Gill, Jaskaran, Chetty, Madhu, Shatte, Adrian, and Hallinan, Jennifer (2022) Integrating steady-state and dynamic gene expression data for improving genetic network modelling. In: Proceedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology. From: CIBCB 2022: IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 15-17 August 2022, Ottawa, Canada.

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Abstract

Reverse engineering of Gene Regulatory Networks (GRNs) from experimentally obtained high-throughput data is an active and promising area of research. Among several modelling techniques, the S-System model, a set of tightly coupled differential equations, mimics the complexities and dynamics of biochemical systems, and thus provides realistic GRN representation. While it offers mathematical flexibility and biological relevance, the high number of learning parameters can lead to a computational burden. In our earlier work, we addressed this issue by judicious use of prior knowledge. However, another major cause of computational load is the need for numerical integration of the differential equations for the estimation of S-system model parameters. In this paper, we propose a method to obtain initial model parameter values from the steady state of the system, thereby computing simpler and less complex algebraic equations compared to the regular differential equations of S-systems. These network parameters are input as prior knowledge for the optimization of the dynamic S-System using differential equations. The proposed framework includes a novel fitness evaluation for steady-state S-System models, a novel evolutionary parameter learning framework, and a technique to incorporate the candidate solutions in dynamic S-System modelling. Our proposed methodology reached optimal model parameter values quickly, requiring only one-third of the fitness function evaluations, compared to our previously reported DRNI (Dynamically regulated network initialization) method for S-System modelling.

Item ID:	81624
Item Type:	Conference Item (Research - E1)
ISBN:	9781665484626
Keywords:	Knowledge engineering; Computational modeling; Biological system modeling; Differential equations; Mathematical models; Data models; Steady-state
Copyright Information:	Copyright © 2022, IEEE
Date Deposited:	14 Feb 2024 23:33
FoR Codes:	31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310202 Biological network analysis @ 60% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 40%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280102 Expanding knowledge in the biological sciences @ 100%
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