  Cellular bioelectricity describes the biological phenomenon in which cells in
living tissue generate and maintain patterns of voltage gradients induced by
differing concentrations of charged ions. A growing body of research suggests
that bioelectric patterns represent an ancient system that plays a key role in
guiding many important developmental processes including tissue regeneration,
tumor suppression, and embryogenesis. Understanding the relationship between
high-level bioelectric patterns and low-level biochemical processes might also
enable powerful new forms of synthetic biology. A key open question in this
area is understanding how a collection of cells, interacting with each other
and the extracellular environment only through simple ligand bindings and ion
fluxes, can compute non-trivial patterns and perform non-trivial information
processing tasks. The standard approach to this question is to model a given
bioelectrical network as a system of differential equations and then explore
its behavior using simulation techniques. In this paper, we propose applying a
computational approach. In more detail, we present the cellular bioelectric
model (CBM), a new computational model that captures the primary capabilities
and constraints of bioelectric interactions between cells and their
environment. We use this model to investigate several important topics in
cellular bioelectricity, including symmetry breaking and information
processing. Among other results, we describe and analyze a basic bioelectric
strategy the efficiently stabilizes arbitrary cell networks into maximal
independent sets (a structure known to play a role in the nervous system
development of flys), and prove cells in our model are Turing complete in their
ability to process information encoded in their initial voltage potential.
