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High-Throughput Metabolic Profiling for Model Refinements of Microalgae

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Constraints-based genome-scale metabolic simulation for systems metabolic engineering.

Jong Myoung Park¹, Tae Yong Kim¹, Sang Yup Lee²

¹Department of Chemical and Biomolecular Engineering (BK21 Program), KAIST, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea; Metabolic and Biomolecular Engineering National Research Laboratory, KAIST, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea; BioProcess Engineering Research Center, KAIST, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea; Bioinformatics Research Center, KAIST, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea; Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, KAIST, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea.

Biotechnology Advances

|May 26, 2009

Summary

Systems metabolic engineering utilizes computational models to modify cellular metabolism. This review covers algorithms for simulating and perturbing cellular metabolism, guiding future research.

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Area of Science:

Biotechnology
Systems Biology
Metabolic Engineering

Background:

Traditional strain development relied on random mutagenesis and selection.
Metabolic engineering uses molecular techniques for targeted cellular modifications.
Advancements in systems biology and omics technologies enable systems-level engineering.

Purpose of the Study:

To review algorithms for system-wide simulation and perturbation of cellular metabolism.
To discuss the characteristics of these metabolic modeling algorithms.
To suggest future research directions in systems metabolic engineering.

Main Methods:

Development of in silico genome-scale metabolic models using genomic data, reactions, literature, and experimental data.
Utilizing various algorithms for simulating cellular metabolic status.
Applying these models for systematic strategies in metabolic engineering.

Main Results:

Genome-scale metabolic models are crucial for systematic metabolic engineering strategies.
Algorithms enable simulation and perturbation of cellular metabolism at a system-wide level.
Understanding algorithm characteristics is key for effective application.

Conclusions:

Systems metabolic engineering, powered by in silico models, is a powerful approach.
Algorithm development is advancing the field of cellular metabolism simulation.
Further research is needed to refine and expand these computational tools.