Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae Speaker: Zhu YaNG 6th step, 2006
Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae Speaker: Zhu YANG 6 th step, 2006
Reference · Herrgard,M小.Lee,B.s., Portnoy,V,and Palsson, B.O. 2006. Integrated analysis of regulatory and metabolic networks reveals novel regulatory meChanisms in Saccharomyces cerevisiae Genome Research,16:627-635
Reference • Herrgard, M.J., Lee, B.-S., Portnoy, V., and Palsson, B.O. 2006. Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae Genome Research, 16: 627 – 635. 16: 627
Outline Background Approach model-based analysis Data and information Reconstructed transcriptional regulatory network Prediction of gene expression changes Systematic expansion of the regulatory network Prediction of growth phenotypes Discussion ·Conc| usIons
Outline • Background • Approach model-based analysis • Data and information • Reconstructed transcriptional regulatory network • Prediction of gene expression changes • Systematic expansion of the regulatory network • Prediction of growth phenotypes • Discussion • Conclusions
Background Vith the rapidly increasing biological productions, the data integration and interpretation task is made challenging by the incompleteness and noisiness of large-scale data sets literature-derived information has enabled the reconstruction of chemically and biologically consistent mathematical descriptions of biochemical networks in well-studied model organisms. and Furthermore model predictions can be directly compared with experimental data obtained Using a reconstructed genome-scale stoichiometric matrix as a starting point, the constraint-based modeling framework can then be used to make phenotypic predictions that can be compared to experimental data requently used constraint-based approaches include flux balance analysis(FBA) and regulated flux-balance analysis (rF BA) approach
Background • With the rapidly increasing biological productions, the data integration and interpretation task is made challenging by the incompleteness and noisiness of large-scale data sets. • literature-derived information has enabled the reconstruction of chemically and biologically consistent mathematical descriptions of biochemical networks in well-studied model organisms. And Furthermore, model predictions can be directly compared with experimental data obtained. • Using a reconstructed genome-scale stoichiometric matrix as a starting point, the constraint-based modeling framework can then be used to make phenotypic predictions that can be compared to experimental data. Frequently used constraint-based approaches include flux balance analysis (FBA) and regulated flux-balance analysis (rFBA) approach
Approach model-based analysis In vivol Regulatory and rimental metabolic network system model Compare system states Growth rates Expression profiles cat8△ Model Rgt1 ify condition-TF KO pairs with mispredictions Identify missing regulatory mechanisms using interaction data Refine model ChIP-chip Sequence motifs iterative 2 c'C
Approach model-based analysis