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The evolution of higher-level biochemical reaction models

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Abstract

Computational tools for analyzing biochemical phenomena are becoming increasingly important. Recently, high-level formal languages for modeling and simulating biochemical reactions have been proposed. These languages make the formal modeling of complex reactions accessible to domain specialists outside of theoretical computer science. This research explores the use of genetic programming to automate the construction of models written in one such language. Given a description of desired time-course data, the goal is for genetic programming to construct a model that might generate the data. The language investigated is Kahramanoğullari’s and Cardelli’s Programming Interface for Modeling (PIM) language. The PIM syntax is defined in a grammar-guided genetic programming system. All time series generated during simulations are described by statistical feature tests, and the fitness evaluation compares feature proximity between the target and candidate solutions. PIM models of varying complexity were used as target expressions for genetic programming, and were successfully reconstructed in all cases. This shows that the compositional nature of PIM models is amenable to genetic program search.

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Notes

  1. The actual CFG grammar used is found at: http://www.cosc.brocku.ca/~bross/research/pimexample.zip.

References

  1. S. Ando, E. Sakamoto, H. Iba, Evolutionary modeling and inference of gene network. Inf. Sci. 145(3–4), 237–259 (2002)

    Article  MathSciNet  Google Scholar 

  2. P. Angeline, Evolving predictors for chaotic time series. In Proceedings of the. SPIE: Application and Science of Computational Intelligence, vol. 3390, pp. 170–180 (1998)

  3. P. Baldan, N. Cocco, A. Marin, M. Simeoni, Petri nets for modelling metabolic pathways: a survey. Nat. Comput. 9(4), 955–989 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  4. W. Banzhaf, in Genetic Programming Theory and Practice, ed. by R. Riolo, B. Worzel. Artificial Regulatory Networks and Genetic Programming (Kluwer, Dordrecht, 2003), pp. 43–61.

    Google Scholar 

  5. P. Bentley, J. Wakefield, in Soft Computing in Engineering Design and Manufacturing, ed. by P.K. Chawdhry et al. Finding Acceptable Solutions in the Pareto-Optimal Range Using Multiobjective Genetic Algorithms (Springer, Berlin, 1997)

  6. BioSPI: The biospi project (2010). http://www.wisdom.weizmann.ac.il/biospi. Last accessed July 9, 2010

  7. R. Blossey, L. Cardelli, A. Phillips, A compositional approach to the stochastic dynamics of gene networks. Trans. Comput. Syst. Biol. 3939, 99–122 (2006)

    MathSciNet  Google Scholar 

  8. A. Borrelli, I. De Falco, A. Della Cioppa, M. Nicodemi, G. Trautteura, Performance of genetic programming to extract the trend in noisy data series. Phys. A Stat. Theor. Phys. 370(1), 104–108 (2006)

    Article  Google Scholar 

  9. J. Bower, H. Bolouri, Computational Modeling of Genetic and Biochemical Networks. (MIT Press, Kaufmann, Cambridge, 2001)

    Google Scholar 

  10. H. Cao, F. Romero-Campero, S. Heeb, M. Camara, N. Krasnogor, Evolving cell models for systems and synthetic biology. Syst. Synth. Biol. 4(1), 55–84 (2010)

    Article  Google Scholar 

  11. A. Castellini, V. Manca, in Proceedings of the GECCO 2009, ed. by A.I. Esparcia et al. Learning Regulation Functions of Metabolic Systems by Artificial Neural Networks (ACM Press, New york, 2009), pp. 193–200

  12. C. Chaoulya, Petri net modelling of biological networks. Brief Bioinform 8(4), 210–219 (2007)

    Article  Google Scholar 

  13. D.Y. Cho, K.H. Cho, B.T. Zhang, Identification of biochemical networks by S-tree based genetic programming. Bioinformatics 22(13), 1631–1640 (2006)

    Article  Google Scholar 

  14. I. Chou, E. Voit, Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math. Biosci. 219, 57–83 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  15. D. Chu, in Proceedings of the CEC 2007, ed. by D. Srinivasan, L. Wang. Evolving Genetic Regulatory Networks for Systems Biology (IEEE Press, Singapore, 2007), pp. 875–882

  16. W. Clocksin, C. Mellish, Programming in Prolog. 4th edn. (Springer, Berline, 1994)

    Book  MATH  Google Scholar 

  17. C.C. Coello, G. Lamont, D.V. Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems. 2nd edn. (Kluwer, Dordrecht, 2007)

