PPT-Model SEED Resource for the Generation, Optimization, and Analysis of Genome-scale Metabolic

Christopher Henry Matt DeJongh Aaron Best Ross Overbeek and Rick Stevens Presented by Christopher Henry Pathway Tools Workshop October 2010 Metabolic Modeling is One Key to Predicting Phenotype from Genotype. Veronika Vonstein. Fellowship for Interpretation of Genomes. December 2008. Your genome after RAST. SEED genome in RAST. Use Function based . c. omparison . t. ool. Choose genome to compare to. Compare Metabolic Reconstruction of your genome (A) and the SEED genome (B). – where are we in 2011?. What I aim to do in 30 minutes.... . Give you a brief intro into our system of study.. . Recap the things we talked about in York in York in 2009.. Think about what we could do in Edinburgh in 2011. multilinear. gradient elution in HPLC with Microsoft Excel Macros. Aristotle University of Thessaloniki. A. . Department of Chemistry, Aristotle University of . Thessaloniki. B. Department of Chemical Engineering, Aristotle University of Thessaloniki. Introduction. In many complex optimization problems, the objective and/or the constraints are . nonlinear functions . of the decision variables. Such optimization problems are called . nonlinear programming . Jintao . Meng. , . Sangmin. . Seo. , . Pavan. . Balaji. , . Yanjie. Wei, . Bingqiang. Wang, . Shengzhong. . Feng. Joint work with. Shenzhen . Institutes of Advanced Technology(SIAT), . CAS, China. Genome: the total number of genes in an individual.. Human Genome- approx. 20,000 genes on the 46 human chromosomes.. Human Genome Project (HGP). Ongoing effort to completely map and sequence our genome.. Introduction. In many complex optimization problems, the objective and/or the constraints are . nonlinear functions . of the decision variables. Such optimization problems are called . nonlinear programming . Jintao . Meng. , . Sangmin. . Seo. , . Pavan. . Balaji. , . Yanjie. Wei, . Bingqiang. Wang, . Shengzhong. . Feng. Joint work with. Shenzhen . Institutes of Advanced Technology(SIAT), . CAS, China. Novelty 1: Ice thickness is allowed to vary during the optimization (but constrained by observational uncertainties) to provide another degree of freedom. Probabilistic Sea-Level Projections from Ice Sheet and Earth System Models 3:  Derek M Bickhart . Animal Genomics and Improvement Laboratory . Research Geneticist (Animal) . derek.bickhart@ars.usda.gov . Phone: (301) 504-8679 Fax: (301) 504-8092. USDA disclaimer. Disclaimers: Mention of trade names, commercial products, or companies in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture over others not mentioned. . Alex Rodriguez * Dinanath Sulakhe Elizabeth Marland arodri7@mcs.anl.gov sulakhe@mcs.anl.gov marland@mcs.anl. Veronika Nefedova Gong Xin Yu Natalia Maltsev * nefedova@mcs.anl.gov gxyu@mcs.anl.g Jianlin. . Jack . Cheng. Computer Science Department. University of Missouri, . Columbia, USA. Mexico, 2014. Large-Scale Model Sampling. Targeted. Sampling. Fold Space. Alignment Space. Model Pool. Sequence Space. Parameter estimation, gait synthesis, and experiment design. Sam Burden, Shankar . Sastry. , and Robert Full. Optimization provides unified framework. 2. ?. ?. ?. ?. ?. Blickhan. & Full 1993. Srinivasan. http://ncgas.org. . Carrie . Ganote. Ram . Podicheti. Le-Shin Wu. Tom . Doak. Quality Control and Assessment of. RNA-. Seq. Data. National Center for Genome Analysis Support: . http://ncgas.org. .