Abstract
This paper presents a genetic algorithm running on a grid computing environment for inference of genetic networks. In bioinformatics, inference of genetic networks is one of the most important problems, in which mutual interactions among genes are estimated by using gene-expression time-course data. Network-Structure-Search Evolutionary Algorithm (NSS-EA) is a promising inference method of genetic networks that employs S-system as a model of genetic network and a genetic algorithm (GA) as a search engine. In this paper, we propose an implementation of NSS-EA running on a multi-PC-cluster grid computing environment where multiple PC clusters are connected over the Internet. We “Gridifiy” NSS-EA by using a framework for the development of GAs running on a multi-PC-cluster grid environment, named Grid-Oriented Genetic Algorithm Framework (GOGA Framework). We examined whether the “Gridified” NSS-EA works correctly and evaluated its performance on Open Bioinformatics Grid (OBIGrid) in Japan.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ando, S., Iba, H.: Inference of Gene Regulatory Model by Genetic Algorithms. In: Proc. Con-gress on Evolutionary Computation 2001, pp. 712–719 (2001)
Ando, S., Sakamoto, E., Iba, H.: Modeling Genetic Network by Hybrid GP. In: Proc. Congress on Evolutionary Computation 2002 (CEC 2002), pp. 291–296 (2002)
BioGrid, http://www.biogrid.jp
Foster, I., Kasseleman, C.: Globus: A metacomputing infrastructure toolkit. Int’l Journal of Supercomputing Applications 11(2), 115–128 (1997)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company Inc., Reading (1989)
Gordon, V.S., Whitley, D.W.: Serial and Parallel Genetic Algorithms as Function Optimiz-ers. In: Proc. of the Fifth Int’l Conf. on Genetic Algorithms, pp. 434–439 (1993)
Gorges-Scheluter, M.: ASPARAGOS An Asynchronous Parallel Genetic Optimization Strategy. In: Proc. of the Third Int’l Conf. on Genetic Algorithms, pp. 422–427 (1989)
Hamilton-Wright, A., Stacey, D.: Fault-Tolerant Network Computation of Individuals in Genetic Algorithms. Congress on Evolutionary Computation 2002, 1721–1726 (2002)
Hiroyasu, T., Miki, M., Hamasaki, M., Tanimura, Y.: A New Model of Distributed Genetic Algorithm for Cluster Systems: Dual Individual DGA. In: Proc. of the Int’l Conf. on Parallel and Distributed Processing Techniques and Applications, vol. 1, pp. 477–483 (2000)
Iba, H., Mimura, A.: Inference of Gene Regulatory Network by means of Interactive Evolutionary Computing. Information Sciences 145(3-4), 225–236 (2002)
Imade, H., Morishita, R., Ono, I., Ono, N., Okamoto, M.: A Grid-Oriented Genetic Algorithm Framework for Bioinformatics. New Generation Computing 22, 177–186 (2004)
Kimura, S., Hatakeyama, H., Konagaya, A.: Inference of S-system Models of Genetic Networks using a Genetic Local Search. In: Proc. 2003 Congress on Evolutionary Computation (CEC 2003), pp. 631–638 (2003)
Konagaya, A., Konishi, F., Hatakeyama, M., Satou, K.: The Superstructure towards Open Bioinformatics Grid. New Generation Computing 22, 167–176 (2004)
Laszewski, G., Foster, I., Gawor, J., Smith, W., Tuecke, S.: CoG Kits: A Bridge between Commodity Distributed Computing and High-Performance Grids. In: ACM 2000 Java Grande Conf., pp. 97–106 (2000)
Lee, C.H., Park, K.H., Kim, J.H.: Hybrid Parallel Evolutionary Algorithms for constrained optimization utilizing PC Clustering. Congress on Evolutionary Computation 2001, 1436–1441 (2001)
Maki, Y., Tominaga, D., Okamoto, M., Watanabe, S., Eguchi, Y.: Development of a System for the Inference of Large Scale Genetic Networks. In: Proc. Pacific Symp. on Biocomputing, pp. 446–458 (2001)
Maki, Y., Ueda, T., Okamoto, M., Uematsu, N., Inamura, Y., Eguchi, Y.: Inference of Genetic Network Using the Expression Profile Time Course Data of Mouse P19 Cells. Genome Informatics 13, 382–383 (2002)
Morishita, R., Imade, H., Ono, I., Ono, N., Okamoto, M.: Finding Multiple Solutions Based on An Evolutionary Algorithm for Inference of Genetic Networks by S-system. In: Proc. 2003 Congress on Evolutionary Computation (CEC 2003), pp. 615–622 (2003)
North Carolina Bioinformatics Grid, http://www.ncbiogrid.org
Ono, I., Kobayashi, S.: A real-coded genetic algorithm for function optimization using Unimodal Normal Distribution Crossover. In: Proc. of the Seventh Int’l Conf. on Genetic Algorithms, pp. 246–253 (1997)
Sakamoto, E., Iba, H.: Inferring a System of Differential Equations for a Gene Regulatory Network by using Genetic Programming. In: Proc. 2001 Congress on Evolutionary Computation (CEC 2001), pp. 720–726 (2001)
Sato, H., Yamamura, M., Kobayashi, S.: Minimal generation gap model for GAs considering both exploration and exploitation. In: Proc. of 4th Int. Conf. on Soft Computing, pp. 494–497 (1996)
Savageau, M.A.: Biochemical Systems Analysis: A Study of Function and Design in Molecular Biology. Addison-Wesley, Massachusetts (1976)
Tanese, R.: Distributed Genetic Algorithms. In: Proc. of the Third Int’l Conf. on Genetic Algorithms, pp. 434–439 (1989)
Tominaga, D., Okamoto, M.: Design of canonical model describing complex nonlinear dynamics. In: Computer Applications in Biotechnology 1998 (CAB7), p. 85. Pergamon Press, Oxford (1998)
Tominaga, D., Koga, N., Okamoto, M.: Efficient Numerical Optimization Algorithm Based on Genetic Algorithm for Inverse Problem. In: Proc. of the Genetic and Evolutionary Computation Conf., pp. 251–258 (2000)
Ueda, T., Koga, N., Ono, I., Okamoto, M.: Efficient Numerical Optimization Technique Based on Read-Coded Genetic Algorithm for Inverse Problem. In: Proc. 7th Int’l Symp. On Artificial Life and Robotics (AROB 2002), pp. 290–293 (2002)
Voit, E.O.: Computational Analysis of Biochemical Systems. In: A Practical Guide for Bio-chemists and Molecular Biologists. Cambridge University Press, Cambridge (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Imade, H., Mizuguchi, N., Ono, I., Ono, N., Okamoto, M. (2005). “Gridifying” an Evolutionary Algorithm for Inference of Genetic Networks Using the Improved GOGA Framework and Its Performance Evaluation on OBI Grid. In: Konagaya, A., Satou, K. (eds) Grid Computing in Life Science. LSGRID 2004. Lecture Notes in Computer Science(), vol 3370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32251-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-32251-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25208-5
Online ISBN: 978-3-540-32251-1
eBook Packages: Computer ScienceComputer Science (R0)