Computational design of auxotrophy-dependent microbial biosensors for combinatorial metabolic engineering experiments

Area of Science:

• Metabolic Engineering
• Synthetic Biology
• Computational Biology

Background:

• Combinatorial approaches in metabolic engineering aim to improve microbial chemical production by screening for enhanced phenotypes.
• A significant challenge is the costly and time-consuming screening of chemicals lacking easily detectable attributes.
• Microbial biosensors offer a potential solution for facilitating chemical detection and quantification.

Purpose of the Study:

• To develop novel computational methods for rationally designing microbial biosensors.
• To enable high-throughput screening of chemical-producing strains based on substrate auxotrophy.
• To predict engineering strategies for coupling target chemical synthesis with a detectable proxy metabolite.

Main Methods:

• Development of computational methods for microbial biosensor design.
• Utilizing substrate auxotrophy as a basis for biosensor design.
• Predicting metabolic engineering strategies for biosensor coupling.
• Validation using known genetic modifications in E. coli auxotrophic strains.

Main Results:

• A validated method for rationally designing microbial biosensors.
• Demonstrated potential for high-throughput screening of chemical production.
• Predicted improvements in chemical production rates compared to current rational design approaches.
• A Matlab implementation of the biosensor design method is available.

Conclusions:

• Novel computational tools can significantly enhance metabolic engineering efforts.
• Biosensor-based approaches offer a promising strategy for efficient strain development.
• This work advances the field of microbial chemical production through improved screening methodologies.