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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
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.

instructor
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/

course information
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/

syllabus
Syllabus
  • Introduction to R
  • Epidemiological Cancer Datasets
  • Gene Expression Analyses
  • Biochemical Systems
  • Pharmacokinetic Models
  • Tumor Growth and Invasion
times are changing
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

icb goals
ICB Goals

Ultimate Goal: individualized, state feedback based clinical trials

case icbp problem statement
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:

de novo dntp synthesis
De Novo dNTP Synthesis

ADP

dATP

GDP

dGTP

DNA

ATP

CDP

dCTP

UDP

dTTP

dUMP

enzyme activity profiles
Enzyme Activity Profiles

Data from Barry Cooperman’s group

rational polynomial reaction surface
Rational Polynomial Reaction Surface

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

case icbp problem statement1
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

icb model based approaches to therapeutic gain
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

risk state transfer
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
model sharing use
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
sbmlr
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)")

summary
Summary

The Present

The Future

acknowledgements
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