Abstract
Parameter estimation (PE) of biological systems is one of the most challenging problems in Systems Biology. Here we present a PE method that integrates particle swarm optimization (PSO) to estimate the value of kinetic constants, and a stochastic simulation algorithm to reconstruct the dynamics of the system. The fitness of candidate solutions, corresponding to vectors of reaction constants, is defined as the point-to-point distance between a simulated dynamics and a set of experimental measures, carried out using discrete-time sampling and various initial conditions. A multi-swarm PSO topology with different modalities of particles migration is used to account for the different laboratory conditions in which the experimental data are usually sampled. The whole method has been specifically designed and entirely executed on the GPU to provide a reduction of computational costs. We show the effectiveness of our method and discuss its performances on an enzymatic kinetics and a prokaryotic gene expression network.
Partially supported by Regione Lombardia, project “Network Enabled Drug Design (NEDD)”.
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References
Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D., Vanneschi, L.: A comparison of Genetic Algorithms and Particle Swarm Optimization for parameter estimation in stochastic biochemical systems. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2009. LNCS, vol. 5483, pp. 116–127. Springer, Heidelberg (2009)
Cao, Y., Gillespie, D.T., Petzold, L.: Efficient step size selection for the tau-leaping simulation method. J. Chem. Phys. 124, 44109 (2006)
Chou, I.C., Voit, E.O.: Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math. Biosci. 219, 57–83 (2009)
Durstenfeld, R.: Algorithm 235: Random permutation. Commun. ACM 7, 420 (1964)
Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Comp. Phys. 81, 2340–2361 (1977)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley (1989)
Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer for dynamic optimization problems. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 513–524. Springer, Heidelberg (2004)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. of the IEEE International Conference on Neural Networks, Piscataway, NJ, vol. IV, pp. 1942–1948 (1995)
Lin, S.C., Punch III, W.F., Goodman, E.D.: Coarse-grain parallel genetic algorithms: Categorization and new approach. In: Proc. of Sixth IEEE Symposium on Parallel and Distributed Processing, pp. 28–37. IEEE Computer Society Press (1994)
Moles, C.G., Mendes, P., Banga, J.R.: Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res. 13, 2467–2474 (2003)
Romero, J., Cotta, C.: Optimization by island-structured decentralized particle swarms. In: Reusch, B. (ed.) Computational Intelligence, Theory and Applications. AISC, vol. 33, pp. 25–33. Springer, Heidelberg (2005)
Tanese, R.: Distributed genetic algorithms. In: Proc. of Third Int. Conference on Genetic Algorithms, pp. 434–439. Morgan Kaufmann Publishers, San Francisco (1989)
Wang, Y., Christley, S., Mjolsness, E., Xie, X.: Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent. BMC Syst. Biol. 4(1), 99 (2010)
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Nobile, M.S., Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D. (2012). A GPU-Based Multi-swarm PSO Method for Parameter Estimation in Stochastic Biological Systems Exploiting Discrete-Time Target Series. In: Giacobini, M., Vanneschi, L., Bush, W.S. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2012. Lecture Notes in Computer Science, vol 7246. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29066-4_7
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DOI: https://doi.org/10.1007/978-3-642-29066-4_7
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