Abstract
We present a new reverse-engineering framework for gene regulatory network reconstruction. It works on temporal series of gene activation data and, using genetic programming, it extracts the activation functions of the different genes from those data. Successively, the gene regulatory network is reconstructed exploiting the automatic feature selection performed by genetic programming and its dynamics can be simulated using the previously extracted activation functions. The framework was tested on the well-known IRMA gene regulatory network, a simple network composed by five genes in the yeast Saccharomyces cerevisiae, defined in 2009 as a simplified biological model to benchmark reverse-engineering approaches. We show that the performances of the proposed framework on this benchmark network are encouraging.
Keywords
- Positive Predictive Value
- Activation Function
- Gene Regulatory Network
- Reverse Engineering
- Temporal Series
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Banzhaf, W.: Artificial regulatory networks and genetic programming. In: Riolo, R.L., Worzel, B. (eds.) Genetic Programming Theory and Practice, ch. 4, pp. 43–62. Kluwer, Dordrecht (2003)
Basso, K., Margolin, A.A., Stolovitzky, G., Klein, U., Dalla-Favera, R., Califano, A.: Reverse engineering of regulatory networks in human B cells. Nat. Genet. 37, 382–390 (2005)
Bongard, J., Lipson, H.: Automated reverse engineering of nonlinear dynamical systems. Proceedings of the National Academy of Science 104, 9943–9948 (2007)
Cantone, I., Marucci, L., Iorio, F., Ricci, M.A., Belcastro, V., Bansal, M., Santini, S., Di Bernardo, M., Di Bernardo, D., Cosma, M.P.: A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell 137(1), 172–181 (2009)
Damiani, C., Kauffman, S.A., Serra, R., Villani, M., Colacci, A.: Information transfer among coupled random boolean networks. In: Bandini, S., Manzoni, S., Umeo, H., Vizzari, G. (eds.) ACRI 2010. LNCS, vol. 6350, pp. 1–11. Springer, Heidelberg (2010)
Della Gatta, G., Bansal, M., Ambesi-Impiombato, A., Antonini, D., Missero, C., Di Bernardo, D.: Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering. Genome Res. 18, 939–948 (2008)
Di Bernardo, D., Thompson, M.J., Gardner, T.S., Chobot, S.E., Eastwood, E.L., Wojtovich, A.P., Elliot, S.J., Schaus, S.E., Collins, J.J.: Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat. Biotechnol. 23, 377–383 (2005)
Di Ventura, B., Lemerle, C., Michalodimitrakis, K., Serrano, L.: From in vivo to in silico biology and back. Nature 443, 527–533 (2006)
Faith, J.J., Hayete, B., Thaden, J.T., Mogno, I., Wierzbowski, J., Cottarel, G., Kasif, S., Collins, J.J., Gardner, T.S.: Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 5, e8 (2007)
Gardner, T.S., Di Bernardo, D., Lorenz, D., Collins, J.J.: Inferring genetic networks and identifying compound mode af action via expression profiling. Science 301, 102–105 (2003)
Hasty, J., McMillen, D.: Engineered gene circuits. Nature 420, 224–230 (2002)
Hayete, J., McMillen, D., Collins, J.J.: Size matters: network inference tackles the genome scale. Mol. Syst. Biol. 3, 77 (2007)
Kauffman, S.A.: Metabolic stability of epigenesis in randomly contructed genetic nets. Journal of Theoretical Biology 22, 437–467 (1969)
Kauffman, S.A.: The Origins of Order. Oxford University Press, New York (1993)
Koza, J.R.: Genetic Programming. The MIT Press, Cambridge (1992)
Quackenbush, J.: Computational analysis of microarray data. Nat. Rev. Genet. 2(6), 418–427 (2001)
Sakamoto, E., Iba, H.: Inferring a system of differential equations for a gene regulatory network by using genetic programming. In: Proceedings of the Congress on Evolutionary Computation, pp. 720–726. IEEE Press, Los Alamitos (2001)
Serra, R., Villani, M.: Recent results on random boolean networks. In: Minati, G., Pessa, E., Abram, M. (eds.) Systemics of Emergence: Research and Development, pp. 625–634. Springer, Heidelberg (2006)
Serra, R., Villani, M., Damiani, C., Graudenzi, A., Colacci, A.: The diffusion of perturbations in a model of coupled random boolean networks. In: Umeo, H., Morishita, S., Nishinari, K., Komatsuzaki, T., Bandini, S. (eds.) ACRI 2008. LNCS, vol. 5191, pp. 315–322. Springer, Heidelberg (2008)
Silva, S.: GPLAB – a genetic programming toolbox for MATLAB, version 3.0 (2007), http://gplab.sourceforge.net
Sprinzak, D., Elowitz, M.B.: Reconstruction of genetic circuits. Nature 438, 443–448 (2005)
Stolovitzky, G., Monroe, D., Califano, A.: Dialogue on reverse-engineering assessment and methods: the dream of high-throughput pathway inference. Ann. NY Acad. Sci. 1115, 1–22 (2007)
Streichert, F., Planatscher, H., Spieth, C., Ulmer, H., Zell, A.: Comparing genetic programming and evolution strategies on inferring gene regulatory networks. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 471–480. Springer, Heidelberg (2004)
Szallasi, Z., Stelling, J., Periwal, V.: System modeling in cellular biology: From concepts to nuts and bolts. The MIT Press, Boston (2006)
Vanneschi, L., Farinaccio, A., Giacobini, M., Mauri, G., Antoniotti, M., Provero, P.: Identification of individualized feature combinations for survival prediction in breast cancer: A comparison of machine learning techniques. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2010. LNCS, vol. 6023, pp. 110–121. Springer, Heidelberg (2010)
Langdon, W.B., Barrett, S.J.: Genetic Programming in data mining for drug discovery. Evolutionary Computing in Data Mining, 211–235 (2004)
Yu, J., Smith, V.A., Wang, P.P., Hartemink, A.J., Jarvis, E.D.: Advances to bayesian network inference for generating casual networks from observational biological data. Bioinformatics 20, 3594–3603 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Farinaccio, A., Vanneschi, L., Provero, P., Mauri, G., Giacobini, M. (2011). A New Evolutionary Gene Regulatory Network Reverse Engineering Tool. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2011. Lecture Notes in Computer Science, vol 6623. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20389-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-20389-3_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20388-6
Online ISBN: 978-3-642-20389-3
eBook Packages: Computer ScienceComputer Science (R0)