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Integrative Cancer Biology EPBI 473

Integrative Cancer Biology EPBI 473 . 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. . Instructor. Tomas Radivoyevitch, PhD Assistant Professor

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Integrative Cancer Biology EPBI 473

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  1. Integrative Cancer BiologyEPBI 473 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.

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

  3. Course Information 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.shtml Softwarehttp://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/

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

  5. Times are Changing Model components: (Deterministic = signal) + (Stochastic = noise) Engineering Statistics 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? 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

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

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

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

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

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

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

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

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

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

  15. SBMLR 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)")

  16. Summary The Present The Future

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

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