2. Objective To learn how to use mathematical models and computer simulations to synthesize various forms of cancer relevant data to yield experimentally testable scientific hypotheses. Integrative Cancer Biology EPBI 473

3. Tomas Radivoyevitch, PhD Assistant Professor Epidemiology and Biostatistics Case Western Reserve University Office: BRB G-19 Tel: 216-368-1965 Email: radivot@hal.cwru.eduradivot@hal.cwru.edu Website: http://epbi-radivot.cwru.edu/http://epbi-radivot.cwru.edu/ Course website: http://epbi-radivot.cwru.edu/ICB/http://epbi-radivot.cwru.edu/ICB/Instructor

4. Prerequisites: general biochemistry, introductory statistics Required Reading: Introductory Statistics with R (Dalgaard, 2002); class notes & papers. Meeting Times: Tues. and Thurs. (4:00 pm to 5:15 pm) Office Hours (in BRB G-19): 2:00pm–5:30pm (Mon. and Wed.) Grading: 40% projects, 20% HWs and 40% Exams Links ICB http://icbp.nci.nih.gov/ http://plan.cancer.gov/biology.shtmlhttp://icbp.nci.nih.gov/http://plan.cancer.gov/biology.shtml Software http://www.r-project.org/ http://www.bioconductor.org/http://www.r-project.org/http://www.bioconductor.org/ Datasets http://www.rerf.or.jp/ http://seer.cancer.gov/ http://www.ncbi.nlm.nih.gov/geo/http://www.rerf.or.jp/http://seer.cancer.gov/http://www.ncbi.nlm.nih.gov/geo/ Course Information

5. Syllabus Introduction to R Introduction to R Epidemiological Cancer Datasets Epidemiological Cancer Datasets Gene Expression Analyses Gene Expression Analyses Biochemical Systems Biochemical Systems Pharmacokinetic Models Pharmacokinetic Models Tumor Growth and Invasion Tumor Growth and Invasion

6. Emphasis is on the stochastic component of the model. Is there something in the black box or are the input wires disconnected from the output wires such that only thermal noise is being measured? Do we have enough data? Model components: (Deterministic = signal) + (Stochastic = noise) Statistics Engineering Emphasis is on the deterministic component of the model We already know what is in the box, since we built it. The goal is to understand it well enough to be able to control it. Increasing amounts of data/knowledge Times are Changing

7. ICB Goals Ultimate Goal: individualized, state feedback based clinical trials

8. Metabolism of dNTPs + Analogs Metabolism of DNA + Drug-DNA Damage Driven or S-phase Driven dNTP demand is either Focus on cancers caused by DNA repair system failures DNA repair Salvage De novo Focus on nucleoside analogs CASE ICBP Problem Statement For Example:

9. UDP CDP GDP ADP dTTP dCTP dGTP dATP DNA dUMP ATP De Novo dNTP Synthesis

10. Data from Barry Cooperman’s group Enzyme Activity Profiles

11. Radivoyevitch T, Kashlan OB, Cooperman BS: Rational Polynomial Representation of Ribonucleotide Reductase Activity. BMC Biochemistry 2005, 6:8. Rational Polynomial Reaction Surface

12. Metabolism of dNTPs + Analogs Metabolism of DNA + Drug-DNA Damage Driven or S-phase Driven dNTP demand is either Focus on cancers caused by DNA repair system failures DNA repair Salvage De novo Focus on nucleoside analogs Case ICBP Problem Statement

13. ICB Model-Based Approaches to Therapeutic Gain Direct Approach Direct Approach –IUdR metabolism applied to MMR - cancers Cell death surrogate Anti-cancer input agents Treatment failure risk-state-transfer Approach Treatment failure risk-state-transfer Approach –TEL-AML1 patients as guides for BCR-ABL patients Cause of cancer Determinant of treatment failure Anti-cancer input agents Model contents

14. Risk State Transfer T: TEL-AML1 with HR T: TEL-AML1 with HR t : TEL-AML1 with CCR t : TEL-AML1 with CCR t : other outcome t : other outcome B: BCR-ABL with CCR B: BCR-ABL with CCR b: BCR-ABL with HR b: BCR-ABL with HR b: censored, missing, or other outcome b: censored, missing, or other outcome

15. Model Sharing & Use Systems Biology Markup Language (SBML) Systems Biology Markup Language (SBML) –A standard for representing biochemical systems R –Free statistics-oriented computational environment Bioconductor Bioconductor –R packages primarily for DNA microarray data analyses SBMLR SBMLR –An SBML-R interface and model analysis tool

16. library(SBMLR);library(odesolve) curto=readSBML(file.path(.path.package("SBMLR"), "models/curto.xml")) out1=simulate(curto,seq(-20,0,1)) curto$species$PRPP$ic=50 out2=simulate(curto,0:70) outs=data.frame(rbind(out1,out2));attach(outs) par(mfrow=c(2,1),cex.lab=1.5) plot(time,IMP,type="l",xlab="minutes",ylab="IMP (uM)") plot(time,HX,type="l",xlab="minutes",ylab="HX (uM)") SBMLR

17. Summary The Present The Future

18. Acknowledgements Comprehensive Cancer Center of Case Western Reserve University and University Hospitals of Cleveland (P30 CA43703) Comprehensive Cancer Center of Case Western Reserve University and University Hospitals of Cleveland (P30 CA43703) American Cancer Society (IRG-91-022-09) American Cancer Society (IRG-91-022-09) Case Integrative Cancer Biology Program (P20 CA112963-01) Case Integrative Cancer Biology Program (P20 CA112963-01) NIH Career Development Award (1K25 CA104791-01A1) NIH Career Development Award (1K25 CA104791-01A1) Thank you Thank you