Queuing Models for Abstracting Interactions in Bacterial Communities

Abstract

Microbial communities play a significant role in bioremediation, plant growth, human and animal digestion, global elemental cycles including the carbon-cycle, and water treatment. They are also posed to be the engines of renewable energy via microbial fuel cells, which can reverse the process of electrosynthesis. Microbial communication regulates many virulence mechanisms used by bacteria. Thus, it is of fundamental importance to understand interactions in microbial communities and to develop predictive tools that help control them, in order to aid the design of systems exploiting bacterial capabilities. This position paper explores how abstractions from communications, networking and information theory can play a role in understanding and modeling bacterial interactions. In particular, two forms of interactions in bacterial systems will be examined: electron transfer and quorum sensing . While the diffusion of chemical signals has been heavily studied, electron transfer occurring in living cells and its role in cell–cell interaction is less understood. Recent experimental observations open up new frontiers in the design of microbial systems based on electron transfer, which may coexist with the more well-known interaction strategies based on molecular diffusion. In quorum sensing, the concentration of certain signature chemical compounds emitted by the bacteria is used to estimate the bacterial population size, so as to activate collective behaviors. In this position paper, queuing models for electron transfer are summarized and adapted to provide new models for quorum sensing. These models are stochastic, and thus capture the inherent randomness exhibited by cell colonies in nature. It is shown that queuing models allow the characterization of the state of a single cell as a function of interactions with other cells and the environment, thus enabling the construction of an information theoretic framework, while being amenable to complexity reduction using methods based on statistical physics and wireless network design.