Tomas Radivoyevitch, PhDAssistant ProfessorEpidemiology and BiostatisticsCase Western Reserve University

Email: radivot@hal.cwru.edu Website: http://epbi-radivot.cwru.edu/

Integrative Cancer Integrative Cancer BiologyBiology

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 EngineeringEmphasis 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 ChangingTimes are Changing

EXPERIMENTALBIOLOGY

COMPUTERMODELING

CONTROLTHEORY

models

control lawsdata

hypotheses

proposed clinical trial

validated process model development

control system design methods development

ICB GoalsICB Goals

Ultimate Goal: individualized, state feedback based clinical trialsUltimate Goal: individualized, state feedback based clinical trials

Metabolism of Metabolism of dNTPs + AnalogsdNTPs + Analogs

Metabolism of Metabolism of DNA + Drug-DNADNA + Drug-DNA

Damage DrivenDamage Drivenor or

S-phase DrivenS-phase Driven

dNTP demand dNTP demand is eitheris either

Focus on cancers Focus on cancers caused by DNA repair caused by DNA repair system failuressystem failures

DNA repairDNA repair

SalvageSalvage

De novoDe novo

Focus on Focus on nucleoside nucleoside analogsanalogs

CASE ICBP CASE ICBP Problem StatementProblem Statement

For Example:For Example:

UDP

CDP

GDP

ADP

dTTP

dCTP

dGTP

dATP

DNA

dUMP

ATP

De Novo De Novo dNTP dNTP SynthesisSynthesis

0 2 4 6 8 10

0.0

0.1

0.2

0.3

0.4

ADP.reductase

dATP (uM)

kcat

(1/

s)

0 200 400 600

0.0

0.1

0.2

0.3

0.4

ADP.reductase

ATP (uM)

kcat

(1/

s)

0 2 4 6 8 12

0.0

0.4

0.8

ADP.reductase

dGTP (uM)kc

at (

1/s)

0 5 10 15 20

0.0

0.1

0.2

0.3

0.4

GDP.reductase

dATP (uM)

kcat

(1/

s)

0 4000 8000

0.0

0.1

0.2

0.3

0.4

GDP.reductase

ATP (uM)

kcat

(1/

s)

0 1 2 3 4 5 6

0.0

0.4

0.8

GDP.reductase

dTTP (uM)

kcat

(1/

s)

0 1000 25000.

00.

40.

8

GDP.reductase

dGTP (uM)

kcat

(1/

s)

0 1 2 3 4 5

0.0

0.1

0.2

0.3

0.4

CDP.reductase

dATP (uM)

kcat

(1/

s)

0 1000 2500

0.0

0.1

0.2

0.3

0.4

CDP.reductase

ATP (uM)

kcat

(1/

s)

0 1 2 3 4 5

0.0

0.1

0.2

0.3

0.4

UDP.reductase

dATP (uM)

kcat

(1/

s)

0 1000 2000

0.0

0.1

0.2

0.3

0.4

UDP.reductase

ATP (uM)

kcat

(1/

s)

Data from Barry Data from Barry Cooperman’s group Cooperman’s group

Enzyme Activity Enzyme Activity ProfilesProfiles

Radivoyevitch T, Kashlan OB, Cooperman BS: Radivoyevitch T, Kashlan OB, Cooperman BS: Rational Polynomial Rational Polynomial Representation of Ribonucleotide Reductase Activity.Representation of Ribonucleotide Reductase Activity. BMC Biochemistry BMC Biochemistry 2005, 2005, 6:6:8.8.

