How do we understand and manipulate metabolic pathways?

Understanding and Manipulating Metabolic Pathways

Metabolic pathways are crucial for understanding the biochemical processes that sustain life. They involve complex interactions among enzymes and metabolites, and their manipulation can lead to significant advancements in fields such as biomedicine, biofuels, and biotechnology. This synthesis presents key insights from various research papers on how we understand and manipulate these pathways.

Key Insights

Identification and Analysis of Metabolic Pathways:
- Advances in modeling and stoichiometric analyses have improved our understanding of metabolic pathways and facilitated their manipulation .
- Computational methods and algorithms, such as those used in PathAligner, enable the retrieval and prediction of metabolic pathways from biological databases .
Predictive Methods and Tools:
- Novel methods utilizing chemical-chemical, chemical-protein, and protein-protein interactions can predict the metabolic pathways of small molecules and enzymes with high accuracy.
- Graph theory and pathfinding algorithms help infer relevant pathways in biochemical networks by avoiding irrelevant shortcuts through highly connected nodes.
Systemic and Theoretical Approaches:
- The concept of extreme pathways, derived from system stoichiometry and thermodynamics, provides a framework for analyzing and predicting metabolic functions from a pathway-oriented perspective .
- Metabolic Control Analysis (MCA) quantitatively determines the control exerted by enzymes on flux and metabolite concentrations, aiding in the successful manipulation of metabolic pathways.
Applications in Metabolic Engineering:
- Metabolic engineering involves the targeted modification of pathways to improve chemical transformations, energy transduction, and macromolecular synthesis, leading to applications in agriculture, pharmaceuticals, and environmental decontamination.
- Genome-scale metabolic networks and network-based pathway definitions facilitate the analysis of metabolic capabilities, robustness, and optimal growth rates, supporting the design of engineered biological systems.

Conclusion

Understanding and manipulating metabolic pathways involve a combination of computational modeling, predictive methods, and theoretical frameworks. Advances in these areas have led to improved methods for identifying and analyzing pathways, predicting their behavior, and engineering them for various applications. These insights collectively enhance our ability to harness metabolic pathways for scientific and industrial advancements.

How do we understand and manipulate metabolic pathways?

Summary

Understanding and Manipulating Metabolic Pathways

Key Insights

Conclusion

Sources and full results

Modelling and analysis of metabolic pathways.

Metabolic pathway modeling and analysis have improved our understanding and facilitated their manipulation, leading to better metabolic pathway manipulation and optimization.

Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Our method accurately predicts metabolic pathways of small molecules and enzymes using chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions.

Web-based information retrieval system for the prediction of metabolic pathways

PathAligner is a web-based system that predicts metabolic pathways by extracting information from biological databases and providing an easy-to-use interface for retrieving, displaying, and manipulating metabolic information.

Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective.

This study presents an algorithm and classification scheme for identifying systemically independent metabolic pathways, enabling the analysis and prediction of metabolic function from a pathway-oriented perspective.

Metabolic PathFinding: inferring relevant pathways in biochemical networks

This web server improves metabolic pathway inference by filtering out highly connected compounds and computing shortest paths on a weighted metabolic graph.

Manipulating the Steady State of Metabolic Pathways

Our algorithms effectively identify enzyme sets that can manipulate metabolic pathways, with the genetic algorithm finding solutions for large pathways in less than 10 minutes.

Metabolic Pathway Analysis: Basic Concepts and Scientific Applications in the Post‐genomic Era

Metabolic pathway analysis can help biochemists and biotechnologists better understand cellular metabolism and design new metabolic pathways using genomic data.

Metabolic Control Analysis: A Tool for Designing Strategies to Manipulate Metabolic Pathways

Metabolic Control Analysis (MCA) helps identify steps to modify to successfully alter flux or metabolite concentration in metabolic pathways of biotechnological or clinical relevance.

Metabolic pathways in the post-genome era.

Network-based metabolic pathways are emerging as an important paradigm for analyzing biological systems, enabling the design of engineered systems and generating testable hypotheses about network structure and function.

Metabolic Engineering of Pathways and Gene Discovery

Try another search