Gill, Jaskaran, Chetty, Madhu, Shatte, Adrian, and Hallinan, Jennifer (2021) Dynamically Regulated Initialization for S-system Modelling of Genetic Networks. In: Proceedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology. From: CIBCB 2021: IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 13-15 October 2021, Melbourne, VIC, Australia.

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Abstract

Reverse engineering of gene regulatory networks through temporal gene expression data is an active area of research. Among the plethora of modelling techniques under investigation is the decoupled S-system model, which attempts to capture the non-linearity of biological systems in detail. For the model, number of parameters to be estimated are significantly high even when the network is of small or medium scale. Thus, the inference process poses a significant computational burden. In this paper, we propose: (1) a novel population initialization technique, Dynamically Regulated Prediction Initialization (DRPI), which utilises prior knowledge of biological gene expression data to create a feedback loop to produce dynamically regulated high-quality individuals for initial population; (2) an adaptive fitness function; and (3) a method for the maintenance of population diversity. The aim of this work is to reduce the computational complexity of the inference algorithm, to speed up the entire process of reverse engineering. The performance of the proposed algorithm was evaluated against a benchmark dataset and compared with other methods from earlier work. The experimental results show that we succeeded in achieving higher accuracy results in lesser fitness evaluations, considerably reducing the computational burden of the inference process.

Item ID:	81635
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-6654-0112-8
Keywords:	Feedback loop; Computational modeling; Biological system modeling; Heuristic algorithms; Sociology; Reverse engineering;Inference algorithms
Copyright Information:	© 2021 IEEE
Date Deposited:	13 Feb 2024 00:50
FoR Codes:	31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310201 Bioinformatic methods development @ 50% 31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310202 Biological network analysis @ 50%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280102 Expanding knowledge in the biological sciences @ 100%
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