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Hypothesis and Theory ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Comput. Neurosci. | doi: 10.3389/fncom.2018.00007

Is Smaller Better?: A Proposal to Use Bacteria For Neuroscientific Modeling

 Archana Ram1* and Andrew Lo1
  • 1Massachusetts Institute of Technology, United States

Bacteria are easily characterizable model organisms with an impressively complicated set of abilities. Among them is quorum sensing, a cell-cell signaling system that may have a common evolutionary origin with eukaryotic cell-cell signaling. The two systems are behaviorally similar, but quorum sensing in bacteria is more easily studied in depth than cell-cell signaling in eukaryotes. Because of this comparative ease of study, bacterial dynamics are also more suited to direct interpretation than eukaryotic dynamics, e.g. those of the neuron. Here we review literature on neuron-like qualities of bacterial colonies and biofilms, including ion-based and hormonal signaling, and a phenomenon similar to the graded action potential. This suggests that bacteria could be used to help create more accurate and detailed biological models in neuroscientific research. More speculatively, bacterial systems may be considered an analogue for neurons in biologically based computational research, allowing models to better harness the tremendous ability of biological organisms to process information and make decisions.

Keywords: Quorum Sensing, Neural Networks (Computer), Bacillus subtilis, cell-cell communication, network models

Received: 29 Sep 2017; Accepted: 17 Jan 2018.

Edited by:

David Holcman, École Normale Supérieure, France

Reviewed by:

Leslie S. Smith, University of Stirling, United Kingdom
Selena Bartlett, Translational Research Institute, Australia  

Copyright: © 2018 Ram and Lo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Archana Ram, Massachusetts Institute of Technology, Cambridge, United States,