Closing the loop: establishing an autonomous test-learn cycle to optimize induction of bacterial systems using a...

Area of Science:

• Synthetic biology
• Automation in biological research
• Computational biology

Background:

• Synthetic biology aims to create predictable biological parts for genetic assemblies.
• Automated robotic platforms accelerate data collection but analysis remains manual, hindering the design-build-test-learn (DBTL) cycle.
• Current DBTL cycles lack full automation, requiring human intervention for workflow refinement.

Purpose of the Study:

• To develop a software framework for a robotic platform to autonomously adjust experimental test parameters.
• To transform a static robotic platform into a dynamic system for closed-loop optimization.
• To enable fully automated design-build-test-learn cycles in synthetic biology.

Main Methods:

• Developed a software framework with components for data import and database writing.
• Implemented an optimizer that balances exploration and exploitation to select next measurement points.
• Utilized a robotic platform to optimize inducer concentration in *Bacillus subtilis* and inducer/feed release in *Escherichia coli*.

Main Results:

• The robotic platform autonomously optimized experimental parameters for *Bacillus subtilis* and *Escherichia coli* systems.
• Green fluorescent protein (GFP) production was optimized over multiple iterations.
• Evaluated learning algorithms for single and dual factor optimization within automated DBTL cycles.

Conclusions:

• The developed software framework enables dynamic, autonomous optimization of biological experiments on robotic platforms.
• This work moves synthetic biology closer to fully automated, circular DBTL cycles.
• Lessons learned from development and execution of automated DBTL cycles are shared.