Development and application of efficient pathway enumeration algorithms for metabolic engineering applications

Area of Science:

• Metabolic Engineering
• Synthetic Biology
• Computational Biology

Background:

• Designing microbial cell factories for valuable compound production requires identifying suitable heterologous pathways.
• Existing algorithms for pathway discovery employ diverse approaches like constraint-based modeling and graph-based methods.
• Focus is on pathway enumeration and evaluation, not solely on optimizing specific functions.

Purpose of the Study:

• To analyze and enhance graph-based pathway enumeration algorithms for metabolic engineering.
• To evaluate the Solution Structure Generation and Find Path algorithms on synthetic metabolic engineering case studies.
• To improve the scalability and pathway extraction capabilities of these algorithms.

Main Methods:

• Utilized graph-based representations for pathway enumeration.
• Applied Solution Structure Generation and Find Path algorithms.
• Implemented targeted improvements to enhance scalability and database searching.
• Tested methods on case studies for 1-butanol, curcumin, and vanillin production in E. coli and S. cerevisiae.

Main Results:

• Both algorithms successfully identified known pathways for target compound production.
• Novel, alternative pathways were discovered, offering new metabolic engineering solutions.
• Improvements enhanced the algorithms' ability to handle large-scale databases and scalability.
• The methods proved effective for designing heterologous pathways in microbial hosts.

Conclusions:

• Enhanced graph-based algorithms are effective tools for discovering known and novel heterologous pathways in metabolic engineering.
• The implemented improvements address limitations in scalability and database extraction.
• These refined methods can accelerate the design of microbial cell factories for compound production.