinear dynamical systems
Optimization
Synthetic data
Systems biology
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DOI10.1109/ICIEV.2018.8641047

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Summary:Revealing the fundamental cellular progression through systems biology is a long felt task and application of computational models in Inferring Regulations among the genes is a suggested approach to be optimized. Various models have been used to extract the probable structure and dynamics of such networks from gene expression data. However, capturing the complex nonlinear system dynamics is still a big issue to be worked out. In this paper a method based on a unique algorithm named ACO+ has been proposed for reverse engineering Gene Regulatory Network (GRN) from microarray data using the biologically relevant optimization algorithm Ant Colony Optimization (ACO), with an enhancement by incorporating two genetic operators, crossover and mutation. The Linear Time Variant (LTV) Model, being simple and able to simulate also nonlinear system dynamics, has been used for modeling the GRN in a fitted way. Attempting ACO+ to model GRN is of its first kind and optimization has been carried out using ACO and the variants ACODE (ACO followed by Differential Evolution), and ACO+ with synthetic noise free and noise in data. The applicability of the proposed method has been tested on synthetic datasets as well as real expression data set of SOS DNA repair system in Escherichia coli. Of the algorithms attempted, ACO+ simulated the best optimization.
DOI:10.1109/ICIEV.2018.8641047