Shaping bacterial population behavior through computer-interfaced control of individual cells.

Area of Science:

• Microbiology and Synthetic Biology
• Quantitative Biology
• Biotechnology

Background:

• Bacteria exhibit individual variation and interact within populations, influencing gene expression at a population level.
• Understanding these complex behaviors necessitates single-cell resolution for measurement and control, coupled with defined environmental conditions.
• Previous methods lacked the integrated capabilities for dynamic, high-throughput single-cell analysis and manipulation.

Purpose of the Study:

• To present an automated, programmable platform for high-resolution analysis of bacterial populations.
• To enable precise control over individual cell gene expression and environmental conditions.
• To bridge the gap between individual cell behaviors and emergent population-level phenomena.

Main Methods:

• An integrated platform combining image-based gene expression and growth measurements.
• On-line optogenetic control of gene expression in hundreds of individual Escherichia coli cells.
• Dynamically adjustable environmental conditions and closed-loop control systems.

Main Results:

• Demonstrated population structuring through independent, closed-loop control of individual cell gene expression.
• Showcased control of cell-cell variation during antibiotic perturbations.
• Implemented hybrid bio-digital circuits and digital communication between individual bacteria.

Conclusions:

• The developed platform broadly enables experiments linking individual and population behaviors in microbial systems.
• Real-time integration of theoretical models with measurement and control facilitates investigation and engineering of microbial populations.
• This technology opens new avenues for synthetic biology and understanding microbial community dynamics.