Columbia Technology Ventures

Engineered bacteria with spatial control for patterning and information readout

This technology is an in vitro platform for biosensing of various stimuli, encoding the readout in the spatial distribution of the engineered bacterial strain.

Unmet Need: Living biosensor to robustly detect and record spatial distribution of stimuli

Molecular detection is employed in a wide variety of fields, from detection of water safety to the diagnosis of disease. Typically, the output of these systems is colorimetric or fluorometric, but the need for sensation of diverse and complex signals is expanding. Bacterial cells offer a unique opportunity to expand the current capabilities of sensors, for their properties as a living cell can be engineered to detect a complex combination of target molecules or patterns. One untapped facet of living biosensors is the ability to change spatial organization as a response.

The Technology: High-throughput and robust synthetic bacterial-based biosensor platform

This technology utilizes the swarming genes of Proteus mirabilis, which function to produce unique swarming behavior and patterns when exposed to predetermined chemical stimuli. The data generated from the patterns can then be analyzed for detection or diagnosis of various stimuli, as well as information encoding within the patterning. This novel biosensor can be multiplexed with different engineered strains to detect various stimuli and encode complex information to be interpreted visually or through machine learning.

This technology has been validated in vitro.

Applications:

  • Diagnostic sensor for pathogenic bacteria
  • Spatial detection for antibiotic resistance, contaminants and toxins
  • Visual detection of horizontal gene transfer
  • Localization of toxin production
  • Research tool for studying biofilm formation

Advantages:

  • Simple and high-throughput platform for bacterial behavior analysis
  • Can create unique patterning for different stimuli
  • Highly reproducible
  • Can combine computational modelling systems to convert data outputs from swarming patterns

Lead Inventor:

Tal Danino, Ph.D.

Patent Information:

Patent Pending

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