    MATH  Google Scholar 

  18. V. Danos, J. Feret, W. Fontana, R. Harmer, J. Krivine, in CONCUR 2007, ed. by L. Caires, V. Vasconcelos. Rule-Based Modelling of Cellular Signalling. LNCS 4703 (Springer, Berline, 2007), pp. 17–41

  19. L. Dematte, C. Priami, A. Romanel, in SFM 2008, ed. by M. Bernardo, P. Degano, G. Zavattaro. The BlenX Language: A Tutorial. LNCS 5016 (Springer, Berline, 2008), pp. 313–365

  20. A. Deutsch, S. Dormann, Cellular Automaton Modeling of Biological Pattern Formation. (Birkhauser, Basel, 2005)

    MATH  Google Scholar 

  21. B. Drennan, R. Beer, in Artificial Life X: Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems, ed. by L.R. Rocha et al. Evolution of Repressilators Using a Biologically-Motivated Model of Gene Expression (MIT Press, Cambridge, 2006), pp. 22–27

  22. G. Ermentrout, L. Edelstein-Keshet, Cellular automata approaches to biological modeling. J. Theor. Biol. 160, 97–133 (1993)

    Article  Google Scholar 

  23. J. Fisher, T. Henzinger, Executable cell biology. Nat. Biotechnol. 25(11), 1239–1249 (2007)

    Article  Google Scholar 

  24. A. Floares, in WCCI 2008, ed. by J. Wang. Automatic Inferring Drug Gene Regulatory Networks with Missing Information Using Neural Networks and Genetic Programming (IEEE, New York, 2008), pp. 3078–3085

  25. N. Friedman, M. Linial, I. Nachman, D. Pe’er, Using Bayesian networks to analyze expression data. J. Comput. Biol. 7(3–4), 601–620 (2000)

    Article  Google Scholar 

  26. D. Gillespie, Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem 81, 2340–2361 (1977)

    Article  Google Scholar 

  27. D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. (Addison Wesley, Reading, 1989)

    MATH  Google Scholar 

  28. M. Guerriero, J. Heath, C. Priami, in CMSB 2007, ed. by M. Calder, S. Gilmore. An Automated Translation from a Narrative Language for Biological Modelling into Process Algebra. LNCS 4695 (Springer, Berlin, 2007), pp. 136–151

  29. M. Hecker, S. Lambeck, S. Toepfer, van E. Someren, R. Guthke, Gene regulatory network inference: data integration in dynamic models—a review. Biosystems 96(1), 86–103 (2009)

    Article  Google Scholar 

  30. C.A.R. Hoare, Communicating Sequential Processes. (Prentice-Hall, Englewood Cliffs, 1985)

    MATH  Google Scholar 

  31. D. Husmeier, Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 19(17), 2271–2282 (2003)

    Article  Google Scholar 

  32. J. Imada, Evolutionary synthesis of stochastic gene network models using feature-based search spaces. Master’s thesis, Department of Computer Science, Brock University (2009)

  33. J. Imada, B. Ross, Evolutionary synthesis of stochastic gene network models using feature-based search spaces. New Generation Computing (2010, in press)

  34. INRIA: OCAML (2010). http://caml.inria.fr/ocaml/. Last accessed July 1, 2010

  35. Y. Jin, J. Branke, Evolutionary optimization in uncertain environments–a survey. IEEE Trans. Evol. Comput. 9(3), 303–317 (2005)

    Article  Google Scholar 

  36. O. Kahramanogullari, Pim—spim (2010). http://sites.google.com/site/ozankahramanogullari/software/pim. Last accessed July 12, 2011

  37. O. Kahramanogullari, L. Cardelli, in DCM’09, ed. by S.B. Cooper, V. Danos. An Intuitive Automated Modelling Interface for Systems Biology (EPTCS, 2009), pp. 1–18

  38. S. Kikuchi, D. Tominaga, M. Arita, K. Takahashi, M. Tomita, Dynamic modeling of genetic networks using genetic algorithm and S-system. Bioinformatics 19(5), 643–650 (2003)

    Article  Google Scholar 

  39. J. Kitagawa, H. Iba, Identifying Metabolic Pathways and Gene Regulation Networks with Evolutionary Algorithms. In: G. Fogel, D. Corne (eds) Evolutionary Computation in Bioinformatics, (Morgan Kaufmann, Los Altos, 2003) pp. 255–278.

