期刊
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
卷 32, 期 12, 页码 2402-2416出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2014.2367666
关键词
Queuing theory; energy harvesting; Markov chain; bacterial communication; electron transfer; cell energetics; molecular diffusion
资金
- NASA [NNA13AA92A]
- Division of Chemical Sciences, Geosciences, and Biosciences, Office of Basic Energy Sciences, US Department of Energy [DE-FG02-13ER16415]
- Italian Association of Electrical Engineering (AEIT)
- [ONR N00014-09-1-0700]
- [CCF-0917343]
- [CCF-1117896]
- [CNS-1213128]
- [AFOSR FA9550-12-1-0215]
- [DOT CA-26-7084-00]
Biological systems are known to communicate by diffusing chemical signals in the surrounding medium. However, most of the recent literature has neglected the electron transfer mechanism occurring among living cells, and its role in cell-cell communication. Each cell relies on a continuous flow of electrons from its electron donor to its electron acceptor through the electron transport chain to produce energy in the form of the molecule adenosine triphosphate, and to sustain the cell's vital operations and functions. While the importance of biological electron transfer is well-known for individual cells, the past decade has also brought about remarkable discoveries of multi-cellular microbial communities that transfer electrons between cells and across centimeter length scales, e.g., biofilms and multi-cellular bacterial cables. These experimental observations open up new frontiers in the design of electron-based communications networks in microbial communities, which may coexist with the more well-known communication strategies based on molecular diffusion, while benefiting from a much shorter communication delay. This paper develops a stochastic model that links the electron transfer mechanism to the energetic state of the cell. The model is also extensible to larger communities, by allowing for electron exchange between neighboring cells. Moreover, the parameters of the stochastic model are fit to experimental data available in the literature, and are shown to provide a good fit.
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