| 초록 |
As the abundant experimental data on gene function and regulatory interactions have been accumulated in the field of molecular biology due to recent advances in high-throughput technology many data-driven approaches have become necessary to uncover functional inference and genetic regulatory networks from the large data sets. Though the traditional flux balance analysis has successfully predicted intracellular fluxes using stoichiometry, linear programming, and metabolic pathways, it has not automatically reflected any potential genetic effects in response to the environmental changes in the metabolic pathways. Thus in order to overcome the difficulties of the binary system, weight-added regulatory networks were devised so that the influence of sensors/regulators on target genes could be represented more flexibly. The weight-added regulatory networks consisting of many weight-added sub-networks were developed and incorporated into the FBA to remove inappropriate metabolic reactions generated by inconsistent regulatory events and to generate the dynamic level of expression of genes under the given conditions. |
