Engineered E. coli swarming for binary and analog input recording

Area of Science:

• Microbiology
• Synthetic Biology
• Biophysics

Background:

• Bacterial swarming enables self-organization into macroscale patterns.
• Escherichia coli (E. coli) has significant genetic tractability but underexplored swarming control.
• Developing robust methods for controlling E. coli swarming is crucial for applied biological systems.

Purpose of the Study:

• To engineer E. coli strains for centimeter-scale swarming patterns capable of recording environmental inputs.
• To investigate methods for modulating swarming behavior in response to chemical and optical signals.
• To develop computational tools for decoding and analyzing bacterial swarming patterns.

Main Methods:

• Modulating the expression of swarming-related genes in E. coli.
• Utilizing chemical and optical signals to control swarm pattern formation (analog or binary-like).
• Developing scalable computational methods including feature extraction, regression, and deep learning for pattern analysis.
• Employing time-lapse imaging for dynamic recording and early-stage classification of inputs.

Main Results:

• Successfully generated E. coli strains producing centimeter-scale swarming patterns.
• Demonstrated dynamic recording of environmental inputs by bacterial colonies.
• Achieved early-stage classification of inputs based on observed swarming patterns.
• Established methods for reshaping swarming patterns in response to external signals.

Conclusions:

• This work presents a novel strategy for spatial information recording using E. coli.
• The engineered system expands the possibilities for programming emergent microbial behaviors at macroscopic scales.
• The developed computational tools enable decoding of complex bacterial patterns for data recording applications.