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Seeding-inspired chemotaxis genetic algorithm for the inference of biological systems

Paper ID Volume ID Publish Year Pages File Format Full-Text
15092 1374 2014 16 PDF Available
Title
Seeding-inspired chemotaxis genetic algorithm for the inference of biological systems
Abstract

•A new seeding-inspired chemotaxis genetic algorithm (SCGA) is proposed.•SCGA successfully identified a thirty-gene 1800-connection system by only three steps.•A good initial start and a restricted search space are not necessary.•Seeding-inspired genetic operations largely improve population competition.•Winner-chemotaxis-induced population migration overcomes GA’s weakness.

A large challenge in the post-genomic era is to obtain the quantitatively dynamic interactive information of the important constitutes of underlying systems. The S-system is a dynamic and structurally rich model that determines the net strength of interactions between genes and/or proteins. Good generation characteristics without the need for prior information have allowed S-systems to become one of the most promising canonical models. Various evolutionary computation technologies have recently been developed for the identification of system parameters and skeletal-network structures. However, the gaps between the truncated and preserved terms remain too small. Additionally, current research methods fail to identify the structures of high dimensional systems (e.g., 30 genes with 1800 connections). Optimization technologies should converge fast and have the ability to adaptively adjust the search. In this study, we propose a seeding-inspired chemotaxis genetic algorithm (SCGA) that can force evolution to adjust the population movement to identify a favorable location. The seeding-inspired training strategy is a method to achieve optimal results with limited resources. SCGA introduces seeding-inspired genetic operations to allow a population to possess competitive power (exploitation and exploration) and a winner-chemotaxis-induced population migration to force a population to repeatedly tumble away from an attractor and swim toward another attractor. SCGA was tested on several canonical biological systems. SCGA not only learned the correct structure within only one to three pruning steps but also ensures pruning safety. The values of the truncated terms were all smaller than 10−14, even for a thirty-gene system.

Graphical abstractSCGA successfully identified a thirty-gene 1800-connection system (1732 redundant) in only three steps with truncated terms smaller than 10−14.Figure optionsDownload full-size imageDownload as PowerPoint slide

Keywords
Reverse engineering; S-system modeling; Memetic algorithm; Structure identification; Evolutionary algorithm
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 53, Part B, December 2014, Pages 292–307
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us