ECAL 2007: Advances in Artificial Life pp 163-171 | Cite as
Cell Tracking: Genesis and Epigenesis in an Artificial Organism
Abstract
This paper belongs to the field of Computational Development. It describes a method that has the objective to provide an effective way of generating arbitrary shapes by using evolutionary-developmental techniques, i.e. by evolving genomes that guide the development of the organism starting from a single cell. The key feature of the method is the explicit introduction of an epigenetic memory, that is a cell variable that is modified during the development process and can take different values in different cells. This variable represents the source of differentiation, that leads different cells to read out different portions of the genome at different times. Preliminary experiments have been performed and the results appear to be quite encouraging: the proposed method was able to evolve a number of 25x25, 32x48 and 64x64 target shapes.
Keywords
computational development morphogenesis epigenetic memoryPreview
Unable to display preview. Download preview PDF.
References
- 1.De Garis, H.: Artificial Embryology and Cellular Differentiation. In: Bentley, P.J. (ed.) Evolutionary Design by Computers, pp. 281–295. Academic Press, London (1999)Google Scholar
- 2.Eggenberger, P.: Evolving Morphologies of Simulated 3d Organisms Based on Differential Gene Expression. In: Husbands, P., Harvey, I. (eds.) Proceedings of the 4th European Conference on Artificial Life, MIT Press, Cambridge (1997)Google Scholar
- 3.Fontana, A., Fraccaro, W.: A Functional Model of Cell Genome. In: Proc. of Alife IX (2004)Google Scholar
- 4.Fontana, A.: A Functional Model of Development and Expression in an Artificial Organism. In: International Conference on Morphological Computation, Venice (2007)Google Scholar
- 5.Gershenson, C., Wuensche, A.: Tutorial: Introduction to random Boolean networks. In: Workshop and Tutorial Proc. of Alife IX (2004)Google Scholar
- 6.Gruau, F., Whitley, D., Pyeatt, L.: A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks. In: Genetic Programming 1996: Proceedings of the First Annual Conference (1996)Google Scholar
- 7.Kumar, S., Bentley, P.J.: On Growth, Form and Computers. Academic Press, London (2003)Google Scholar
- 8.Lindenmayer, A.: Mathematical models for cellular interaction in development I. Filaments with one-sided inputs. Journal of Theoretical Biology 18, 280–289 (1968)CrossRefGoogle Scholar
- 9.Miller, J.F.: Evolving Developmental Programs for Adaptation, Morphogenesis, and Self-Repair. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, Springer, Heidelberg (2003)Google Scholar
- 10.Stanley, K.O., Miikkulainen, R.: A Taxonomy for Artificial Embryogeny. Artificial Life 9(2), 93–130 (2003)CrossRefGoogle Scholar
- 11.Torres-Padilla, M.E., Parfitt, D.E., Kouzarides, T., Zernicka-Goetz, M.: Histone arginine methylation regulates pluripotency in the early mouse embryo. Nature (2007)Google Scholar