Using bees hill flux balance analysis (BHFBA) for in silico microbial strain optimization

Microbial strains can be manipulated to improve product yield and improve growth characteristics. Optimization algorithms are developed to identify the effects of gene knockout on the results. However, this process is often faced the problem of being trapped in local minima and slow convergence due...

Full description

Saved in:
Bibliographic Details
Main Authors: Yee, Wen Choon, Mohamad, Mohd. Saberi, Deris, Safaai, Md. Illias, Rosli, Lian, En Chai, Chuii, Khim Chong
Format: Conference or Workshop Item
Published: 2013
Subjects:
Online Access:http://eprints.utm.my/id/eprint/51385/
https://doi.org/10.1007/978-3-642-36546-1_39
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Teknologi Malaysia
id my.utm.51385
record_format eprints
spelling my.utm.513852017-09-18T01:59:46Z http://eprints.utm.my/id/eprint/51385/ Using bees hill flux balance analysis (BHFBA) for in silico microbial strain optimization Yee, Wen Choon Mohamad, Mohd. Saberi Deris, Safaai Md. Illias, Rosli Lian, En Chai Chuii, Khim Chong QA Mathematics Microbial strains can be manipulated to improve product yield and improve growth characteristics. Optimization algorithms are developed to identify the effects of gene knockout on the results. However, this process is often faced the problem of being trapped in local minima and slow convergence due to repetitive iterations of algorithm. In this paper, we proposed Bees Hill Flux Balance Analysis (BHFBA) which is a hybrid of Bees Algorithm, Hill Climbing Algorithm and Flux Balance Analysis to solve the problems and improve the performance in predicting optimal sets of gene deletion for maximizing the growth rate and production yield of desired metabolite. Escherichia coli is the model organism in this paper. The list of knockout genes, growth rate and production yield after the deletion are the results from the experiments. BHFBA performed better in term of computational time, stability and production yield. 2013 Conference or Workshop Item PeerReviewed Yee, Wen Choon and Mohamad, Mohd. Saberi and Deris, Safaai and Md. Illias, Rosli and Lian, En Chai and Chuii, Khim Chong (2013) Using bees hill flux balance analysis (BHFBA) for in silico microbial strain optimization. In: Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics). https://doi.org/10.1007/978-3-642-36546-1_39
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA Mathematics
spellingShingle QA Mathematics
Yee, Wen Choon
Mohamad, Mohd. Saberi
Deris, Safaai
Md. Illias, Rosli
Lian, En Chai
Chuii, Khim Chong
Using bees hill flux balance analysis (BHFBA) for in silico microbial strain optimization
description Microbial strains can be manipulated to improve product yield and improve growth characteristics. Optimization algorithms are developed to identify the effects of gene knockout on the results. However, this process is often faced the problem of being trapped in local minima and slow convergence due to repetitive iterations of algorithm. In this paper, we proposed Bees Hill Flux Balance Analysis (BHFBA) which is a hybrid of Bees Algorithm, Hill Climbing Algorithm and Flux Balance Analysis to solve the problems and improve the performance in predicting optimal sets of gene deletion for maximizing the growth rate and production yield of desired metabolite. Escherichia coli is the model organism in this paper. The list of knockout genes, growth rate and production yield after the deletion are the results from the experiments. BHFBA performed better in term of computational time, stability and production yield.
format Conference or Workshop Item
author Yee, Wen Choon
Mohamad, Mohd. Saberi
Deris, Safaai
Md. Illias, Rosli
Lian, En Chai
Chuii, Khim Chong
author_facet Yee, Wen Choon
Mohamad, Mohd. Saberi
Deris, Safaai
Md. Illias, Rosli
Lian, En Chai
Chuii, Khim Chong
author_sort Yee, Wen Choon
title Using bees hill flux balance analysis (BHFBA) for in silico microbial strain optimization
title_short Using bees hill flux balance analysis (BHFBA) for in silico microbial strain optimization
title_full Using bees hill flux balance analysis (BHFBA) for in silico microbial strain optimization
title_fullStr Using bees hill flux balance analysis (BHFBA) for in silico microbial strain optimization
title_full_unstemmed Using bees hill flux balance analysis (BHFBA) for in silico microbial strain optimization
title_sort using bees hill flux balance analysis (bhfba) for in silico microbial strain optimization
publishDate 2013
url http://eprints.utm.my/id/eprint/51385/
https://doi.org/10.1007/978-3-642-36546-1_39
_version_ 1643653024676052992