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An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux
Sean M Halper1, Daniel P Cetnar1, Howard M Salis2,3
1Department of Chemical Engineering, Pennsylvania State University, University Park, PA, 16802, USA.
Methods in Molecular Biology (Clifton, N.J.)
|November 25, 2017
Summary
Designing efficient metabolic pathways is complex. This study introduces an automated computational-experimental pipeline to optimize DNA sequences for robust, high-yield enzyme pathways, minimizing bottlenecks and extensive testing.
Area of Science:
- Synthetic Biology
- Metabolic Engineering
- Computational Biology
Background:
- Engineering multi-enzyme metabolic pathways is challenged by the vast number of possible DNA sequences, leading to suboptimal performance and metabolic bottlenecks.
- Traditional methods require extensive screening and iterative design-build-test cycles, which are inefficient and costly.
Purpose of the Study:
- To develop an automated computational-experimental pipeline for designing optimal DNA sequences for multi-enzyme metabolic pathways.
- To overcome the 'curse of dimensionality' in pathway engineering by efficiently mapping sequence-expression-activity relationships.
- To predict DNA sequences that yield desired metabolic fluxes in bacterial hosts.
Main Methods:
- Utilized an integrated pipeline combining Operon Calculator, RBS Library Calculator, and Pathway Map Calculator algorithms.
- Operon Calculator designs host-specific, evolutionarily robust operon sequences with tunable expression.
- RBS Library Calculator minimizes library size for systematic variation of enzyme expression levels.
- Pathway Map Calculator parameterizes a kinetic metabolic model using experimental data to predict optimal sequences and expression levels.
Main Results:
- The pipeline efficiently maps the sequence-expression-activity space of metabolic pathways.
- It predicts optimal DNA sequences and enzyme expression levels for desired metabolic fluxes.
- This approach avoids high-throughput screening and reduces the number of design-build-test cycles.
Conclusions:
- The developed Pathway Optimization Pipeline offers an efficient method for designing robust and productive multi-enzyme metabolic pathways.
- This computational-experimental approach accelerates the optimization of synthetic biology constructs.
- The pipeline enables precise control over metabolic fluxes through optimized DNA sequences and enzyme expression.
