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Cell Pattern Generation in Artificial Development
Author(s) -
Arturo Chavoya
Publication year - 2008
Publication title -
intech ebooks
Language(s) - English
Resource type - Book series
DOI - 10.5772/6043
Subject(s) - computer science , artificial intelligence
In biological systems, development is a fascinating and very complex process that involves following an extremely intricate program coded in the organism's genome. One of the crucial stages in the development of an organism is that of pattern formation, where the fundamental body axes of the individual are outlined. It is now evident that gene regulatory networks play a central role in the development and metabolism of living organisms. Moreover, it has been discovered in recent years that the diverse cell patterns created during the developmental stages are mainly due to the selective activation and inhibition of very specific regulatory genes. Over the years, artificial models of cellular development have been proposed with the objective of understanding how complex structures and patterns can emerge from one or a small group of initial undifferentiated cells. An artificial development model that generates cell patterns by means of the selective activation and inhibition of development genes under the constraints of morphogenetic gradients is proposed here. Cellular growth is achieved through the expression of structural genes, which are in turn controlled by an Artificial Regulatory Network (ARN) evolved by a Genetic Algorithm (GA). The ARN determines when cells are allowed to grow and which gene to use for reproduction, while morphogenetic gradients constrain the position at which cells can replicate. Both the ARN and the structural genes constitute the artificial cell's genome. In order to test the functionality of the development program found by the GA, the evolved genome was applied to a cellular growth testbed that has been successfully used in the past to develop simple 2D and 3D geometrical shapes (Chavoya & Duthen, 2006b). The artificial development model for cell pattern generation was based on the cellular automata (CA) paradigm. CA have previously been used to study form generation, as they provide an excellent framework for modelling local interactions that give rise to emergent properties in complex systems. Morphogenetic gradients were used to provide cells with positional information that constrained cellular replication. After a genome was evolved, a single cell in the middle of the CA lattice was allowed to reproduce until a cell pattern was formed. The model was applied to the canonical problem in cellular development of growing a French flag pattern.

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