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Development of a Differential Evolutionary Algorithm Application in Optimizing Microbial Metabolic System

by Adel Maghsoudpour, Ali Ghaffari, Mohammad Teshnehlab
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 35 - Number 9
Year of Publication: 2011
Authors: Adel Maghsoudpour, Ali Ghaffari, Mohammad Teshnehlab
10.5120/4427-6164

Adel Maghsoudpour, Ali Ghaffari, Mohammad Teshnehlab . Development of a Differential Evolutionary Algorithm Application in Optimizing Microbial Metabolic System. International Journal of Computer Applications. 35, 9 ( December 2011), 5-11. DOI=10.5120/4427-6164

@article{ 10.5120/4427-6164,
author = { Adel Maghsoudpour, Ali Ghaffari, Mohammad Teshnehlab },
title = { Development of a Differential Evolutionary Algorithm Application in Optimizing Microbial Metabolic System },
journal = { International Journal of Computer Applications },
issue_date = { December 2011 },
volume = { 35 },
number = { 9 },
month = { December },
year = { 2011 },
issn = { 0975-8887 },
pages = { 5-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume35/number9/4427-6164/ },
doi = { 10.5120/4427-6164 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:21:30.763616+05:30
%A Adel Maghsoudpour
%A Ali Ghaffari
%A Mohammad Teshnehlab
%T Development of a Differential Evolutionary Algorithm Application in Optimizing Microbial Metabolic System
%J International Journal of Computer Applications
%@ 0975-8887
%V 35
%N 9
%P 5-11
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Advancement of powerful biological technology has caused to achievement to numerous omic data that possibility of using algorithmic methods in analysis and optimizing of biological system has provided beside advancement of calculative biology. In this study, optimizing calculative instrument of microbial metabolism is extended on base of differential evolutionary algorithm with vision from bi-level optimizing functions. The outcome algorithm has been used for optimizing of succinic acid microbial production. The result shows the algorithm can reproduce scenario of metabolic engineer in less calculative time which were previously produced by other bi-level microbial optimizing methods, on the base of linear programming. Also the algorithm has adjusting parameters so that user has the capability of collation and adjustment with studying problem. In addition, it provided possibility of using non-linear goal function in optimizing on base of differential evolutionary algorithm and also possibility of finding strategy of metabolic engineer that cause to efficiency of optimizing production in microbial system.

