Abstract
Morphogenesis and the spatial structure of an organism have repercussions on gene expression. These effects can influence the results of regulatory network reconstruction. An integrated, flexible and extensible computational framework for modelling gene expression dynamics within spatially growing structures is developed and used as a test system for evaluating a reconstruction algorithm. With complex morphological structures, significant effects of spatial organisation on the reconstruction process are observed. The results also reveal that stronger regulatory interactions result in more frequent cases of indirect regulation, posing a challenge for accurate network reconstruction.
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Kauffman, S.A.: Developmental logic and its evolution. BioEssays 6, 82–87 (1987)
Reil, T.: Dynamics of gene expression in an artificial genome – implications for biological and artificial ontogeny. In: Floreano, D., Nicoud, J.D., Mondada, F. (eds.) ECAL 1999. LNCS, vol. 1674, pp. 457–466. Springer, Heidelberg (1999)
Kim, J.T.: Lindevol: Artificial models for natural plant evolution. In: Künstliche Intelligenz, pp. 26–32 (2000)
Kim, J.T.: transsys: A generic formalism for modelling regulatory networks in morphogenesis. In: Kelemen, J., SosÃk, P. (eds.) ECAL 2001. LNCS (LNAI), vol. 2159, pp. 242–251. Springer, Heidelberg (2001)
Banzhaf, W.: On the dynamics of an artificial regulatory network. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 217–227. Springer, Heidelberg (2003)
Bongard, J.: Evolving modular genetic regulatory networks. In: Fogel, D.B., El-Sharkawi, M.A., Yao, X., Greenwood, G., Iba, H., Marrow, P., Shackleton, M. (eds.) Proceedings of the IEEE 2002 Congress on Evolutionary Computation (CEC 2002, pp. 1872–1877. IEEE Press, Piscataway (2002)
Bornholdt, S., Rohlf, T.: Topological evolution of dynamical networks: Global criticality from local dynamics. Physical Review Letters 84, 6114–6117 (2000)
Lee, T.I., Rinaldi, N.J., Robert, F., Odom, D.T., Bar-Joseph, Z., Gerber, G.K., Hannett, N.M., Harbison, C.T., Thompson, C.M., Simon, I., Zeitlinger, J., Jennings, E.G., Murray, H.L., Gordon, D.B., Ren, B., Wyrick, J.J., Tagne, J.B., Volkert, T.L., Fraenkel, E., Gifford, D.K., Young, R.A.: Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 799–804 (2002)
Yu, H., Luscombe, N.M., Quian, J., Gerstein, M.: Genomic analysis of gene expression relationships in transcriptional regulatory networks. Trends in Genetics 19, 422–427 (2003)
Bray, D.: Molecular networks: The top-down view. Science 301, 1864–1865 (2003)
Barabási, A.L., Oltvai, Z.N.: Network biology: Understanding the cell’s functional organization. Nature Reviews Genetics 5, 101–113 (2004)
Akutsu, T., Miyano, S., Kuhara, S.: Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics 16, 727–734 (2000)
Rung, J., Schlitt, T., Brazma, A., Freivalds, K., Vilo, J.: Building and analysing genome-wide gene disruption networks. Bioinformatics 210, S202–S210 (2002)
Repsilber, D., Liljenström, H., Andersson, S.G.: Reverse engineering of regulatory networks: Simulation studies on a genetic algorithm approach for ranking hypotheses. BioSystems 66, 31–41 (2002)
Bongard, J., Lipson, H.: Automating genetic network inference with minimal physical experimentation using coevolution. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 333–345. Springer, Heidelberg (2004)
Variano, E.A., McCoy, J.H., Lipson, H.: Networks, dynamics and modularity. Physical Review Letters 92, 188701 (2004)
R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2004) ISBN 3-900051-07-0
Boerlijst, M., Hogeweg, P.: Self-structuring and selection: Spiral waves as a substrate for prebiotic evolution. In: Langton, C.G., Taylor, C., Farmer, J.D., Rasmussen, S. (eds.) Artificial Life II. Santa Fe Institute Studies in the Sciences of Complexity, Proceedings, vol. X, pp. 255–276. Addison-Wesley, Reading (1992)
Kumar, S., Bentley, P.J. (eds.): On Growth, Form and Computers. Elsevier Academic Press, Amsterdam (2003)
Repsilber, D., Kim, J.T.: Developing and testing methods for microarray data analysis using an artificial life framework. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 686–695. Springer, Heidelberg (2003)
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Kim, J.T. (2005). Effects of Spatial Growth on Gene Expression Dynamics and on Regulatory Network Reconstruction. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds) Advances in Artificial Life. ECAL 2005. Lecture Notes in Computer Science(), vol 3630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553090_83
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DOI: https://doi.org/10.1007/11553090_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28848-0
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