Systems metabolic engineering: genome-scale models and beyond

Area of Science:

• Bioinformatics
• Systems Biology
• Metabolic Engineering

Background:

• High-throughput bioinformatics generates vast cellular data.
• Systems metabolic engineering (SME) uses this data for target identification and phenotype optimization.
• Current SME tools struggle with complex traits like chemical tolerance.

Purpose of the Study:

• To review advances in SME, focusing on de novo genome-scale metabolic reconstructions.
• To explore the expansion of these models for novel organisms.
• To discuss challenges and prospects for model-based metabolic engineering.

Main Methods:

• In silico genome-scale metabolic reconstruction as a pragmatic SME tool.
• Review of current literature on SME approaches and their applications.
• Examination of limitations and future directions for predictive modeling.

Main Results:

• Genome-scale metabolic reconstructions are widely adopted for modeling cell growth and predicting gene knockouts.
• SME techniques offer a viable first step for enhancing product yield.
• Complex phenotypes remain challenging for current predictive models.

Conclusions:

• Expanding de novo genome-scale metabolic reconstructions is crucial for novel organisms.
• Model-based metabolic engineering requires integration with other techniques for optimal results.
• Further advancements are needed to fully address complex phenotypes in metabolic engineering.