Recent work:

T. E. Portegys, "Generating an artificial nest building pufferfish in a cellular automaton through behavior decomposition", International Journal of Artificial Intelligence and Machine Learning. 2019.
Abstract:
A species of pufferfish builds fascinating circular nests on the sea floor to attract mates. This project simulates the nest building behavior in a cellular automaton using the Morphognosis model. The model features hierarchical spatial and temporal contexts that output motor responses from sensory inputs. By considering the biological neural network of the pufferfish as a black box, decomposing only its external behavior, an artificial counterpart can be generated. In this way a complex biological system producing a behavior can be filtered into a system containing only functions that are essential to reproduce the behavior. The derived system not only has intrinsic value as an artificial entity but also might help to ascertain how the biological system produces the behavior.
Code.

Thomas Portegys, Gabriel Pascualy, Richard Gordon, Steve McGrew, Bradly Alicea, "Morphozoic: cellular automata with nested neighborhoods as a metamorphic representation of morphogenesis", in "Multi-Agent Based Simulations Applied to Biological and Environmental Systems", ISBN: 978-1-5225-1756-6, February 2017
Abstract:
When an embryo develops, its cells exhibit interactions with other cells ranging from nearest neighbors to hormonal effects that can reach anywhere in the body, and are thus global in range. Some of these interactions are mechanical, and of intermediate range. Some involve propagating waves and some are hypothesized to involve diffusion gradients. We can generalize the concept of morphogenetic fields to encompass this full range of cell-cell interactions. These morphogenetic fields would then include the morphogenesis of cellular structures such as tissues and organs and the differentiation of the cells in them. Although there is a preponderance of evidence for the existence of morphogenetic fields of various types, there is also much that is unknown about many of them, including how they are expressed, sensed, and acted upon. Here we investigate a cellular automaton model, Morphozoic, which may be used to investigate the computational power of morphogenetic fields to foster the development of structures and cell differentiation. We are therefore assuming some equivalence between a living cell in an embryo and a cellular automaton cell. A cellular automaton (CA) is a network of elements (cells) that pass signals to one another, then send signals to other cells, depending on the signals that are received. Here we confine cells to a square grid as in ordinary cellular automata. The term "morphogenetic field" is used here to describe a generalized abstraction: a cell signals information about its state to its environment and is able to sense and act on signals from a subset of the other cells. The received set of signals determines the field value for the cell. The subsets are defined by nested neighborhoods that can represent local to global morphogenetic effects. Neighborhood signals are compacted into aggregated quantities, forming a precision gradient that caps the amount of information exchanged: signals from smaller, more local neighborhoods are thus more finely discriminated, while those from larger, more global neighborhoods are less so. Our model allows a cell to use this signaling scheme to determine and perform metamorphic actions, such as division, death, and state change (differentiation). An assembly of cells can thus cooperate to generate spatial and temporal structure. Unlike traditional cellular automata, such as the Game of Life, Morphozoic was found to be robust, redundant, and noise tolerant. Morphozoic is thus an enrichment of the space of cellular automata, which may give us some insight into the unsolved problem of how an embryo builds itself. The evidence for interactions over various distances in real embryos, up to global, is briefly reviewed. Applications of Morphozoic presented here include: (1) Conway's Game of Life, (2) cell regeneration, (3) evolution of a gastrulation-like sequence, (4) neuron pathfinding, and (5) a simulation of Turing's reaction-diffusion morphogenesis. An artificial neural network implementation also provides a noise tolerant generalization capability. Morphozoic provides a tool for exploring relationships between the whole and parts in many realms.
Code.