Modeling regulatory networks using machine learning for systems metabolic engineering

Area of Science:

• Synthetic Biology
• Metabolic Engineering
• Computational Biology

Background:

• Systems metabolic engineering aims to optimize biological networks for sustainable chemical production.
• Genome-scale metabolic models are established, but regulatory network models are underutilized.
• Regulatory networks, at transcriptional and translational levels, are crucial for biological functions.

Purpose of the Study:

• To review recent advances in inferring and characterizing regulatory networks.
• To highlight the potential of computational methods for regulatory network optimization.
• To identify needs for advancing the application of regulatory network models in metabolic engineering.

Main Methods:

• Literature review of recent studies on regulatory network inference and characterization.
• Analysis of computational methods for modeling transcriptional and translational regulation.
• Discussion of the application of these models in systems metabolic engineering.

Main Results:

• Recent computational methods can effectively infer and characterize regulatory networks.
• These methods show promise for optimizing production hosts in metabolic engineering.
• The integration of regulatory network models can enhance biological production.

Conclusions:

• Regulatory network models are essential for advancing systems metabolic engineering.
• Further development and application of computational tools are needed.
• Generation of biological sequence-phenotype relationship datasets is critical for model success.