    Chapter  Google Scholar 

  40. J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. (MIT Press, Cambridge, 1992)

    MATH  Google Scholar 

  41. J. Koza, M. Keane, M. Streeter, W. Mydlowec, J. Yu, G. Lanza, Genetic Programming IV: Routine Human-Competitive Machine Intelligence. (Kluwer Academic Publishers, Dordrecht, 2003)

    MATH  Google Scholar 

  42. J. Koza, W. Mydlowec, G. Lanza, J. Yu, M. Keane, Reverse Engineering and Automatic Synthesis of Metabolic Pathways from Observed Data using Genetic Programming. Technical report SMI-2000-0851, Stanford Medical Informatics (2000)

  43. W.P. Lee, K.C. Yang, Applying intelligent computing techniques to modeling biologial networks from expression data. Genomics Proteomics Bioinf. 6(2), 111–120 (2008)

    Article  Google Scholar 

  44. A. Leier, P. Kuo, W. Banzhaf, K. Burrage, in EuroGP 2006, ed. by P. Collet et al. Evolving Noisy Oscillatory Dynamics in Genetic Regulatory Networks. LNCS, vol. 3905 (Springer, Berlin, 2006), pp. 290–299

  45. R. Linden, A. Bhaya, Evolving fuzzy rules to model gene expression. Biosystems 88(1–2), 76–91 (2007)

    Article  Google Scholar 

  46. H. Liu, H. Motoda, Computational Methods of Feature Selection. (Chapman and Hall/CRC, London, 2007)

    MATH  Google Scholar 

  47. F. Markowetz, A Bibliography on Learning Causal Networks of Gene Interactions (2005). http://www.molgen.mpg.de/~markowet/docs/network-bib.pd. Last accessed April 1, 2005

  48. F. Markowetz, R. Spang, Inferring cellular networks—a review. BMC Bioinf. 8, 1–17 (2007)

    Article  Google Scholar 

  49. R. Milner, Communication and Concurrency. (Prentice Hall, Englewood Cliffs, 1989)

    MATH  Google Scholar 

  50. J. Moore, L. Hahn, In: Proceeding of the GECCO 2004, ed. by K. Deb et al. Systems Biology Modeling in Human Genetic Using Petri Nets and Grammatical Evolution. LNCS 3102 (Springer, Berlin, 2004), pp. 392–401

  51. A. Nanopoulos, R. Alcock, Y. Manolopoulos, in Information Processing and Technology, ed. by N. Mastorakis, S.D. Nikolopoulos. Feature-Based Classification of Time-Series Data. (Nova Science Publishers, Inc., Commack, 2001), pp. 49–61

  52. M. Nielsen, in Petri Nets: Application and Relationship to Other Models of Concurrency, ed. by W. Brauer. CCS—and its Relationship to Net Theory, LNCS 255 (Springer, Berlin, 1987), pp. 393–415.

    Google Scholar 

  53. J. Nummela, B. Julstrom, in Proceedings of the GECCO 2005, ed. by H.-G. Beyer et al. Evolving Petri Nets to Represent Metabolic Pathways (ACM, New York, 2005), pp. 2133–2139

  54. G. Paun, Membrane Computing: An Introduction. (Springer, Berlin, 2002)

    Book  MATH  Google Scholar 

  55. M. Peleg, D. Rubin, R. Altman, Using petri net tools to study properties and dynamics of biological systems. J. Am. Med. Inform. Assoc. 12(2), 181–199 (2005)

    Article  Google Scholar 

  56. M. Perez-Jiminez, F. Romero-Campero, P systems: a new computational modelling tool for systems biology. Trans. Comput. Syst.Biol. VI, 176–197 (2006)

    Google Scholar 

  57. A. Phillips, The stochastic pi machine (2008). http://research.microsoft.com/aphillip/spim. Last accessed Dec 9, 2008

  58. A. Phillips, L. Cardelli, in Proceedings of the Bioconcur’04. A Correct Abstract Machine for the Stochastic Pi-calculus (2004)

  59. C. Priami, Stochastic pi-calculus. Comput. J. 38(7), 579–589 (1995)

    Article  Google Scholar 

  60. L. Qian, H. Wang, E. Dougherty, Inference of noisy nonlinear differential equation models for gene regulatory networks using genetic programming and kalman filtering. IEEE Trans. Signal Process. 56(7), 3327–3339 (2008)

