EPBI 473: Integrative Cancer Biology (Fall Semester, 2010)


Instructor:      Tom Radivoyevitch, Ph.D.

Department of Epidemiology and Biostatistics, BRB-G19

Tel: 368-1965; e-mail: txr24@case.edu  

Website: http://epbi-radivot.cwru.edu


Course website: http://epbi-radivot.cwru.edu/EPBI473/

Materials: http://epbi-radivot.cwru.edu/EPBI473/files/  (see contents of folders therein)

Prerequisites: BIOC 407 (general biochemistry), EPBI 432 (statistics).

Required Reading: Introductory Statistics with R (Dalgaard, 2008; Springer); Class notes & papers.

Meeting Place and Times: NOA 030 (School of Nursing), Mondays and Wednesdays, 10:00-11:15 AM, Monday 8/23/2010 – Wednesday 12/1/2010

Office Hours: Mondays and Wednesdays, 11:15 AM -1:00 PM
Grading: 50% class project, 20% homework and 30% (take home) mid-term exam.

Links:   http://www.r-project.org/; http://www.bioconductor.org/

(data)  http://seer.cancer.gov/ ; http://www.ncbi.nlm.nih.gov/geo/

(A-bomb survivor data) http://www.rerf.or.jp/library/dl_e/lss12ci.html


Course Description:  Nonlinear mathematical representations of cancer relevant processes will be analyzed and used to interpret data where available. Stochastic processes will be introduced for tumor cell numbers and DNA double strand breaks. SEER, A-bomb, omic and cytometry data will be analyzed.


Tentative Schedule:

Week 1:  Introduction to R. Install R (http://www.r-project.org/), the R packages ISwR and rjava, eclipse and StatET. Read setup.ppt and Longhow Lam’s StatET and Rcourse tutorials here. Also read the standard R documentation manual “An Introduction to R.”  

Week 2:  Basic statistical analyses with R.  Read Dalgaard front to back. Work through this R script.                  

Week 3:  Analysis of SEER data. Obtain SEER data, read documentation, see materials.

For each week below go to the corresponding subdirectory to get files for that week.

Week 4:  Japanese A-bomb survivor data analysis.  Obtain A-bomb data from RERF.

Week 5:  Biologically-based carcinogenesis modeling. Two stage clonal expansion modeling.

Week 6:  Microarray data analyses.  The Affy and limma Bioconductor packages.

Week 7:  DNA sequence data analyses.

Week 8:  Flow cytometry data analyses using Bioconductor.

Week 9:  Systems Biology Markup Language (SBML) models of cancer relevant processes. Purines, folates, dNTP supply, glycolysis, p53-MDM2, EGFR and the cell cycle.

Week 10:  CellDesigner, SBMLR and Copasi.  Applied to a cancer relevant system above

Week 11:  Models of DNA damage and repair. Stochastic chemical reaction models of DNA double strand breaks (DSBs) and their relationship to mean value ordinary differential equation models.  Theory of dual radiation action.

Week 12:  Project proposals. The focus here should be on background material.  

Week 13:  To be determined by proposals.

Week 14:  Tumor growth models in cancer therapy. This will include pharmacokinetic modeling.  

Week 15:  Class project presentations. The focus here should be on R code details.