On Bioelectric Algorithms

Cellular bioelectricity describes the biological phenomenon in which cells in living tissue generate and maintain patterns of voltage gradients across their membranes 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. This paper applies techniques from distributed algorithm theory to help better understand how cells work together to form these patterns. To do so, 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 from the relevant biology research literature. We begin with symmetry breaking, analyzing a simple cell definition that when combined in single hop or multihop topologies, efficiently solves leader election and the maximal independent set problem, respectively - indicating that these classical symmetry breaking tasks are well-matched to bioelectric mechanisms. We then turn our attention to the information processing ability of bioelectric cells, exploring upper and lower bounds for approximate solutions to threshold and majority detection, and then proving that these systems are in fact Turing complete - resolving an open question about the computational power of bioelectric interactions

On the Power of Rounds : Explorations of the Heard-Of Model

Distributed computing studies which problems can be solved by communicating processes -- computers, people,.... Because communication can take many shapes, and because of its uncertainty, lots of different models exist. So many that it's easy to get lost. One way to deal with this overabundance constrains processes to use rounds: they repeatedly broadcast a message tagged with their current round number, wait for messages with this same round number, and then use them to compute their next state and change round. The Heard-Of model leverages this idea through heard-of predicates, which constrain which messages is received at which round. Yet this model lacks the attention that it deserves from the research community. I believe the reason lies on the following three unsolved problems: how to find the heard-of predicate corresponding to a given model, is anything lost in this translation, and how to prove general results on heard-of predicates. This thesis addresses all three