Morphogenesis, the establishment and repair of emergent complex anatomy by
groups of cells, is a fascinating and biomedically-relevant problem. One of its
most fascinating aspects is that a developing embryo can reliably recover from
disturbances, such as splitting into twins. While this reliability implies some
type of goal-seeking error minimization over a morphogenic field, there are
many gaps with respect to detailed, constructive models of such a process being
used to implement the collective intelligence of cellular swarms. We describe a
closed-loop negative-feedback system for creating reaction-diffusion (RD)
patterns with high reliability. It uses a cellular automaton to characterize a
morphogen pattern, then compares it to a goal and adjusts accordingly,
providing a framework for modeling anatomical homeostasis and robust generation
of target morphologies. Specifically, we create a RD pattern with N
repetitions, where N is easily changeable. Furthermore, the individual
repetitions of the RD pattern can be easily stretched or shrunk under genetic
control to create, e.g., some morphological features larger than others.
Finally, the cellular automaton uses a computation wave that scans the
morphogen pattern unidirectionally to characterize the features that the
negative feedback then controls. By taking advantage of a prior process
asymmetrically establishing planar polarity (e.g., head vs. tail), our
automaton is greatly simplified. This work contributes to the exciting effort
of understanding design principles of morphological computation, which can be
used to understand evolved developmental mechanisms, manipulate them in
regenerative medicine settings, or embed a degree of synthetic intelligence
into novel bioengineered constructs.Comment: 20 pages, 3 tables, 5 figure