On Bioelectric Algorithms: A Novel Application of Theoretical Computer Science to Core Problems in Developmental Biology

TL;DR

This paper introduces a new computational model for cellular bioelectricity, enabling analysis of complex pattern formation and information processing in biological tissues, with implications for developmental biology and synthetic biology.

Contribution

The paper presents the cellular bioelectric model (CBM), a novel computational framework capturing bioelectric interactions, and demonstrates its Turing completeness and ability to stabilize cell networks.

Findings

CBM captures key bioelectric interactions.

Cells in CBM are Turing complete.

Efficiently stabilizes cell networks into maximal independent sets.

Abstract

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.