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Gene regulatory networks reconstruction from time series datasets using genetic programming: a comparison between tree-based and graph-based approaches

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Abstract

Genetic programming researchers have shown a growing interest in the study of gene regulatory networks in the last few years. Our team has also contributed to the field, by defining two systems for the automatic reverse engineering of gene regulatory networks called GRNGen and GeNet. In this paper, we revise this work by describing in detail the two approaches and empirically comparing them. The results we report, and in particular the fact that GeNet can be used on large networks while GRNGen cannot, encourage us to pursue the study of GeNet in the future. We conclude the paper by discussing the main research directions that we are planning to investigate to improve GeNet.

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Notes

  1. It is worth reminding that the fitness of an individual in GeNet is always calculated as the RMSE between the target time series dataset and the one reconstructed by the individual itself. Thus it has no relationship with the PPV and Se of the network. Furthermore, we also point out that the PPV and Se themselves could not have been used as fitness values, because, in order to calculate them, the target network must be known, while reverse engineering methods must work using only the information contained in the time series datasets.

  2. Exactly the same qualitative conclusions can be drawn for the switch-on dataset; we do not report the results here.

References

  1. J. Hayete, D. McMillen, J.J. Collins, Size matters: network inference tackles the genome scale. Mol. Syst. Biol. 3, 77 (2007)

    Article  Google Scholar 

  2. D. Sprinzak, M. B. Elowitz, Reconstruction of genetic circuits. Nature 438, 443–448 (2005)

    Article  Google Scholar 

  3. S.A. Kauffman, Metabolic stability of epigenesis in randomly contructed genetic nets. J. Theor. Biol. 22, 437–467 (1969)

    Article  MathSciNet  Google Scholar 

  4. S.A. Kauffman, The Origins of Order. (Oxford University Press, New York, 1993)

    Google Scholar 

  5. R. Serra, M. Villani, Recent results on random boolean networks. In Systemics of Emergence: Research and Development, ed. by G. Minati, E. Pessa, M. Abram (Springer, New York, 2006) pp. 625–634

  6. R. Serra, M. Villani, C. Damiani, A. Graudenzi, A. Colacci, The diffusion of perturbations in a model of coupled random boolean networks. In ACRI, volume 5191 of Lecture Notes in Computer Science, ed. by H. Umeo, S. Morishita, K. Nishinari, T. Komatsuzaki, S. Bandini (Springer, New York, 2008), pp. 315–322

  7. C. Damiani, S. A. Kauffman, R. Serra, M. Villani, A. Colacci. Information transfer among coupled random boolean networks. In ACRI, Volume 6350 of Lecture Notes in Computer Science, ed. by S. Bandini, S. Manzoni, H. Umeo, G. Vizzari (Springer, New York, 2010) pp. 1–11

  8. B. Di Ventura, C. Lemerle, K. Michalodimitrakis, L. Serrano, From in vivo to in silico biology and back. Nature 443, 527–533 (2006)

    Article  Google Scholar 

  9. Z. Szallasi, J. Stelling, V. Periwal, System Modeling in Cellular Biology: From Concepts to Nuts and Bolts. (The MIT Press, Cambridge, MA, 2006)

    Google Scholar 

  10. G. Della Gatta, M. Bansal, A. Ambesi-Impiombato, D. Antonini, C. Missero, D. Di Bernardo, Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering. Genome Res. 18, 939–948 (2008)

    Article  Google Scholar 

  11. J.J. Faith, B. Hayete, J.T. Thaden, I. Mogno, J. Wierzbowski, G. Cottarel, S. Kasif, J. J. Collins, T.S. Gardner, Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biology. Open Access Database, 5, 8 pages. Freely available online at http://www.readcube.com/articles/10.1371/journal.pbio.0050008.(2007)

  12. J. Bongard, H. Lipson, Automated reverse engineering of nonlinear dynamical systems. In Proceedings of the National Academy of Science, vol. 104, pp. 9943–9948 (2007)

  13. J.R. Koza, Genetic Programming. (The MIT Press, Cambridge, MA, 1992)

    MATH  Google Scholar 

  14. R. Poli, W.B. Langdon, N.F. McPhee, A Field Guide to Genetic Programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (2008)

  15. A. Farinaccio, L. Vanneschi, P. Provero, G. Mauri, M. Giacobini, A new evolutionary gene regulatory network reverse engineering tool. In Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, volume 6623 of Lecture Notes in Computer Science, C. Pizzuti, M. Ritchie, M. Giacobini ed. by (Springer, Berlin, 2011) pp. 13–24

  16. L. Vanneschi, M. Mondini, M. Bertoni, A. Ronchi, M. Stefano, Genet: a graph-based genetic programming framework for the reverse engineering of gene regulatory networks. In Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, volume 7246 of Lecture Notes in Computer Science, ed. by M. Giacobini, L. Vanneschi, W. Bush (Springer Berlin, 2012), pp 97–109. 10.1007/978-3-642-29066-4_9

  17. I. Cantone, L. Marucci, F. Iorio, M. A. Ricci, V. Belcastro, M. Bansal, S. Santini, M. di Bernardo, D. di Bernardo, M.P. Cosma, A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell 137(1), 172–81 (2009)

    Article  Google Scholar 

  18. G. Stolovitzky, D. Monroe, A. Califano, Dialogue on reverse-engineering assessment and methods: the dream of high-throughput pathway inference. Ann. N. Y. Acad. Sci. 1115, 1–22 (2007)

    Article  Google Scholar 

  19. J. Yu, V.A. Smith, P.P. Wang, A.J. Hartemink, E.D. Jarvis, Advances to bayesian network inference for generating casual networks from observational biological data. Bioinformatics 20, 3594–3603 (2004)

