ABSTRACT
Given concentrations of metabolites over a sequence of time steps, the metabolic pathway prediction problem seeks a set of reactions and rate constants for them that could yield the concentration-time data. Such metabolic pathways can be modeled with Petri nets: bipartite graphs whose nodes are called places and transitions and in which tokens move from place to place through the transitions. Thus the pathway prediction problem can be addressed by searching a space of Petri nets, and such a search can be undertaken evolutionarily.Here, a genetic algorithm performs such a search. The GA seeks only the net's structure; a hill-climbing step applied as part of evaluation approximates parameters associated with the net's transitions. On one contrived problem instance, the GA sometimes identifies the pathway used to generate the given data, but on a second contrived instance, apparently no harder, it fails. On an instance drawn from real biology---the pathway for phospholipid synthesis---the genetic algorithm identifies a Petri net whose pathway is very similar, but not identical to, the real one. In all three cases, the GA develops Petri nets that represent pathways that closely reproduce the target concentration-time data.
- Erick Cantú-Paz, editor. Genetic and Evolutionary Computation -- GECCO 2003, volume 2724 of LNCS, Berlin, 2003. Springer-Verlag. Part II.Google Scholar
- René David and Hassane Alla. On hybrid Petri nets. Discrete Event Dynamic Systems: Theory and Applications, 11(1--2):9--40, 2001. Google ScholarDigital Library
- Atsushi Doi, Sachie Fujita, Hiroshi Matsuno, Masao Nagasaki, and Satoru Miyano. Constructing biological pathway models with hybrid functional Petri nets. In Silico Biology, 4, 2004.Google Scholar
- Philip Hingston. A genetic algorithm for regular inference. In Lee Spector and et~al., editors, Proceedings of the 2001 Genetic and Evolutionary Computation Conference, pages 1299--1306, San Francisco, CA, 2001. Morgan Kaufman.Google Scholar
- Terry Jones and Gregory J. E. Rawlins. Reverse hillclimbing, genetic algorithms and the Busy Beaver problem. In Stephanie Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 70--75, San Mateo, CA, 1993. Morgan Kaufmann Publishers. Google ScholarDigital Library
- Bryant A. Julstrom. Evolutionary discovery of DFA size and structure. In K. M. George, Janice H. Carroll, Dave Oppenheim, and Jim Hightower, editors, Applied Computing 1996: Official Program of the 1996 ACM Symposium on Applied Computing, pages 263--268, New York, 1996. ACM Press. Google ScholarDigital Library
- Junji Kitagawa and Hitoshi Iba. Identifying metabolic pathways and gene regulation networks with evolutionary algorithms. In Gary B. Fogel and David W. Corne, editors, Evolutionary Computation in Bioinformatics, pages 255--278. Morgan Kaufmann, San Francisco, CA, 2003.Google ScholarCross Ref
- Marc M. Lankhorst. A genetic algorithm for the induction of nondeterministic pushdown automata. Technical Report CS-R 9502, University of Groningen, 2002.Google Scholar
- Simon M. Lucas and T. Jeff Reynolds. Learning DFA: Evolution versus evidence driven state merging. In Congress on Evolutionary Computation 2003, volume 1, pages 351--358, 2003.Google ScholarCross Ref
- Holger Mauch. Evolving Petri nets with a genetic algorithm. In Cantú-Paz {1}, pages 1810--1811. Part II. Google ScholarDigital Library
- P. Mendes and D. B. Kell. Non-linear optimization of biochemical pathways: Applications to metabolic engineering and parameter estimation. Bioinformatics, 14:869--883, 1999.Google ScholarCross Ref
- Jason H. Moore and Lance W. Hahn. Grammatical evolution for the discovery of Petri net models of complex genetic systems. In Cantú-Paz {1}, pages 2412--2413. Part II. Google ScholarDigital Library
- N. Niparnan and P. Chongstitvatana. An improved genetic algorithm for the inference of finite state machines. In 2002 International Conference on Systems, Man, and Cybernetics, volume 7, 2002.Google ScholarCross Ref
- James L. Peterson. Petri nets. Computing Surveys, 9(3):223--252, 1977. Google ScholarDigital Library
- James L. Peterson. Petri Net Theory and the Modeling of Systems. Prentice-Hall, Englewood Cliffs, NJ, 1981. Google ScholarDigital Library
- William H. Press, Brian P. Flannery, Saul A. Teukolsky, and William T. Vetterling. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge, second edition, 1992. Google ScholarDigital Library
- Nicholas J. Radcliffe. Genetic set recombination. In L. Darrell Whitley, editor, Foundations of Genetic Algorithms 2, pages 203--219. Morgan Kaufmann Publishers, San Mateo, CA, 1993.Google Scholar
- Kazuhiro Saitou, Samir Malpathak, and Helge Qvam. Robust design of flexible manufacturing systems using colored Petri net and genetic algorithm. Journal of Intelligent Manufacturing, 13:339--351, 2002.Google ScholarCross Ref
- H. Tohme, M. Nakamura, K. Hachiman, and K. Onaga. Evolutionary Petri net approach to periodic job-shop scheduling. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'99), volume 4, pages 441--446, Tokyo, 1999. IEEE Computer Society Press.Google ScholarCross Ref
- Edgar E. Vallejo and Fernando Ramos. Evolving Turing machines for biosequence recognition and analysis. In EuroGP '01: Proceedings of the 4th European Conference on Genetic Programming, pages 192--203, London, 2001. Springer-Verlag. Google ScholarDigital Library
- John Yen, James C. Liao, Bogju Lee, and David Randolph. A hybrid approach to modeling metabolic systems using a genetic algorithm and simplex method. IEEE Transactions on Systems, Man, and Cybernetics--Part B, 28(2):171--191, 1998. Google ScholarDigital Library
- Pascal Yim and Thomas Bourdeaud'huy. Petri net controller synthesis using genetic search. In Proceedings of the Second IEEE International Conference on Systems, Man and Cybernetics (SMC'02), volume 1, pages 528--533, Hammemet, Tunisia, 2002. IEEE Computer Society Press.Google Scholar
Index Terms
- Evolving petri nets to represent metabolic pathways
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