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Reverse Engineering of Biochemical Reaction Networks Using Co-evolution with Eng-Genes

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Abstract

A major challenge when attempting to model biochemical reaction networks within the cell is that the dimensionality can become huge, where a large number of molecular species can be involved even in relatively small networks. This investigation attempts to infer models of these networks using a co-evolutionary algorithm that reverse engineers differential equation models of the target system from time-series data. The algorithm not only estimates the system parameters, but also the symbolic structure of the network. To reduce the problem of dimensionality, the algorithm uses a partitioning method while integrating candidate models in order to decouple system equations. In addition, the conventional evolutionary algorithm has been modified and extended to include a technique called ‘eng-genes’, where candidate models are built up from fundamental mathematical terms derived from knowledge about the target system a priori. This technique essentially focuses the search on more biologically plausible models. The approach is demonstrated on several example reaction networks. The results show that the eng-genes method of limiting the term pool using a priori knowledge improves the convergence of the reverse engineering process compared with the conventional method, resulting in more accurate and transparent models.

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References

  1. Ando S, Sakamoto E, Iba H. Evolutionary modeling and inference of gene network. Inf Sci. 2002;145:237–59.

    Article  Google Scholar 

  2. Andrec M, Kholodenko BN, Levy RM, Sontag E. Inference of signaling and gene regulatory networks by steady-state perturbation experiments: structure and accuracy. J Theor Biol. 2005;232:427–41.

    Article  PubMed  CAS  Google Scholar 

  3. Arnqvist G, Rowe L. Antagonistic coevolution between the sexes in a group of insects. Nature. 2002;415:787–9.

    Article  PubMed  CAS  Google Scholar 

  4. Bongard J, Lipson H. Automating genetic network inference with minimal physical experimentation using coevolution. In: Genetic and evolutionary computation GECCO 2004. Lecture Notes in Computer Science. Heidelberg: Springer; 2004. p. 333–45.

  5. Bongard J, Lipson H. Nonlinear system identification using coevolution of models and tests. IEEE Trans Evol Comput. 2005;9(4):361–84.

    Article  Google Scholar 

  6. Bongard J, Lipson H. Automated reverse engineering of nonlinear dynamical systems. Proc Natl Acad Sci. 2007;104:9943–8.

    Article  PubMed  CAS  Google Scholar 

  7. Chan ZSH, Havukkala I, Jain V, Hu Y, Kasabov N. Soft computing methods to predict gene regulatory networks: an integrative approach on time-series gene expression data. Appl Soft Comput. 2008;8:1189–99.

    Article  Google Scholar 

  8. Chandra R, Zhang M. Cooperative coevolution of elman recurrent neural networks for chaotic time series prediction. Neurocomputation. 2012;86:116–23.

    Article  Google Scholar 

  9. Cho DY, Cho KH, Zhang BT. Identification of biochemical networks by s-tree based genetic programming. Bioinformatics. 2006;22:1631–40.

    Article  PubMed  CAS  Google Scholar 

  10. Clarke R, Ressom HW, Wang A, Xuan J, Liu MC, Gehan EA, Wang Y. The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nat Rev Cancer. 2008;8(1):37–49.

    Article  PubMed  CAS  Google Scholar 

  11. Connally P, Li K, Irwin GW. Two applications of eng-genes bases nonlinear identification. In: Proceedings of the 16th IFAC world congress. 2005.

  12. Connally P, Li K, Irwin GW. Integrated structure selection and parameter optimisation for eng-genes neural models. Neurocomputing. 2008;71(13–15):2964–77.

    Article  Google Scholar 

  13. Cosentino C, Curatola W, Montefusco F, Bansal M, di Bernardo D, Amato F. Linear matrix inequalities approach to reconstruction of biological networks. IET Syst Biol. 2007;1(3):164–73.

    Article  PubMed  CAS  Google Scholar 

  14. Gardner TS, Faith JJ. (2005) Reverse-engineering transcription control networks. Phys Life Rev. 2(1):65–88.

    Article  PubMed  Google Scholar 

  15. Gormley P, Li K, Irwin G. Modelling molecular interaction pathways using a two-stage identification algorithm. Syst Synth Biol. 2007;1(3):145–60.

    Article  Google Scholar 

  16. Gustafsson M, Hornquist M, Lombardi A. Constructing and analyzing a large-scale gene-to-gene regulatory network lasso-constrained inference and biological validation. IEEE/ACM Trans Comput Biol Bioinform. 2005;2(3):254–61.

    Article  PubMed  CAS  Google Scholar 

  17. Kitano H. Systems biology: a brief overview. Science. 2002;295:1662–4.

    Article  PubMed  CAS  Google Scholar 

  18. Kikuchi S, Tominaga D, Arita M, Takahashi K, Tomita M. Dynamic modeling of genetic networks using genetic algorithm and s-system. Bioinformatics. 2003;19(5):643–50.

    Article  PubMed  CAS  Google Scholar 

  19. Kimura S, Ide K, Kashihara A, et al. Inference of s-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics. 2005;21(7):1154–63.

    Article  PubMed  CAS  Google Scholar 

  20. KLi, Peng J. System oriented neural networks problem formulation, methodology, and application. Int J Pattern Recognit Artif Intell. 2006;20:143–58.