    Article  Google Scholar 

  20. T.S. Gardner, D. Di Bernardo, D. Lorenz, J.J. Collins. Inferring genetic networks and identifying compound mode af action via expression profiling. Science 301, 102–105, (2003)

    Article  Google Scholar 

  21. W. Banzhaf, Artificial regulatory networks and genetic programming. In GP Theory and Practice, chapter 4 ed. by R.L. Riolo, B. Worzel (Kluwer, Dordrecht, 2003) pp. 43–62

  22. W. Banzhaf, On evolutionary design, embodiment and artificial regulatory networks. In Embodied Artificial Intelligence, vol. 3139, (Springer, New York, 2004) pp. 284–292

  23. P.D. Kuo, W. Banzhaf, A. Leier, Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence. Biosystems 85(3), 177 – 200 (2006)

    Article  Google Scholar 

  24. A. Leier, P.D. Kuo, W. Banzhaf, K. Burrage, Evolving noisy oscillatory dynamics in genetic regulatory networks. In Genetic Programming, volume 3905 of Lecture Notes in Computer Science ed. by P. Collet, M. Tomassini, M. Ebner, S. Gustafson, A. Ekrt (Springer, Berlin, 2006) pp. 290–299

  25. C.S. Greene, J.H. Moore, Solving complex problems in human genetics using gp: challenges and opportunities. SIGEVOlution 3(2), 2–8 (2008)

    Article  Google Scholar 

  26. H. Wang, L. Qian, E. Dougherty, Inference of gene regulatory networks using genetic programming and kalman filter. In IEEE International Workshop on Genomic Signal Processing and Statistics, 2006. GENSIPS ’06. (May 2006) pp. 27 –28

  27. L. Qian, H. Wang, X. Li, Gene regulatory networks inference: Combining a genetic programming and \(H_{\infty}\) filtering approach. In Applied Statistics for Network Biology: Methods in Systems Biology ed. by M. Dehmer, F. Emmert-Streib, A. Graber, A. Salvador (Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim) Published Online: 21 Apr 2011 at: http://onlinelibrary.wiley.com/doi/10.1002/9783527638079.ch7/summary. 10.1002/9783527638079.ch7. (2011)

  28. F. Streichert, H. Planatscher, C. Spieth, H. Ulmer, A. Zell, Comparing genetic programming and evolution strategies on inferring gene regulatory networks. In Genetic and Evolutionary Computation (GECCO 2004), volume 3102 of Lecture Notes in Computer Science, ed. by K. Deb 10.1007/978-3-540-24854-5_47. (Springer, Berlin / Heidelberg, 2004) p. 471–480

  29. X. Cai, S.M. Welch, P. Koduru, S. Das, Discovering structures in gene regulatory networks using genetic programming and particle swarms. In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECCO ’07, (ACM, New York, NY, 2007). pp. 1750–1750

  30. A.G. Floares, Automatic reverse engineering algorithm for drug gene regulating networks. In Proceedings of the Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing, ASC ’07, (Anaheim, CA, ACTA Press, 2007) pp. 238–243

  31. M. Nicolau, M. Schoenauer, On the evolution of scale-free topologies with a gene regulatory network model. Biosystems 98(3), 137–148 (2009)

    Article  Google Scholar 

  32. R.L. Lopes, E. Costa, The regulatory network computational device. Genetic Program. Evolvable Mach. 13, 339–375 (2012)

    Article  Google Scholar 

  33. J.A. Foster, J.H. Moore, GECCO-2006 highlights: biological applications. SIGEVOlution, 1(3), 23 (2006)

    Google Scholar 

  34. J. Quackenbush, Computational analysis of microarray data. Nat Rev Genet 2(6), 418–427, (2001)

    Article  Google Scholar 

  35. J. Niehaus, C. Igel, W. Banzhaf, Reducing the number of fitness evaluations in graph genetic programming using a canonical graph indexed database. Evol. Comput. 15, 199–221 (2007)

    Article  Google Scholar 

  36. A.-L. Barabasi, Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life (Plume Books, USA, 2003)

    Google Scholar 

  37. J. Kennedy, R. Eberhart, Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE Computer Society (1995)

  38. M. Clerc, Particle Swarm Optimization. (ISTE, Eugene, 2006)

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ISEGI, Universidade Nova de Lisboa, 1070-312, Lisboa, Portugal

    Leonardo Vanneschi

  2. INESC-ID, IST, Universidade Técnica de Lisboa, 1000-029, Lisboa, Portugal

    Leonardo Vanneschi

  3. Dipartimento di Informatica, Sistemistica e Comunicazione (D.I.S.Co.), Università di Milano-Bicocca, 20126, Milan, Italy

    Leonardo Vanneschi

  4. Mario Negri Institute for Pharmacological Research, Ranica, Italy

    Matteo Mondini

  5. Biozentrum, Universität Basel, Basel, Switzerland

    Martino Bertoni

  6. Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy

    Alberto Ronchi

  7. Università di Milano-Bicocca, 20126, Milan, Italy

    Mattia Stefano

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  1. Leonardo Vanneschi
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  2. Matteo Mondini
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  3. Martino Bertoni
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  4. Alberto Ronchi
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  5. Mattia Stefano
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Correspondence to Leonardo Vanneschi.

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Vanneschi, L., Mondini, M., Bertoni, M. et al. Gene regulatory networks reconstruction from time series datasets using genetic programming: a comparison between tree-based and graph-based approaches. Genet Program Evolvable Mach 14, 431–455 (2013). https://doi.org/10.1007/s10710-013-9183-z

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  • DOI: https://doi.org/10.1007/s10710-013-9183-z

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