Abstract
Metabolic fluxes are the key determinants of cellular metabolism. They are estimated from stable isotope labeling experiments and provide the informative parameters in evaluating cell physiology and causes of disease. Metabolic flux analysis involves in solving a system of non-linear isotopomer balance equations by simulating the isotopic labeling distributions of metabolites measured by tandem mass spectrometry, which is essentially an optimization problem. In this work, we introduce the cooperative coevolution optimization method for solving the set of non-linear equations that decomposes a large problem into a set of subcomponents. We demonstrate that cooperative coevolution can be used for solving the given metabolic flux model. While the proposed approach makes good progress on the use of evolutionary computation techniques for this problem, there exist a number of disadvantages that need to be addressed in the future to meet the expectation of the biologists.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bailey, J.: Toward a science of metabolic engineering. Science 252(5013), 1668–1675 (1991)
Nielsen, J.: It is all about metabolic fluxes. J. Bacteriol. 185(24), 7031–7035 (2003)
Sauer, U.: Metabolic networks in motion: 13C-based flux analysis. Mol. Syst. Biol. 2, 62 (2006)
Jeffrey, F.M., Roach, J.S., Storey, C.J., Sherry, A.D., Malloy, C.R.: 13C isotopomer analysis of glutamate by tandem mass spectrometry. Anal. Biochem. 300(2), 192–205 (2002)
Choi, J., Antoniewicz, M.R.: Tandem mass spectrometry: A novel approach for metabolic flux analysis. Metab. Eng. 13(2), 225–233 (2011)
Peng, L., Arauzo-Bravo, M.J., Shimizu, K.: Metabolic flux analysis for a ppc mutant Escherichia coli based on 13C-labelling experiments together with enzyme activity assays and intracellular metabolite measurements. FEMS Microbiol. Lett. 235(1), 17–23 (2004)
Zhao, J., Shimizu, K.: Metabolic flux analysis of escherichia coli k12 grown on 13c-labeled acetate and glucose using gc-ms and powerful flux calculation method. Journal of Biotechnology 101(2), 101–117 (2003)
Zhang, H., Yao, S.: Simulation of flux distribution in central metabolism of saccharomyces cerevisiae by hybridized genetic algorithm. Chinese Journal of Chemical Engineering 15(2), 150–156 (2007)
Potter, M.A., Jong, K.A.D.: A Cooperative Coevolutionary Approach to Function Optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN III. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)
Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Inf. Sci. 178(15), 2985–2999 (2008)
Chandra, R., Frean, M., Zhang, M.: An Encoding Scheme for Cooperative Coevolutionary Feedforward Neural Networks. In: Li, J. (ed.) AI 2010. LNCS, vol. 6464, pp. 253–262. Springer, Heidelberg (2010)
Chandra, R., Frean, M., Zhang, M.: On the issue of separability for problem decomposition in cooperative neuro-evolution. Neurocomputing 87, 33–40 (2012)
Chandra, R., Frean, M., Zhang, M., Omlin, C.W.: Encoding subcomponents in cooperative co-evolutionary recurrent neural networks. Neurocomputing 74(17), 3223–3234 (2011)
Chandra, R., Zhang, M.: Cooperative coevolution of elman recurrent neural networks for chaotic time series prediction. Neurocomputing 86(0), 116–123 (2012)
Supudomchok, S., Chaiyaratana, N., Phalakomkule, C.: Co-operative co-evolutionary approach for flux balance in bacillus subtilis. In: IEEE Congress on Evolutionary Computation, pp. 1226–1231. IEEE (2008)
Schmidt, K., Carlsen, M., Nielsen, J., Villadsen, J.: Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices. Biotechnology and Bioengineering 55(6), 831–840 (1997)
Zupke, C., Stephanopoulos, G.: Modeling of isotope distributions and intracellular fluxes in metabolic networks using atom mapping matrices. Biotechnology Progress 10(5), 489–498 (1994)
Online Tandem MS Software (May 2012), http://softwarefoundationfiji.org/research/bioinfor/tandem/
Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter optimization. Evol. Comput. 10(4), 371–395 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chandra, R., Zhang, M., Peng, L. (2012). Application of Cooperative Convolution Optimization for 13C Metabolic Flux Analysis: Simulation of Isotopic Labeling Patterns Based on Tandem Mass Spectrometry Measurements. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-34859-4_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34858-7
Online ISBN: 978-3-642-34859-4
eBook Packages: Computer ScienceComputer Science (R0)