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
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
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
Software 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/
Syllabus






Introduction to R
Epidemiological Cancer Datasets
Gene Expression Analyses
Biochemical Systems
Pharmacokinetic Models
Tumor Growth and Invasion
Times are Changing
Model components: (Deterministic = signal) + (Stochastic = noise)
Statistics
Engineering
Emphasis is on the stochastic
component of the model.
Emphasis is on the deterministic
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?
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.
Do we have enough data?
Increasing amounts of data/knowledge
ICB Goals
validated process
model development
EXPERIMENTAL
BIOLOGY
hypotheses
data
control system design
methods development
COMPUTER models
CONTROL
MODELING control laws THEORY
proposed clinical trial
Ultimate Goal: individualized, state feedback based clinical trials
CASE ICBP
Problem Statement
dNTP demand
is either
Damage Driven
or
S-phase Driven
Focus on
nucleoside
analogs
Problem Statement
Salvage
Metabolism of
dNTPs + Analogs
De novo
Damage Driven
or
S-phase Driven
Metabolism of
DNA + Drug-DNA
Metabolism of
dNTPs + Analogs
Focus on cancers
Focus on cancers
salvage
DNA
repair
caused
by DNA repair
caused by DNA repair
system failures
system failures
Metabolism of
DNA + DrugDrug-DNA
DNA repair
MMRBest time to hit with IR
0.5
0.8
y2 - y
0.4
0.6
0.3
y
0.2
0.4
0.1
0.0
MMR+
0.2
For Example:
0.6
1.0
0.7
IU-DNA levels
0.0
De novo
Nucleoside
demand is either
0
2
4
6
x
Time
8
10
0
2
4
6
x
8
10
De Novo dNTP Synthesis
ADP
dATP
GDP
dGTP
DNA
ATP
CDP
dCTP
UDP
dTTP
dUMP
Enzyme Activity Profiles
4
6
8 10
0.8
R11
inactive
dATP
ATP
ATP
R12
R14
R16
kcat=k2
kcat=k4
kcat=k6
0.0
0
200
400
600
0 2 4 6 8
R14
12
dATP (uM)
ATP (uM)
dGTP (uM)
GDP.reductase
GDP.reductase
GDP.reductase
inactive
GDP.reductase
5
10 15 20
0
4000
8000
0.8
0.4
0.0
0.4
kcat (1/s)
0.0
0.0
0
kcat (1/s)
0.8
0.4
0.3
0.2
kcat (1/s)
0.2
0.1
0.1
0.3
0.4
dTTP
dGTP
dATP
ATP
0.4
kcat (1/s)
0.3
0.2
kcat (1/s)
0.0
2
0.0
0 1 2 3 4 5 6
0
1000
2500
CDP.reductase
CDP.reductase
UDP.reductase
UDP.reductase
2
3
4
dATP (uM)
5
0
1000
2500
ATP (uM)
0.3
0.2
kcat (1/s)
0.0
0.1
0.3
0.2
kcat (1/s)
0.0
0.0
1
0.1
0.3
0.2
kcat (1/s)
0.3
0.2
0.1
0.0
0
0.4
dGTP (uM)
0.4
dTTP (uM)
0.4
ATP (uM)
0.4
dATP (uM)
0.1
kcat (1/s)
0.1
0.3
0.2
kcat (1/s)
0.1
0.0
0
kcat (1/s)
ADP.reductase
0.4
ADP.reductase
0.4
ADP.reductase
0
1
2
3
4
dATP (uM)
5
0
1000
2000
ATP (uM)
Data from Barry
Cooperman’s group
Rational Polynomial
Reaction Surface


NDP / K mNDP
v  kcat E0 

 ADP / K mADP  GDP / K mGDP  CDP / K mCDP  UDP / K mUDP  1 
ADP reduction


2
2
2
2 

  k2  k4  dATP / K idATP    ATP / K iATP 
 dGTP / K sdGTP 
 ATP / K aATP  


kcat  

k
6
2
2
2
2 
2
2
2
 1   dGTP / K

1   dATP / K idATP    ATP / K iATP 
1   ATP / K aATP  
sdGTP    dTTP / K sdTTP    dATP / K sdATP    ATP / K sATP   



GDP reduction


2
2
2
2 

  k2  k4  dATP / K idATP    ATP / K iATP 
 dTTP / K sdGTP 
 ATP / K aATP  

kcat  
 k6
2
2
2
2 
2
2
2
 1   dGTP / K

1   dATP / K idATP    ATP / K iATP 
1   ATP / K aATP  
sdGTP    dTTP / K sdTTP    dATP / K sdATP    ATP / K sATP   



CDP reduction
2
2
2


k2 dA  dATP / K sdATP   k2 A  ATP / K sATP   k2e
 ATP / K aATP  
k
1

kcat  
 6 
2
2
2
2
2
2
 1   dGTP / K

k2 A 1   ATP / K aATP 2 
sdGTP    dTTP / K sdTTP    dATP / K sdATP    ATP / K sATP   1   dATP / K idATP    ATP / K iATP 


UDP reduction
2
2
2


k2 dA  dATP / K sdATP   k2 A  ATP / K sATP 
 ATP / K aATP  
k
1

kcat  
 6 
2
2
2
2
2
2
 1   dGTP / K

k2 A 1   ATP / K aATP 2 
sdGTP    dTTP / K sdTTP    dATP / K sdATP    ATP / K sATP   1   dATP / K idATP    ATP / K iATP 


Radivoyevitch T, Kashlan OB, Cooperman BS: Rational Polynomial Representation of
Ribonucleotide Reductase Activity. BMC Biochemistry 2005, 6:8.
Case ICBP
Problem Statement
dNTP demand
is either
Damage Driven
or
S-phase Driven
Focus on
nucleoside
analogs
Salvage
De novo
Metabolism of
dNTPs + Analogs
Focus on cancers
caused by DNA repair
system failures
Metabolism of
DNA + Drug-DNA
DNA repair
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
Anti-cancer input agents
Determinant of
treatment failure


B: BCR-ABL with CCR
b: BCR-ABL with HR
b: censored, missing, or
other outcome
1000
800
t
t
b
b btt
t
t tt
t
tt
t t
600
b
b
b
t
400

B
t
b t Tt t
bttb t t tt
t
tt
b t t
t tt
tbt t t tt t t
t tt t tt t
tt
tt
bt t
t
b tbt tT t
t
200

b
t
t
t
t
t
T
t
t
t
t
t
t
t
t
t
t
0

T: TEL-AML1 with HR
t : TEL-AML1 with CCR
t : other outcome
DNPS Flux (uM/hr)

1200
Risk State Transfer
0
2
4
6
8
DNTS Flux (uM/hr)
10
12
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
SBMLR
SBMLR model
definition file
sa
ve
(Model sharing)
SB
M
L
ad
SB
ML
(Model editing)
sa
v
ad
re
re
SB
ML
eS
R
BM
LR
SBML model
definition file
115
110
105
IMP (uM)
Simulation
100
od
tM
ge
simulate
y
ar
MCA
m
um
Incidence
matrix
,S
(Model using)
(Model testing and
summary methods)
=“
el
“=
In
fo
model object of
class SBML in R
-20
0
20
40
60
40
60
10
9
8
HX (uM)
11
minutes
7
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)")
-20
0
20
minutes
Summary
validated process
model development
EXPERIMENTAL
BIOLOGY
hypotheses
data
control system design
methods development
COMPUTER models
CONTROL
MODELING control laws THEORY
proposed clinical trial
The Present
The Future
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