Using DNA to Generate 3D Organic Art Forms
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Abstract
A novel biological software approach to define and evolve 3D computer art forms is described based on a re-implementation of the FormGrow system produced by Latham and Todd at IBM in the early 1990’s. This original work is extended by using DNA sequences as the input to generate complex organic-like forms. The translation of the DNA data to 3D graphic form is performed by two contrasting processes, one intuitive and one informed by the biochemistry. The former involves the development of novel, but simple, look-up tables to generate a code list of functions such as the twisting, bending, stacking, and scaling and their associated parametric values such as angle and scale. The latter involves an analysis of the biochemical properties of the proteins encoded by genes in DNA, which are used to control the parameters of a fixed FormGrow structure. The resulting 3D data sets are then rendered using conventional techniques to create visually appealing art forms. The system maps DNA data into an alternative multi-dimensional space with strong graphic visual features such as intricate branching structures and complex folding. The potential use in scientific visualisation is illustrated by two examples. Forms representing the sickle cell anaemia mutation demonstrate how a point mutation can have a dramatic effect. An animation illustrating the divergent evolution of two proteins with a common ancestor provides a compelling view of an evolutionary process lost in millions of years of natural history.
Keywords
Sickle Cell Anaemia Cellular Automaton Ancestral Sequence Shape Grammar Argininosuccinate LyasePreview
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References
- 1.Todd, S., Latham, W.: Evolutionary Art and Computers. Academic Press, London (1992)zbMATHGoogle Scholar
- 2.Latham, W.: Form Synth. In: Computers in Art, Design and Animation, Springer, Heidelberg (1989)Google Scholar
- 3.Latham, W., Todd, S.: Computer sculpture. IBM Systems Journal 28(4), 682–688 (1989)Google Scholar
- 4.Burridge, J.M., et al.: The WINSOM solid modeller. IBM Systems Journal 28(4) (1989)Google Scholar
- 5.Todd, S., Latham, W.: Artificial life or surreal art? In: Varela, F.J., Bourgine, P. (eds.) Toward a Practice of Autonomous Systems (A Bradford Book), pp. 504–513. MIT Press, Cambridge (1992)Google Scholar
- 6.Bentley, P.J. (ed.): Evolutionary Design by Computers. Morgan Kaufmann, San Francisco (1999)zbMATHGoogle Scholar
- 7.Sims, K.: Artificial evolution for computer graphics. Computer Graphics 25(4) (1991)Google Scholar
- 8.Sims, K.: Evolving 3D morphology and behavior. In: Proc. of Artificial Life IV (1994)Google Scholar
- 9.Prusinkiewicz, P., Lindenmayer, A.: The Algorithmics Beauty of Plants. Springer, Heidelberg (1990)Google Scholar
- 10.Leyton, M.: A process grammar for shape. A.I. Journal 34(2), 213–247 (1988)Google Scholar
- 11.Leyton, M.: A Generative Theory of Shape. LNCS, vol. 2145. Springer, Heidelberg (2001)zbMATHGoogle Scholar
- 12.Whitelaw, M.: Metacreation — Art and Artificial Life. MIT Press, Cambridge (2004)Google Scholar
- 13.Leymarie, F.F.: Aesthetic computing and shape. In: Fishwick, P. (ed.) Aesthetic Computing. Leonardo Books, pp. 259–288. MIT Press, Cambridge (2006)Google Scholar
- 14.Lord, E.A., Wilson, C.B.: Math. Description of Shape and Form. Halsted Press (1984)Google Scholar
- 15.Lindenmayer, A.: Mathematical models for cellular interactions in development: Parts I and II. Journal of Theoretical Biology 18, 280–315 (1968)CrossRefGoogle Scholar
- 16.Ferraro, P., et al.: Toward a quantification of self-similarity in plants. Fractals 13(2) (2005)Google Scholar
- 17.Wolfram, S.: Cellular Automata and Complexity: Collected Papers. Addison-Wesley, Reading (1994)zbMATHGoogle Scholar
- 18.Deutsch, A., Dormann, S.: Cellular Automaton Modeling of Biological Pattern Formation. In: Modeling and Simulation in Science, Engineering and Technology, Birkhäuser (2005)Google Scholar
- 19.McCormack, J.: Aesthetic evolution of L-systems revisited. In: Raidl, G.R., et al. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 477–488. Springer, Heidelberg (2004)Google Scholar
- 20.Dawkins, R.: The Blind Watchmaker. Penguin Books (1986)Google Scholar
- 21.Kumar, S., Bentley, P.J. (eds.): On Growth, Form and Computers. Elsevier, Amsterdam (2003)Google Scholar
- 22.Taylor, W.R.: The classification of amino acid conservation. J. Theor. Biology 119 (1986)Google Scholar
- 23.Shamim, M.T.A., et al.: Support vector machine-based classification of protein folds. Bioinformatics 23(24), 3320–3327 (2007)CrossRefGoogle Scholar
- 24.Cai, W., Pei, J., Grishin, N.V.: Reconstruction of ancestral protein sequences and its applications. BMC Evolutionary Biology 4(33) (2004)Google Scholar
- 25.Finn, R.F., et al.: Pfam: clans, web tools & services. Nucleic Acids Res. 34, D247–51 (2006)Google Scholar
- 26.Wu, C.H., et al.: The universal protein resource. Nucleic Acids Res. 34, D187–91 (2006)Google Scholar