DBTL

DBTL optimizes microbial strains through iterative design-build-test-learn cycles to improve metabolic production, exemplified by increasing 1-dodecanol production from glucose in Escherichia coli MG1655.

Key Features:

• Iterative cycles: Two iterative DBTL cycles were applied to iteratively modify genetic and metabolic components to increase dodecanol yield.
• Target pathway and host: Engineering focused on 1-dodecanol production from glucose in Escherichia coli MG1655.
• Operon composition: A single pathway operon included thioesterase UcFatB1, acyl-ACP/acyl-CoA reductase variants Maqu_2507, Maqu_2220, or Acr1, and acyl-CoA synthetase FadD.
• Regulatory tuning: Modulation of ribosome-binding sites was used to adjust expression levels within the pathway.
• Measurements: Concentrations of dodecanol and pathway proteins were quantified and used as experimental readouts.
• Machine-learning driven design: Data from the initial cycle informed machine-learning algorithms that suggested modifications for the second cycle.
• Performance improvement: Data-driven modifications yielded a 21% increase in dodecanol titer, reaching 0.83 g/L and representing more than a six-fold improvement over previously reported batch values in minimal medium.
• Sequencing verification: Sequencing checks were emphasized on both plasmids in production and cloning strains to ensure genetic fidelity and stability.
• Predictive expression tools: The study highlighted the need for improved predictive tools for protein expression to enhance metabolic engineering accuracy.

Scientific Applications:

• Metabolic engineering for biofuels: Optimization of microbial production of 1-dodecanol as a biofuel or biobased product from glucose.
• Strain development: Iterative DBTL application for systematic microbial strain optimization in synthetic biology.
• Data-driven design strategies: Use of experimental measurements to inform machine-learning-guided genetic modifications.
• Genetic fidelity assessment: Sequencing-based verification of plasmids in production and cloning strains to ensure stable constructs.

Methodology:

Concentrations of dodecanol and pathway protein measurements from the initial DBTL cycle were analyzed by machine-learning algorithms to propose modifications for the subsequent cycle, integrating experimental data with computational analysis.