research-article

Target driven biochemical network reconstruction based on petri nets and simulated annealing

Authors:

David GilbertAuthors Info & Claims

CMSB '10: Proceedings of the 8th International Conference on Computational Methods in Systems Biology

Pages 33 - 42

https://doi.org/10.1145/1839764.1839770

Published: 29 September 2010 Publication History

Abstract

In this paper we describe a method and an associated computational tool to modify and piecewise enlarge the topology of a biological network model, using a set of biochemical components, in order to generate one or more models whose behaviours simulate that of a target biological system. These components are defined as continuous Petri nets and stored in a library for ease of reuse. An optimization algorithm is proposed which exploits Simulated Annealing in order to alter an initial model by reference to the desired behaviour of the target model.

Simulation results on a realistic illustrative example signalling pathway show that the proposed method performs well in terms of exploiting the characteristics of simulated annealing in order to generate interesting models with behaviours close to that of the target biochemical system without any pre-knowledge on the target topology itself. In future work we plan to use the generated topologies as population candidates when using an evolutionary approach to further tune the network structure and kinetic parameters.

References

[1]

}}J. M. Berg, J. L. Tymoczko, and L. Stryer. Biochemistry. W. H. Freeman and Co., New York, fifth edition, 2002.

[2]

}}A. Brāzma, I. Jonassen, J. Vilo, and E. Ukkonen. Pattern discovery in biosequences. In ICGI'98 Proceedings, volume 1433 of LNAI, pages 257--270. Springer, July 1998.

[3]

}}R. Breitling, R. A. Donaldson, D. R. Gilbert, and M. Heiner. Biomodel engineering -- from structure to behavior. In Trans. Computational Systems Biology XII, volume 5945 of LNCS, pages 1--12. Springer, 2010.

[4]

}}R. Breitling, D. Gilbert, M. Heiner, and R. Orton. A structured approach for the engineering of biochemical network models, illustrated for signalling pathways. Brief Bioinform, 9(5):404--422, September 2008.

[5]

}}M. Calder, S. Gilmore, and J. Hillston. Modelling the influence of RKIP on the ERK signalling pathway using the stochastic process algebra PEPA. In Trans. Computational Systems Biology, volume 4230, pages 1--23. Springer, 2004.

[6]

}}B. Canton, A. Labno, and D. Endy. Refinement and standardization of synthetic biological parts and devices. Nature Biotechnology, 26(7):787--793, July 2008.

[7]

}}V. Chickarmane, S. R. Paladugu, F. Bergmann, and H. M. Sauro. Bifurcation discovery tool. Bioinformatics, 21(18):3688--3690, September 2005.

Digital Library

[8]

}}K. H. Cho, S. Y. Shin, H. W. Kim, O. Wolkenhauer, B. Mcferran, and W. Kolch. Mathematical modeling of the influence of RKIP on the ERK signaling pathway. In C. Priami, editor, Computational Methods in Systems Biology (CSMB'03), volume 2602 of LNCS, pages 127--141. Springer, 2003.

Digital Library

[9]

}}A. J. Christopher, D. John, L. Mariana, W. Gabriel, G. Jonathan, A. Adam, and K. Jay. BglBricks: A flexible standard for biological part assembly. J. Biol Eng, 4(1):1, 2010.

[10]

}}W. Elliot and D. Elliot. Biochemistry and Molecular Biology. Oxford University Press, 2nd edition edition, 2002.

[11]

}}X. J. Feng, S. Hooshangi, D. Chen, G. Li, R. Weiss, and H. Rabitz. Optimizing genetic circuits by global sensitivity analysis. Biophys J, 87(4):2195--2202, 2004.

[12]

}}P. Francois and V. Hakim. Design of genetic networks with specified functions by evolution in silico. Proceedings of the National Academy of Sciences of the United States of America, 101(2):580--585, January 2004.

[13]

}}D. Gilbert, R. Breitling, M. Heiner, and R. Donaldson. An introduction to bioModel engineering, illustrated for signal transduction pathways. In D. W. Corne, P. Frisco, G. Paun, G. Rozenberg, and A. Salomaa, editors, 9th International Workshop, WMC 2008, Edinburgh, UK, July 28--31, 2008, volume 5391 of LNCS, pages 13--28. Springer, 2009.

[14]

}}D. Gilbert and M. Heiner. From Petri Nets to differential equations - an integrative approach for biochemical network analysis. In S. Donatelli and P. S. Thiagarajan, editors, 27th International Conference, ATPN 2006, Turku, Finland, June 26--30, 2006, volume 4024 of LNCS, pages 181--200. Springer, 2006.

Digital Library

[15]

}}D. Gilbert, M. Heiner, and S. Lehrack. A unifying framework for modelling and analysing biochemical pathways using Petri Nets. In M. Calder and S. Gilmore, editors, International Conference, CMSB 2007, Edinburgh, Scotland, September 20--21, 2007, volume 4695 of LNCS, pages 200--216. Springer, 2007.

[16]

}}D. Gilbert, D. Westhead, and J. Viksna. Techniques for comparison, pattern matching and pattern discovery: from sequences to protein topology. In P. Frasconi and R. Shamir, editors, Artificial Intelligence and Heuristic Methods in Bioinformatics, volume 183 of NATO Science Series: Computer and Systems Sciences, pages 128--147. IOS Press, 2003.

[17]

}}M. Heiner, D. Gilbert, and R. Donaldson. Petri Nets for systems and synthetic biology. In Trans. Computational Systems Biology XII, volume 5016 of LNCS, pages 215--264. Springer, 2008.

[18]

}}M. Heiner and K. Sriram. Structural analysis to determine the core of hypoxia response network. PloS one, 5(1), 2010.

