Is Smaller Better? A Proposal to Use Bacteria For Neuroscientific Modeling
Name
fncom-12-00007.pdf
Description
Published version
Size
1.07 MB
Format
Adobe PDF
Checksum (MD5)
8f520dc66e1ee7a849615e59e71d07d8
Author(s)
Ram, Archana
Lo, Andrew W
Date Issued
February 23, 2018
Journal
Frontiers in Computational Neuroscience
Publisher
Frontiers Research Foundation
Citation
Ram, Archana, and Andrew W. Lo. “Is Smaller Better? A Proposal to Use Bacteria For Neuroscientific Modeling.” Frontiers in Computational Neuroscience, 12, 7 (February 23, 2018): © 2018 Ram and Lo
Version
Final published version
Abstract
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 reviewliterature 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 analog 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; networkmodels
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Sloan School of Management
Sloan School of Management. Laboratory for Financial Engineering
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/fncom.2018.00007
