From Microbes to Minds: Using a Neural-Bacteria Interface to Discover a Universal Code for Information Processing Across Scales of Biological Organization
TWCF Number
Project Duration
December 1 / 2017
- November 30 / 2020
Core Funding Area
Big Questions
North America
Amount Awarded
Grant DOI*

* A Grant DOI (digital object identifier) is a unique, open, global, persistent and machine-actionable identifier for a grant.

Celia Herrera-Rincon
Institution Tufts University

Biological systems, whether as simple a bacterial cell or as complex as a vertebrate brain, are constantly receiving information from their environments and processing it in order to make decisions. For example, in recent years, the importance of flourishing gut microbiota to the overall health has been universally acknowledged. Current studies on the brain-gut-microbiota axis have revealed several molecular mechanisms by which these components can communicate, but have largely neglected the informational aspects of this communication. In this project, Dr. Celia Herrera-Rincon will probe the nature of the information passed between simple bacteria and more complex neural cells as a means of exploring fundamental questions of information embodiment.

Dr. Herrera-Rincon will test two hypotheses: 

  1. that there is a universal, ancestral communication code that permits effective information transfer across biological kingdoms, and 
  2. that a computational analysis of cross-talk between bacteria and neural cells will reveal unique aspects of how information processing underlies the emergence of complex systems. 

To do so, she will design and construct the first integrated electrical-optical neural-bacteria interface, a versatile, multi-site stimulation and recording platform specifically suited to the extraction of information in real-time across biological entities. With this interface, Dr. Herrera-Rincon will conduct three sets of experiments with distinct aims.

Aim 1: Conduct experiments to monitor information transfer and provide stimuli into the bacterial-neural system.

Aim 2: Generate a quantitative model of cross-kingdom communication by computationally analyzing information metrics.

Aim 3: Manipulate active communication between the neural and bacterial components to assemble a wholistic picture of information dynamics.

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