Abstract
A standard tree-based genetic programming system, called GRNGen, for the reverse engineering of gene regulatory networks starting from time series datasets, was proposed in EvoBIO 2011. Despite the interesting results obtained on the simple IRMA network, GRNGen has some important limitations. For instance, in order to reconstruct a network with GRNGen, one single regression problem has to be solved by GP for each gene. This entails a clear limitation on the size of the networks that it can reconstruct, and this limitation is crucial, given that real genetic networks generally contain large numbers of genes. In this paper we present a new system, called GeNet, which aims at overcoming the main limitations of GRNGen, by directly evolving entire networks using graph-based genetic programming. We show that GeNet finds results that are comparable, and in some cases even better, than GRNGen on the small IRMA network, but, even more importantly (and contrarily to GRNGen), it can be applied also to larger networks. Last but not least, we show that the time series datasets found in literature do not contain a sufficient amount of information to describe the IRMA network in detail.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Banzhaf, W.: Artificial regulatory networks and genetic programming. In: Riolo, R.L., Worzel, B. (eds.) GP Theory and Practice, ch. 4, pp. 43–62. Kluwer (2003)
Barabasi, A.-L.: Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life. Plume Books (April 2003)
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)
Clerc, M. (ed.): Particle Swarm Optimization. ISTE (2006)
Farinaccio, A., Vanneschi, L., Provero, P., Mauri, G., Giacobini, M.: A New Evolutionary Gene Regulatory Network Reverse Engineering Tool. In: Giacobini, M. (ed.) EvoBIO 2011. LNCS, vol. 6623, pp. 13–24. Springer, Heidelberg (2011)
Gardner, T.S., Bernardo, D.D., Lorenz, D., Collins, J.J.: Inferring genetic networks and identifying compound mode af action via expression profiling. Science 301, 102–105 (2003)
Gatta, G.D., Bansal, M., Ambesi-Impiombato, A., Antonini, D., Missero, C., Bernardo, D.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)
Hayete, J., McMillen, D., Collins, J.J.: Size matters: network inference tackles the genome scale. Mol. Syst. Biol. 3, 77 (2007)
Kauffman, S.A.: The Origins of Order. Oxford University Press, New York (1993)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE Int. Conf. on Neural Networks, vol. 4, pp. 1942–1948. IEEE Computer Society (1995)
Koza, J.R.: Genetic Programming. The MIT Press, Cambridge (1992)
Niehaus, J., Igel, C., Banzhaf, W.: Reducing the number of fitness evaluations in graph genetic programming using a canonical graph indexed database. Evol. Comput. 15, 199–221 (2007)
Poli, R., Langdon, W.B., McPhee, N.F.: A field guide to genetic programming (2008), http://lulu.com , http://www.gp-field-guide.org.uk
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. N Y Acad. Sci. 1115, 1–22 (2007)
Szallasi, Z., Stelling, J., Periwal, V.: System modeling in cellular biology: From concepts to nuts and bolts. The MIT Press, Boston (2006)
Ventura, B.D., Lemerle, C., Michalodimitrakis, K., Serrano, L.: From in vivo to in silico biology and back. Nature 443, 527–533 (2006)
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
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vanneschi, L., Mondini, M., Bertoni, M., Ronchi, A., Stefano, M. (2012). GeNet: A Graph-Based Genetic Programming Framework for the Reverse Engineering of Gene Regulatory Networks. In: Giacobini, M., Vanneschi, L., Bush, W.S. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2012. Lecture Notes in Computer Science, vol 7246. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29066-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-29066-4_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29065-7
Online ISBN: 978-3-642-29066-4
eBook Packages: Computer ScienceComputer Science (R0)