SensiPath: computer-aided design of sensing-enabling metabolic pathways

Area of Science:

• Synthetic Biology
• Metabolic Engineering
• Biosensor Development

Background:

• Genetically-encoded biosensors are crucial for synthetic biology, enabling compound detection in medical and environmental fields.
• Current biosensors are limited by the number of compounds detectable via natural mechanisms like allosteric transcription factors.
• Expanding the range of detectable compounds is essential for advancing biosensor applications.

Purpose of the Study:

• To present SensiPath, a web server designed to expand the repertoire of detectable compounds for biosensors.
• To enable the detection of compounds not directly recognized by natural sensing mechanisms.
• To broaden the utility of biosensors in synthetic biology and metabolic engineering.

Main Methods:

• SensiPath screens for multi-step enzymatic transformations to convert non-detectable compounds into detectable ones.
• The approach encodes reactions using signature descriptors to identify sensing-enabling metabolic pathways.
• It explores biochemical transformations leading to known transcription factor effectors.

Main Results:

• SensiPath provides a computational strategy to enlarge the set of detectable compounds.
• The server facilitates the design of biosensors for a wider array of molecules.
• It expands the design space for synthetic biology applications by enabling new sensing capabilities.

Conclusions:

• SensiPath significantly enhances the capabilities of genetically-encoded biosensors.
• The tool supports the development of novel biosensors for previously undetectable compounds.
• This work broadens the application scope of synthetic biology by expanding molecular detection.