References
  1. Kacser H, Burns JA. 1973. The control of flux. Symp Soc Exp Biol 27: 65–104.
  2. Heinrich R, Rapoport TA. 1974. A linear steady-state treatment of enzymatic chains. Eur J Biochem 42:89–95.
  3. Hatzimanikatis V, Emmerling M, Sauer U, Bailey, JE. 1998. Application of mathematical tools for metabolic design of microbial ethanol production. Biotechnol Bioeng 58:154–161.
  4. Savageau MA. 1969a. Biochemical systems analysis. I. Some mathematical properties of the arte law for the component enzymatic reactions. J Theor Biol 25:365–369.
  5. Voit EO. 1992. Optimization of integrated biochemical systems. Biotechnol Bioeng 40:572–582.
  6. Regan L, Bogle IDL, Dunnill P. 1993. Simulation and optimization of metabolic pathways. Comput Chem Eng 17:627–637.
  7. Torres NV, Voit EO, Gonzales-Alcon C. 1996. Optimization of nonlinear biotechnological processes with linear programming: application to citric acid production by Aspergillus niger. Biotechnol Bioeng 49: 247–258.
  8. Price ND, Papin JA, Schilling CH, Palsson BO: Genome-scale microbial in silico models: the constraints-based approach. Trends in Biotechnology 2003, 21:162-169.
  9. Patil KR, Akesson M, Nielsen J: Use of genome-scale microbial models for metabolic engineering. Current Opinion in Biotechnology 2004, 15:64-69.
  10. Schilling CH, Letscher D, Palsson BO: Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective.
  11. Schuster S, Fell DA, Dandekar T: A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks.
  12. Kauffman KJ, Prakash P, Edwards JS: Advances in flux balance analysis. Curr Opin Biotechnol 2003, 14:491-496.
  13. Fell DA, Small JR: Fat synthesis in adipose tissue. An examination of stoichiometric constraints. Biochem J 1986, 238:781-786.
  14. Ibarra RU, Edwards JS, Palsson BO: Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.
  15. Edwards JS, Ibarra RU, Palsson BO: In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data.
  16. Burgard AP, Maranas CD: Optimization-based framework for inferring and testing hypothesized metabolic objective functions.
  17. Famili I, Forster J, Nielsen J, Palsson BO: Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. PNAS 2003, 100:13134-13139.
  18. Henriksen CM, Christensen LH, Nielsen J, Villadsen J. 1996. Growth energetics and metabolic fluxes in continuous cultures of Penicillium chrysogenum. J Biotechnol 45:149–164.
  19. Pons A, Dussap C, Pequignot C, et al. 1996. Metabolic flux distribution in Cornybacterium melassecola ATCC 17965 for various carbon sources. Biotechnol Bioeng 51:177–189.
  20. Varma A, Boesch BW, Palsson BO. 1993a. Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates. Appl Environ Microb 59:2465–2473.
  21. Delgado J, Liao JC. 1997. Inverse flux analysis for reduction of acetate excretion in Escherichia coli. Biotechnol Progr 13:361–367.
  22. Xie L, Wang D. 1994a. Applications if improved stoichiometric model in medium design and fed-batch cultivation of animal cells in bioreactor. Cytotechnology 15:17–29.
  23. Xie L, Wang D. 1994b. Stoichiometric analysis of animal cell growth and its application in medium design. Biotechnol Bioeng 43:1164–1174.
  24. Xie L,Wang D. 1996a. Material balance studies on animal cell metabolism using stoichiometrically based reaction network. Biotechnol Bioeng 52:579–590.
  25. Xie L,Wang D. 1996b. Energy metabolism and ATP balance in animal cell cultivation using a stoichiometrically based reaction network. Biotechnol Bioeng 52:591–601.
  26. Xie L, Wang D. 1996c. High cell density and high monoclonal antibody production through medium design and rational control in a bioreactor. Biotechnol Bioeng 51:725–729.
  27. Xie L, Wang D. 1997. Integrated approaches to the design of media and feeding strategies for fed-batch cultures of animal cells. Trends Biotechnol 15:109–113.
  28. In silico metabolic pathway analysis and design: succinic acid production by metabolically engineered Escherichia coli as an example. Genome Inform. 2002;13:214-23.
  29. In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol Bioeng. 2005 Sep 5;91(5):643-8.
  30. Genome-scale in silico aided metabolic analysis and flux comparisons of Escherichia coli to improve succinate production. Appl Microbiol Biotechnol. 2006 Dec;73(4):887-94. Epub 2006 Aug 23.
  31. Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiol. 2005 Jun 27;5:39.
  32. In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production. Metab Eng. 2006 Mar;8(2):102-11. Epub 2005 Nov 10.
  33. Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene knockout targets. Nat Biotechnol. 2005 May;23(5):612-6. Epub 2005 Apr 10.
  34. Genome-scale analysis of Mannheimia succiniciproducens metabolism. Biotechnol Bioeng. 2007 Jul 1;97(4):657-71.
  35. Patil KR, Nielsen J: Uncovering transcriptional regulation of metabolism by using metabolic network topology. PNAS 2005, 102:2685-2689.
  36. Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, Bumgarner R, Goodlett DR, Aebersold R, Hood L: Integrated genomic and proteomic analyses of a systematically perturbed metabolic network.
  37. Flores N, Xiao J, Berry A, Bolivar F, Valle F. 1996. Pathway engineering for the production of aromatic compounds in Escherichia coli. Nat Biotechnol 14:620– 623.
  38. Chandran SS, Yi J, Draths KM, Von Daeniken R, Weber W, Frost JW. 2003. Phosphoenolpyruvate availability and the biosynthesis of shikimic acid. Biotechnol Progr 19:808– 814.
  39. Burgard AP, Pharkya P, Maranas CD: Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 2003, 84:647-657.
  40. Pharkya P, Burgard AP, Maranas CD: OptStrain: a computational framework for redesign of microbial production systems. Genome Res 2004, 14:2367-2376.
  41. Stephanopoulos G, Vallino JJ. 1991. Network rigidity and metabolic engineering in metabolite overproduction. Science 252:1675– 1681.
  42. Ikeda M. 2003. Amino acid production processes. Adv Biochem Eng Biotechnol 79:1– 35.
  43. Nathan D. Price1, Jennifer L. Reed1 & Bernhard Ø. Palsson, Genome-scale models of microbial cells: evaluating the consequences of constraints. Nature Reviews Microbiology 2, 886-897.Varma A, Palsson BO: Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol. 1994 Oct;60(10):3724-31.
  44. Reed JL, Vo TD, Schilling CH, Palsson BO. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 2003;4(9):R54. Epub 2003 Aug 28.
Index Terms

Computer Science
Information Sciences

Keywords

Differential evolutionary algorithm Optimizing microbial metabolism Metabolic modeling

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