    Article  MathSciNet  Google Scholar 

  61. A. Raj, A. van Oudenaarden, Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008)

    Article  Google Scholar 

  62. A. Regev, E. Panina, W. Silverman, L. Cardelli, E. Shapiro, BioAmbients: an abstraction for biological compartments. Theor. Comput. Sci. 325(1), 141–167 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  63. K. Rodriguez-Vazquez, P.J. Fleming, Evolution of mathematical models of chaotic systems based on multiobjective genetic programming. Knowl. Inf. Syst. 8(2), 235–256 (2005)

    Article  Google Scholar 

  64. B. Ross, Logic-based genetic programming with definite clause translation grammars. New Gener. Comput. 19(4), 313–337 (2001)

    Article  MATH  Google Scholar 

  65. B. Ross, in Proceedings CI-2007, ed. by R. Andonie. Using Genetic Programming to Synthesize Monotonic Stochastic Processes (ACTA Press, 2007)

  66. B. Ross, in Proceedings of the CEC 2011. Evolution of Stochastic Bio-Networks Using Summed Rank Strategies (IEEE Press, New York, 2011)

  67. B. Ross, J. Imada, in Proceedings of the GECCO 2009. Evolving Stochastic Processes Using Feature Tests and Genetic Programming (2009)

  68. B. Ross, J. Imada, in Genetic Programming—Theory and Practice, ed. by R. Riolo et al. Using Multi-objective Genetic Programming to Synthesize Stochastic Processes (Springer, 2009)

  69. E. Sakamoto, H. Iba, in Proceedings of the CEC 2001. Inferring a System of Differential Equations for a Gene Regulatory Network by Using Genetic Programming (IEEE Press, New York, 2001), pp. 720–726

  70. R. Schwaerzel, T. Bylander, in GECCO 2006, ed. by M. Keijzer et al. Predicting Currency Exchange Rates by Genetic Programming with Trigonometric Functions and High-Order Statistics (ACM, New York, 2006), pp. 955–956

  71. R. Schwartz, Biological Modeling and Simulation. (MIT Press, Cambridge, 2008)

    Google Scholar 

  72. SICS: SICStus Prolog 4 (2010). http://www.sics.se/isl/sicstuswww/site/index.htm. Last accessed July 1, 2010.

  73. F. Streichert, H. Planatscher, C. Spieth, H. Ulmer, A. Zell, In GECCO-2004, ed. by K. Deb et al. Comparing Genetic Programming and Evolution Strategies on Inferring Gene Regulatory Networks. LNCS, vol. 3102 (Springer, Seattle, 2004), pp. 471–480

  74. G. Tkacik, W. Bialek, Cell Biology: Networks, Regulation and Pathways. In: R. Meyers (eds) Encyclopedia of Complexity and Systems Science, (Springer, Berlin, 2009)

    Google Scholar 

  75. H. Wang, L. Qian, E. Dougherty, in CIBCB 07, ed. by G. Volkert. Inference of Gene Regulatory Networks using S-System: A Unified Approach. (IEEE, New York, 2007), pp. 82–89

  76. X. Wang, K. Smith, R. Hyndman, Characteristic-based clustering for time series data. Data Min. Knowl. Discov. 13(3), 335–364. (2006). doi:10.1007/s10618-005-0039-x

  77. Wikipedia: Phagocytosis (2010). http://en.wikipedia.org/wiki/Phagocytosi. Last accessed July 2, 2010

  78. J. Yu, V. Smith, P. Wang, A. Hartemink, E. Jarvis, Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20(18), 3594–3603 (2004)

    Article  Google Scholar 

  79. W. Zhang, G. Yang, Wu Z., in Proceedings of the 3rd International Conference on Machine Learning and Cybernetics. Genetic Programming-based Modeling on Chaotic Time Series (IEEE, New York, 2004), pp. 2347–2352

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Acknowledgments

Thanks to Ozan Kahramanoğullari for generously sharing his PIM software, and for his helpful advice; to Cale Fairchild, for help with system issues; and to Janine Imada, for the use of her well-written statistical feature analysis code. Research supported by NSERC Operating Grant 138467.

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  1. Department of Computer Science, Brock University, 500 Glenridge Ave., St. Catharines, ON, L2S 3A1, Canada

    Brian J. Ross

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Ross, B.J. The evolution of higher-level biochemical reaction models. Genet Program Evolvable Mach 13, 3–31 (2012). https://doi.org/10.1007/s10710-011-9144-3

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