Metabolic Objectives and Trade-Offs: Inference and Applications

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Cellular objectives are vital for modeling biological networks in areas like metabolic engineering and drug discovery.
• Cells manage resources to meet biological goals under environmental constraints, with objectives extending beyond biomass production to tissue function and homeostasis.
• Mammalian cells exhibit diverse metabolic objectives, including supporting tissue functions, development, and redox balance, not solely growth.

Purpose of the Study:

• To review methods for determining cellular metabolic objectives and trade-offs from multi-omics data.
• To bridge gene expression patterns with metabolic phenotypes for a comprehensive understanding of cellular behavior.
• To explore the integration of cellular objectives into advanced applications.

Main Methods:

• Leveraging advances in single-cell omics technologies.
• Employing sophisticated metabolic modeling techniques.
• Utilizing machine learning and deep learning algorithms for objective inference.

Main Results:

• Inference of cellular objectives at both transcriptomic and metabolic levels is now feasible.
• Integration of gene expression with metabolic phenotypes provides deeper biological insights.
• In silico models offer predictive power for cellular adaptation and response.

Conclusions:

• In silico models reveal cellular adaptation mechanisms to environmental changes, drug treatments, and genetic modifications.
• Incorporating cellular objectives into models has significant potential for personalized medicine and drug discovery.
• This approach advances systems biology, tissue engineering, and therapeutic development.