[19]

}}S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by simulated annealing. Science, 220(4598):671--680, May 1983.

[20]

}}X. Liu, J. Jiang, O. Ajayi, X. Gu, and D. Gilbert. Bionessie(g)- a grid enabled biochemical networks simulation environment. Studies in Health Technology and Informatics, 138:147--157, 2008.

[21]

}}G. Maria. A review of algorithms and trends in kinetic model identification for chemical and biochemical systems. Chem. Biochem. Eng. Q., 18(3):195--222, 2004.

[22]

}}H. Matsuno, S. Fujita, A. Doi, M. Nagasaki, and S. Miyano. Towards biopathway modeling and simulation. In 24th International Conference, ATPN2003, volume 2679 of LNCS, pages 3--22. Springer, 2003.

Digital Library

[23]

}}T. Murata. Petri Nets: properties, analysis and applications. Proc. of the IEEE 77, 4:541--580, 1989.

[24]

}}R. Randhawa. Model Composition and Aggregation in Macromolecular Regulatory Networks. PhD thesis, Faculty of the Virginia Polytechnic Institute and State University, 2008.

[25]

}}C. Rohr, W. Marwan, and M. Heiner. Snoopy - a unifying petri net framework to investigate biomolecular networks. Bioinformatics, 26(7):974--975, 2010.

Digital Library

[26]

}}F. Romero-Campero, H. Cao, M. Camara, and N. Krasnogor. Structure and parameter estimation for cell systems biology models. In M. K. et.al, editor, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2008), pages 331--338. ACM Publisher, 2008.

Digital Library

[27]

}}M. Schulz, B. M. Bakker, and E. Klipp. TIde: a software for the systematic scanning of drug targets in kinetic network models. BMC bioinformatics, 10(1):344--353, 2009.

[28]

}}D. Voet and J. G. Voet. Biochemistry. John Wiley & Sons, 2006.

Digital Library

[29]

}}V. Vyshemirsky and M. Girolami. Bayesian ranking of biochemical system models. Bioinformatics, 24(6):833--839, 2008.

Digital Library

[30]

}}K. Yeung, P. Janosch, B. McFerran, D. W. Rose, H. Mischak, J. M. Sedivy, and W. Kolch. Mechanism of suppression of the Raf/MEK/Extracellular signal-regulated kinase pathway by the Raf kinase inhibitor protein. Mol. Cell Biol., 20(9):3079--3085, 2000.

[31]

}}K. Yeung, T. Seitz, S. Li, P. Janosch, B. McFerran, C. Kaiser, F. Fee, K. D. Katsanakis, D. W. Rose, H. Mischak, J. M. Sedivy, and W. Kolch. Suppression of Raf-1 kinase activity and MAP kinase signaling by RKIP. Nature, 401:173--177, 1999.

Cited By

Wu ZHuiqin Du (2016)A study of parallel and evolutionary framework for modelling biochemical signalling pathways2016 2nd IEEE International Conference on Computer and Communications (ICCC)10.1109/CompComm.2016.7924810(783-788)Online publication date: Oct-2016
https://doi.org/10.1109/CompComm.2016.7924810
Wu ZPang WCoghill G(2015)An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated AnnealingCognitive Computation10.1007/s12559-015-9328-x7:6(637-651)Online publication date: 3-May-2015
https://doi.org/10.1007/s12559-015-9328-x
Wu ZPang WCoghill G(2015)An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1467-619:6(1595-1610)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1007/s00500-014-1467-6
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Index Terms

Target driven biochemical network reconstruction based on petri nets and simulated annealing
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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cover image ACM Other conferences

CMSB '10: Proceedings of the 8th International Conference on Computational Methods in Systems Biology

September 2010

119 pages

ISBN:9781450300681

DOI:10.1145/1839764

Conference Chair:
Paola Quaglia
CoSBi and University of Trento, Italy

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 September 2010

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CMSB '10: 8th Conference on Computational Methods in Systems Biology

September 29 - October 1, 2010

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Cited By

Wu ZHuiqin Du (2016)A study of parallel and evolutionary framework for modelling biochemical signalling pathways2016 2nd IEEE International Conference on Computer and Communications (ICCC)10.1109/CompComm.2016.7924810(783-788)Online publication date: Oct-2016
https://doi.org/10.1109/CompComm.2016.7924810
Wu ZPang WCoghill G(2015)An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated AnnealingCognitive Computation10.1007/s12559-015-9328-x7:6(637-651)Online publication date: 3-May-2015
https://doi.org/10.1007/s12559-015-9328-x
Wu ZPang WCoghill G(2015)An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1467-619:6(1595-1610)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1007/s00500-014-1467-6
Wu ZGrosan CGilbert D(2014)Empirical Study of Computational Intelligence Strategies for Biochemical Systems ModellingNature Inspired Cooperative Strategies for Optimization (NICSO 2013)10.1007/978-3-319-01692-4_19(245-260)Online publication date: 2014
https://doi.org/10.1007/978-3-319-01692-4_19
Wu ZPang WCoghill G(2013)Stepwise modelling of biochemical pathways based on qualitative model learning2013 13th UK Workshop on Computational Intelligence (UKCI)10.1109/UKCI.2013.6651284(31-37)Online publication date: Sep-2013
https://doi.org/10.1109/UKCI.2013.6651284
Wu ZYang SGilbert D(2012)A hybrid approach to piecewise modelling of biochemical systemsProceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I10.1007/978-3-642-32937-1_52(519-528)Online publication date: 1-Sep-2012
https://dl.acm.org/doi/10.1007/978-3-642-32937-1_52

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