Queuing Models for Abstracting Interactions in Bacterial Communities


Microbial communities play a important role in bioremediation, plant growth, human and animal digestion, world elemental cycles including the carbon-cycle, and water treatment. They're additionally posed to be the engines of renewable energy via microbial fuel cells, which will reverse the process of electrosynthesis. Microbial communication regulates several virulence mechanisms employed by bacteria. Thus, it is of fundamental importance to understand interactions in microbial communities and to develop predictive tools that facilitate management them, in order to assist the planning of systems exploiting bacterial capabilities. This position paper explores how abstractions from communications, networking and info theory will play a task in understanding and modeling bacterial interactions. In specific, 2 kinds of interactions in bacterial systems will be examined: electron transfer and quorum sensing. Whereas the diffusion of chemical signals has been heavily studied, electron transfer occurring in living cells and its role in cell-cell interaction is a smaller amount understood. Recent experimental observations open up new frontiers in the planning of microbial systems based on electron transfer, which might coexist with the additional well-known interaction ways primarily based on molecular diffusion. In quorum sensing, the concentration of sure signature chemical compounds emitted by the bacteria is employed to estimate the bacterial population size, so as to activate collective behaviors. During this position paper, queuing models for electron transfer are summarized and tailored to produce 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 enable the characterization of the state of a single cell as a function of interactions with other cells and therefore the environment, therefore enabling the construction of an information theoretic framework, while being amenable to complexity reduction using methods - ased on statistical physics and wireless network style.

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