    Article  Google Scholar 

  21. Klipp E, Herwig R, Kowald A, Wierling C, Lehrach H. Systems biology in practice. Germany: Wiley-VCH Weinheim; 2005.

    Book  Google Scholar 

  22. Lee WP, Yang KC. A clustering-based approach for inferring recurrent neural networks as gene regulatory networks. Neurocomputing. 2008;71:600–10.

    Article  Google Scholar 

  23. Li B, Lin T, Liao L, Fan C. Genetic algorithm based on multipopulation competitive coevolution. In: 2008 IEEE congress on evolutionary computation; 2008. p. 225–8.

  24. Li K. Eng-genes: a new genetic modelling approach for nonlinear dynamic systems. In: Proceedings of the 16th IFAC world congress; 2005.

  25. Potter MA, Jong KAD. Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput. 2000;8:1–29.

    Article  PubMed  CAS  Google Scholar 

  26. Price ND, Shmulevich I. Biochemical and statistical network models for systems biology. Curr Opin Biotechnol. 2007;18(4):365–70.

    Article  PubMed  CAS  Google Scholar 

  27. Rawal A, Rajagopalan P, Miikkulainen R. Constructing competitive and cooperative agent behavior using coevolution. In: Proceedings of the 2010 IEEE conference on computational intelligence and games; 2010. p. 107–14.

  28. Sakamoto E, Iba H. Inferring a system of differential equations for a gene regulatory network by using genetic programming. In: Proceedings of the 2001 congress on evolutionary computation, vol 1; 2001. p. 720–6.

  29. Savageau MA. Biochemical systems analysis: a study of function and design in molecular biology. Reading: Addison-Wesley; 1976.

    Google Scholar 

  30. Styczynski MP, Stephanopoulos G. Overview of computational methods for the inference of gene regulatory networks. Comput Chem Eng. 2005;29:519–34.

    Article  CAS  Google Scholar 

  31. Sugimoto M, Kikuchi S, Tomita M. Reverse engineering of biochemical equations from time-course data by means of genetic programming. Biosystems. 2005;80(2):155–64.

    Article  PubMed  CAS  Google Scholar 

  32. Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM. Systematic determination of genetic network architecture. Nat Genet. 1999;22(3):281–5.

    Article  PubMed  CAS  Google Scholar 

  33. Tegner J, Björkegren J. Perturbations to uncover gene networks. Trends Genet. 2006;23(1):34–41.

    Article  PubMed  Google Scholar 

  34. Tomita M, Hashimoto K, Takahashi K, Shimizu T, Matsuzaki Y, Miyoshi F, Saito K, Tanida S, Yugi K, Venter J, Hutchison R. E-cell: software environment for whole-cell simulation. Bioinformatics. 1999;15:72–84

    Article  PubMed  CAS  Google Scholar 

  35. Uchibe E, Asada M. Incremental coevolution with competitive and cooperative tasks in a multirobot environment. Proc IEEE. 2006;94:1412–24.

    Article  Google Scholar 

  36. Vera J, JBachmann, Pfeifer A, et al. A systems biology approach to analyse amplification in the jak2–stat5 signalling pathway. BMC Syst Biol. 2008;2(1):38.

    Article  PubMed  Google Scholar 

  37. Vilela M, Chou IC, Vinga S, Vasconcelos A, Voit E, Almeida J Parameter optimization in s-system models. BMC Syst Biol. 2008;2(1):35.

    Article  PubMed  Google Scholar 

  38. Voit EO, Almeida J. Decoupling dynamical systems for pathway identification from metabolic profiles. Bioinformatics. 2004;20(11):1670–81.

    Article  PubMed  CAS  Google Scholar 

  39. Wang X, Wu M, Li Z, Chan C. Short time-series microarray analysis: methods and challenges. BMC Syst Biol. 2008;2(1):58.

    Article  PubMed  Google Scholar 

  40. Wang Y, Miller DJ, Clarke R. Approaches to working in high-dimensional data spaces: gene expression microarrays. Br J Cancer. 2008;98(6):1023–8.

    Article  PubMed  CAS  Google Scholar 

  41. Zykov V, Bongard J, Lipson H. Co-evolutionary variance can guide physical testing in evolutionary system identification. In: Proceedings of the 2005 NASA/DoD conference on evolvable hardware; 2005. p. 213–20.

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Acknowledgments

This work was partially supported by the Research Councils UK under grant EP/G042594/1, the National Science Foundation of China (61074032,51007052,61104089), and Science and Technology Commission of Shanghai Municipality (11ZR1413100).

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Authors and Affiliations

  1. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Stranmillis Road, Belfast, BT9 5AH, UK

    Padhraig Gormley, Kang Li, George W. Irwin & Dajun Du

  2. Systems Biology and Bioinformatics Group, Department of Computer Science, University of Rostock, Albert Einstein Str. 21, 18051, Rostock, Germany

    Olaf Wolkenhauer

  3. Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronical Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Dajun Du

Authors
  1. Padhraig Gormley
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  2. Kang Li
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  3. Olaf Wolkenhauer
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  4. George W. Irwin
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  5. Dajun Du
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Correspondence to Kang Li.

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Gormley, P., Li, K., Wolkenhauer, O. et al. Reverse Engineering of Biochemical Reaction Networks Using Co-evolution with Eng-Genes. Cogn Comput 5, 106–118 (2013). https://doi.org/10.1007/s12559-012-9159-y

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  • DOI: https://doi.org/10.1007/s12559-012-9159-y

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