Using Bees Hill Flux Balance Analysis (BHFBA) for in silico microbial strain optimization

Yee Wen Choon; Mohd Saberi Bin Mohamad; Safaai Deris; Rosli Md Illias; Lian En Chai; Chuii Khim Chong

doi:10.1007/978-3-642-36546-1_39

Using Bees Hill Flux Balance Analysis (BHFBA) for in silico microbial strain optimization

Yee Wen Choon, Mohd Saberi Bin Mohamad, Safaai Deris, Rosli Md Illias, Lian En Chai, Chuii Khim Chong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings
Pages	375-384
Number of pages	10
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-36546-1_39
Publication status	Published - 2013
Externally published	Yes
Event	5th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2013 - Kuala Lumpur, Malaysia Duration: Mar 18 2013 → Mar 20 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7802 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2013
Country/Territory	Malaysia
City	Kuala Lumpur
Period	3/18/13 → 3/20/13

Keywords

Bees Algorithm
Flux Balance Analysis
Hill Climbing
Microbial Strains
Optimization

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-36546-1_39

Cite this

Choon, Y. W., Bin Mohamad, M. S., Deris, S., Illias, R. M., Chai, L. E., & Chong, C. K. (2013). Using Bees Hill Flux Balance Analysis (BHFBA) for in silico microbial strain optimization. In Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings (PART 1 ed., pp. 375-384). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7802 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-36546-1_39

Using Bees Hill Flux Balance Analysis (BHFBA) for in silico microbial strain optimization. / Choon, Yee Wen; Bin Mohamad, Mohd Saberi; Deris, Safaai et al.
Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings. PART 1. ed. 2013. p. 375-384 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7802 LNAI, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Choon, YW, Bin Mohamad, MS, Deris, S, Illias, RM, Chai, LE & Chong, CK 2013, Using Bees Hill Flux Balance Analysis (BHFBA) for in silico microbial strain optimization. in Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7802 LNAI, pp. 375-384, 5th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2013, Kuala Lumpur, Malaysia, 3/18/13. https://doi.org/10.1007/978-3-642-36546-1_39

Choon YW, Bin Mohamad MS, Deris S, Illias RM, Chai LE, Chong CK. Using Bees Hill Flux Balance Analysis (BHFBA) for in silico microbial strain optimization. In Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings. PART 1 ed. 2013. p. 375-384. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-36546-1_39

Choon, Yee Wen ; Bin Mohamad, Mohd Saberi ; Deris, Safaai et al. / Using Bees Hill Flux Balance Analysis (BHFBA) for in silico microbial strain optimization. Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings. PART 1. ed. 2013. pp. 375-384 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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AU - Illias, Rosli Md

AU - Chai, Lian En

AU - Chong, Chuii Khim

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AB - 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.

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Using Bees Hill Flux Balance Analysis (BHFBA) for in silico microbial strain optimization

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Keywords

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