Rational Polynomial Rational Polynomial Reaction SurfaceReaction Surface

Metabolism of Metabolism of dNTPs + AnalogsdNTPs + Analogs

Metabolism of Metabolism of DNA + Drug-DNADNA + Drug-DNA

Damage DrivenDamage Drivenor or

S-phase DrivenS-phase Driven

dNTP demand dNTP demand is eitheris either

Focus on cancers Focus on cancers caused by DNA repair caused by DNA repair system failuressystem failures

DNA repairDNA repair

SalvageSalvage

De novoDe novo

Focus on Focus on nucleoside nucleoside analogsanalogs

Case ICBP Case ICBP Problem StatementProblem Statement

ICB Model-Based ICB Model-Based Approaches to Therapeutic Approaches to Therapeutic

GainGain

Direct ApproachDirect Approach– IUdR metabolism applied to MMRIUdR metabolism applied to MMR--

cancerscancers Cell death surrogateAnti-cancer input agents

Treatment failure Treatment failure risk-state-transfer Approachrisk-state-transfer Approach– TEL-AML1 patients as guides for BCR-ABL patientsTEL-AML1 patients as guides for BCR-ABL patients

Cause of cancer

Determinant of treatment failure

Anti-cancer input agents

Model contents

Risk State TransferRisk State Transfer

TT: TEL-AML1 with HR : TEL-AML1 with HR tt : TEL-AML1 with CCR : TEL-AML1 with CCR tt : other outcome : other outcome

BB: BCR-ABL with CCR: BCR-ABL with CCR bb: BCR-ABL with HR: BCR-ABL with HR bb: censored, missing, : censored, missing,

or other outcome or other outcome

B

b

b

b

b

b

b

b

bb

bb

b

b

b

b

tt t

t

t

t

t t

ttt

tt

t

t

t

t

t

t

t

t

t

tt

t

t

t

t

t

t

tt

t

t

t

t

t

t

t

t

t

tt

t

t

tt

t

t

t

t

t

tt

tt

t

T

T

T

t

t

t

t

t

tt

t

tt

tt

t

tt

t

t

t

t

0 2 4 6 8 10 12 140

20

04

00

60

08

00

10

00

12

00

DNTS Flux (uM/hr)

DN

PS

Flu

x (u

M/h

r)

Model Sharing & Use Model Sharing & Use

Systems Biology Markup Language (SBML)Systems Biology Markup Language (SBML)– A standard for representing biochemical systemsA standard for representing biochemical systems

RR– Free statistics-oriented computational environmentFree statistics-oriented computational environment

BioconductorBioconductor– R packages primarily for DNA microarray data R packages primarily for DNA microarray data

analysesanalyses SBMLRSBMLR

– An SBML-R interface and model analysis toolAn SBML-R interface and model analysis tool

SBML model definition file

SBMLR model definition file

readSBML

saveSBMLreadSBML

saveSBML

model object of class SBML in R

getModelInfo

getModelInfo

Incidencematrix

(Model sharing)

(Model using)

SimulationsaveSBMLR

readSBMLR

saveSBMLR

readSBMLR

(Model editing)

“==“,Summary

“==“,Summary

simulate

simulate

MCA

(Model testing and summary methods)

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=50out2=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)")

SBMLRSBMLR

-20 0 20 40 60

10

01

05

11

01

15

minutes

IMP

(uM

)

-20 0 20 40 60

78

91

01

1

minutes

HX

(uM

)

EXPERIMENTALBIOLOGY

COMPUTERMODELING

CONTROLTHEORY

models

control lawsdata

hypotheses

proposed clinical trial

validated process model development

control system design methods development

SummarySummary

The Present The Future

AcknowledgementsAcknowledgements

Comprehensive Cancer Center of Case Comprehensive Cancer Center of Case Western Reserve University and University Western Reserve University and University Hospitals of Cleveland (P30 CA43703) Hospitals of Cleveland (P30 CA43703)

American Cancer Society (IRG-91-022-09)American Cancer Society (IRG-91-022-09) Case Integrative Cancer Biology Program Case Integrative Cancer Biology Program

(P20 CA112963-01)(P20 CA112963-01) NIH Career Development Award (1K25 NIH Career Development Award (1K25

CA104791-01A1)CA104791-01A1) Thank you Thank you