Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network.
BACKGROUND
Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology.


RESULTS
We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model.


CONCLUSIONS
After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.
Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Republic of Korea.
Hyun Uk
HU
Kim
Tae Yong
TY
Kim
Sang Yup
SY
Lee
D001425:Bacterial Outer Membrane Proteins / Q000235:genetics / Q000378:metabolism; D001499:Bayes Theorem; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism*; D055786:Gene Knockout Techniques; D016680:Genome, Bacterial; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D008954:Models, Biological
D001425:Bacterial Outer Membrane Proteins