Distributed computing inspired by biology

Biological systems are mastering the art of composing cells into colonies, tissues, and organisms. This article reviews striking similarities and differences between such biological systems and distributed computing systems, where computational units are composed to form larger systems with the goal of increasing computational power, enhancing system robustness, or overcoming spatial distances. A problem that recurs in many contexts in distributed systems is obtaining a consistent view of part of the system by its agents. Such problems, known as agreement problems in distributed computing, have been extensively studied across different computational models, varying, for example, in the extent to which the network is stable or dynamic. Motivated by the importance of agreement problems, we discuss examples ranging from simple to more complex cases, the latter in the context of optimization: agents solving graph optimization problems, searching for optima in arbitrary loss landscapes, and applying gradient-based techniques closely related to widely adopted artificial neural networks. We then discuss the reverse direction: distributed systems implemented with biological material. In particular, we detail a theoretical distributed computing model and algorithm targeted toward implementation in bacterial populations. We conclude with an outlook on what we consider the beginning of a promising intersection between distributed computing and biology, highlighting opportunities for both understanding natural systems and engineering novel distributed systems, both biological and in silico.