This program consists of two parts: (a) two weeks of introductory lectures plus short projects and a computer lab, and (b) a summer long research experience (6 weeks to be followed immediately after the 2 weeks) devoted to projects in the interface of mathematics, statistics, and biological sciences.
Week 1: June 22 - 26, 2009 |
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| Monday 6/22: Statistical Phylogenetics | |||
| 8:30am | Registration and welcome | ||
| 9:00am-10:00am | Dennis Pearl: Statistical Phylogenetics I: Background, Sequence Alignment, Parsimony, Maximum Likelihood | ||
| 10:00am-10:30am | Coffee break | ||
| 10:30am-11:30am | Dennis Pearl: Statistical Phylogenetics II: Optimization algorithms, the Bootstrap, Bayesian Phylogenetics | ||
| 11:30am-2:00pm | Lunch break | ||
| 2:00pm-4:00pm | Computer lab: Lori Hoffman Software: PHYLIP, GARLI, & MrBayes |
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| Tuesday 6/23: Mathematical Neuroscience | |||
| 9:00am-10:00am | Michael Rempe: Introduction to Neuroscience | ||
| 10:00am-10:30am | Coffee break | ||
| 10:30am-11:30am | Michael Rempe: Mathematical Modeling and Neuroscience: Hodgkin-Huxley models and dynamical systems | ||
| 11:30am-2:00pm | Lunch break | ||
| 2:00pm-4:00pm | Computer lab Software: XPPAUT, MATLAB |
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| Wednesday 6/24: Chemogenomics | |||
| 9:00am-10:00am | Paul Blower: Introduction to Chemogenomics: chemical structures, bioassays, gene expression | ||
| 10:00am-10:30am | Coffee break | ||
| 10:30am-11:30am | Joe Verducci: Statistical Methods in Chemogenomics: measures of correlation, data mining for association | ||
| 11:30pm-2:00pm | Lunch break | ||
| 2:00pm-4:00pm | Computer lab with GRA Software: R, Tau-path programs |
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| Thursday 6/25: Environmental Statistics | |||
| 9:00am-10:00am | Kate Calder: Environmental Statistics I: Environmental Data, Exploratory Analyses, Statistical Modeling | ||
| 10:00am-10:30am | Coffee break | ||
| 10:30am-11:30am | Kate Calder: Environmental Statistics II: Statistical Methods in Environmental Health, Introduction to Bayesian Hierarchical Modeling | ||
| 11:30pm-2:00pm | Lunch break | ||
| 2:00pm-4:00pm | Computer lab: Candace Berrett Software: R |
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| Friday 6/26 | |||
| 9:00am-10:00am | Kun Huang: Bioinformatics - microarray data analysis I: Background, normalization, data visualization | ||
| 10:00am-10:30am | Coffee break | ||
| 10:30am-11:30am | Kun Huang: Bioinformatics - microarray data analysis II: Data clustering, gene network inference, gene set enrichment analysis | ||
| 11:30am-2:00pm | Lunch break | ||
| 2:00pm-4:00pm | Computer lab: Jie Zhang
Software: Matlab (w/ Bioinformatics and Statistics toolboxes), R (w/ Bioconductor) and DAVID (online tool) |
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Week 2: Lab Tours and Team Projects |
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| Tuesday 6/30 | |||
| 1:00pm | Joe Travers' Neuroscience Lab | ||
| Wednesday 7/1 | |||
| 1:00pm | Museum of Biological Diversity 1315 Kinnear Road - tour by museum director John Wenzel | ||
| 2:00pm | Aquatics Ecology Laboratory of Elizabeth Marschall at 1314 Kinnear Road | ||
| Thursday 7/2: Team Projects | |||
| 12:00-12:30pm | Students set up posters and prepare for presentations | ||
| 12:30-1:00pm | Poster viewing | ||
| 1:00-3:30pm | Oral presentations | ||
REU Presentations |
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| Friday 8/14 | |||
Dennis Pearl: The phylogenetics project will explore the evolution and global spread of the swine flu (H1N1) virus and how this evolution may be related to the clinical course of the disease.
Michael Rempe: The neuroscience project will explore a mathematical model of human sleep and investigate the effects of jet-lag on subsequent sleep timing.
Joe Verducci: The chemogenomics project will search the NCI database for combinations of genes that may affect the chemosensitivity of cancer cell-lines to a class of anti-cancer drugs.
Kate Calder: The environmental statistics project will examine regional differences in the health effects associated with particulate matter exposure using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS).
Kun Huang: The bioinformatics project will explore the gene co-expresion network in cancers using multiple microarray data sets for identifying new cancer biomarkers to predict prognosis.