| dc.description.abstract | Plants, fungi, humans and all other multicellular organisms go through the same process of growing step by step. Starting as a single cell with a genome containing all the genetic information of the organism, they grow into the shape encoded in their genome with stunning accuracy. Not only do they grow into a shape, but a complex composition of cell types. Organisms also know when to stop growing, and some even have abilities to regrow damaged cells. The study of this process, called developmental biology, can provide insight useful for a range of disciplines, such as medicine and artificial intelligence. Computer science has a long history of benefiting from mimicking models of biology, and modern computing power provides tools to simulate biological models in ways that may benefit both fields. Simulations can provide insight and observations that are hard to catch otherwise. This thesis contributes to the tools capable of providing such insights, and aims to simulate morphogenesis by growing a single cell into a three-dimensional colored shape. The framework extends recent work simulating 2D morphogenesis using machine learning combined with an abstract computational system called cellular automatas (CA). In addition to the added dimensionality, we further extend the framework and propose a novel solution allowing guidance of the morphogenesis through certain checkpoints during training. We also experiment with a novel approach of training a simple 3D model to exhibit an oscillating motion, with promising results laying the foundation for future work exceeding past simulation of just morphogenesis. A formula for estimating a hyperparameter, the minimum number of updates a CA needs during training, is derived to provide a basis for future work on 3D neural cellular automatas (3D NCA). The framework is successfully adapted to the higher dimensionality and three-dimensional morphogenesis is simulated with high precision on a range of models covering different geometrical challenges. Both shape and color is correctly grown from a single cell, smaller models are indistinguishable from their targets, while larger models tend to have a few cells misplaced. We observe a significant increase in computational cost with the three-dimensional simulations, indicating that optimisation measures would be critical if using the framework on large scale simulations. In terms of simulating morphogenesis, the framework matches the performance of similar work published while this thesis was written, in this relatively narrow but fast evolving field. | eng |