Inference of biochemical S-systems via mixed-variable multiobjective evolutionary optimization
type: Item
instance of: scholarly article
title: Inference of biochemical S-systems via mixed-variable multiobjective evolutionary optimization
zbMATH Open document ID: 1382.92124
DOI: 10.1155/2017/3020326
author: Yu Chen
author: D. Chen
author: Xiufen Zou
published in: Computational \& Mathematical Methods in Medicine
publication date: 2018-03-15T00:00:00Z
review text: Summary: Inference of the biochemical systems (BSs) via experimental data is important for understanding how biochemical components in vivo interact with each other. However, it is not a trivial task because BSs usually function with complex and nonlinear dynamics. As a popular ordinary equation (ODE) model, the S-System describes the dynamical properties of BSs by incorporating the power rule of biochemical reactions but behaves as a challenge because it has a lot of parameters to be confirmed. This work is dedicated to proposing a general method for inference of S-Systems by experimental data, using a biobjective optimization (BOO) model and a specially mixed-variable multiobjective evolutionary algorithm (mv-MOEA). Regarding that BSs are sparse in common sense, we introduce binary variables indicating network connections to eliminate the difficulty of threshold presetting and take data fitting error and the \(L_0\)-norm as two objectives to be minimized in the BOO model. Then, a selection procedure that automatically runs tradeoff between two objectives is employed to choose final inference results from the obtained nondominated solutions of the mv-MOEA. Inference results of the investigated networks demonstrate that our method can identify their dynamical properties well, although the automatic selection procedure sometimes ignores some weak connections in BSs.
Mathematics Subject Classification ID: 92C40
Mathematics Subject Classification ID: 92C42
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 6850512
zbMATH Keywords: S-systems
zbMATH Keywords: biobjective optimization
zbMATH Keywords: mixed-variable multiobjective evolutionary algorithm
Wikidata QID: Q33760229
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/3020326
OpenAlex ID: W2616450260
cites work: Autoregressive models for gene regulatory network inference: sparsity, stability and causality issues
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cites work: Evolutionary Multi-Criterion Optimization
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DBLP publication ID: journals/cmmm/ChenCZ17
description: scientific article; zbMATH DE number 6850512