Transcript Dose-Response Modeling: Past, Present, and Future II Rory B. Conolly and Rusty Thomas

Dose-Response Modeling: Past, Present, and
Future (Part II)
Rory B. Conolly, Sc.D.
Rusty Thomas, Ph.D.
Center for Computational Systems Biology
& Human Health Assessment
CIIT Centers for Health Research
(919) 558-1330 - voice
[email protected] - e-mail
SOT Risk Assessment Specialty Section, Wednesday, January 12, 2005
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Outline
• Why do we care about dose response?
• Historical perspective
– Brief, incomplete!
• Formaldehyde
• Future directions
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The future
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Outline
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•
•
•
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Long-range goal
Systems in biological organization
Molecular pathways
Data
Example
– Computational modeling
– Modularity
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Long-range goal
• A molecular-level understanding of dose- and timeresponse behaviors in laboratory animals and people.
– Environmental risk assessment
– Drug development
– Public health
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Levels of biological organization
Populations
Descriptive
Organisms
(systems)
Tissues
(systems) Mechanistic
Cells
(systems)
Organelles
Molecules
(systems)
(systems)
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Levels of biological organization
Populations
Organisms
Tissues
Cells
Organelles
Today
Molecules
(systems)
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Molecular pathways
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Segment polarity genes in Drosophila
Albert & Othmer, J. Theor Biol. 223, 1 – 18, 2003
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ATM curated
Pathway from
Pathway Assist®
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Approach
• Initial pathway identification
– Static map
• Existing data
• New data
• Computational modeling
– Dynamic behavior
– Iterate with data collection
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Initial pathway identification
• Use commercial software that can integrate data from
a variety of sources (Pathway Assist)
– Scan Pub Med abstracts to identify “facts”
– Create pathway maps
– Incorporate other, unpublished data
• Quality control
– Curate pathways
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Computational modeling
• To study the dynamic behavior of the pathway
• Analyze data
– Are model predictions consistent with existing data?
• Make predictions
– Suggest new experiments
– Ability to predict data before it is collected is a good
test of the model
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DNA damage and cell cycle checkpoints
(a) G1/S Checkpoint
(b) G2/M Checkpoint
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p21 time-course data and simulation
Experimental data
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Mutation Fraction Rate
Mutations dose-response and model
prediction
model calculated values
IR
(Redpath et al, 2001)
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Data
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Tissue dosimetry is the “front end” to a
molecular pathway model
(Fat)
Air-blood
interface
Liver
Venous
blood
Rest of Body
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Implementing a Systems Biology Approach
Assemble the “Parts List”
Identify How the Pieces Fit Together
Describe the System
Quantitatively
a

V   (a 2  x 2 )dx
a
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Assembling the “Parts List”
Anatomy of a Screen: Constructing The Assay
GFP
LTR
Response
Elements
LTR
Retroviruses
Cellular Assay
(Promoter/RE Reporter)
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Assembling the “Parts List”
Anatomy of a Screen: Constructing The Assay
RNAi
Loss of function
Two “Functional” Approaches
Full-length Genes
Gain of function
Cellular Assay
(Promoter/RE Reporter)
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Background on siRNA
Long dsRNAs
Dicer-RDE1
complex
19mer
TT
TT
Functional
KO
RNA Induced Silencing
Complex (RISC) formation
RNA
Unwinding
Association
With Target mRNA
Target mRNA
Cleavage
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Assembling the “Parts List”
Anatomy of a Screen
Arrayed, full-length genes
set in 384-well plates
Transfect genes into
reporter cells
Identify hits
P
Gene1 Gene2
Gene3
Gene4 Gene5
Gene6
PP
P P
P
P
TT
PP
TT
Arrayed siRNAs in 384-well
plates
Construct putative cellular
signaling pathway
Transfect siRNAs
into reporter cells
Identify hits
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Identify How the Pieces Fit Together
Anatomy of a Screen: Organizing the Pathway
P
P
TT
TT
P
P
P
P
cDNA
Expression
siRNA
Knockdown
siRNA
Knockdown
cDNA
Expression
TT
P
P
P
P
TT
P
P
P P
Reduced or No
Reporter Activity
Reporter
Activity
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Preliminary Results
NFkB cDNA and siRNA Screen
Screen Type: cDNA
Genes Screened: ~2,400
Screen Type: siRNA
Genes Screened: ~550
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Preliminary Results
Combined Structural Network
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Example
• Skin irritation
• MAPK, IL-1a, and NF-kB computational “modules”
• High throughput overexpression data to characterize
IL-1a – MAPK interaction with respect to NF-kB
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Skin Irritation
Chemical
Dead cells
Tissue damage
Tissue
damage
Nerve
Endings
A cascade of inflammatory
responses (cytokines)
Epidermis
(keratinocytes)
Dermis
(fibroblasts)
Blood vessels
•
Study on the dose response of the skin cells to inflammatory cytokines contributes
to quantitative assessment of skin irritation
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Modular Composition of IL-1 Signaling
IL-1
IL-1R
Secondary messenger
Constitutive NF-kB
downstream
NF-kB
module
MAPK
Extracellular
Intracellular
IL-1 specific
top module
Others
IL-6, etc.
Transcriptional factors
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Top IL-1 Signaling Module
P
MyD88
TRAF6
P
IRAK
TAB1
TAK1
TAB2
P
P
TRAF6
Self-limiting
mechanism
IkK
IkK P
NF-kB module
Degraded
IRAK
Cytoplasm
IRAK gene
Nucleus
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Top Module Simulation
• IL-1 receptor number and ligand binding
parameters from human keratinocytes
• Other parameters constrained by reasonable
ranges of similar reactions/molecules, and
tuned to fit data
TAK1*
IRAKp
Increasing IRAKp
degradation
Time (hrs)
Time (hrs)
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Constitutive NF-kB Signaling Module
Input signal
IkK P
IkK
P
IkB
P
NF kB
IkB
NF kB
NF kB
IkB
Degraded
IkB
NF kB
Cytoplasm
Negative
feedback
IkB gene
NF kB
IkB
NF kB
IL-6 gene
Nucleus
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NF-kB Module Simulation
• Parameters from existing NF-kB model
(Hoffmann et al., 2002) and refined to fit
experimental data in literature
+
Add constant
input signal
IkB
IL-6
_
NF-kB
Time (hrs)
Smoothened
oscillations
Longer
delay
Time (hrs)
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The IB–NF-B Signaling Module: Temporal Control and Selective Gene Activation
Alexander Hoffmann, Andre Levchenko, Martin L. Scott, David Baltimore
Science 298:1241 – 1245, 2002
6 hr
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MAPK intracellular signaling cascades
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http://www.weizmann.ac.il/Biology/open_day/book/rony_seger.pdf
Growth factor
PKC
MAPKKK
AA
MAPKK
PLA2
MAPK
MKP
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MAPK time-course and bifurcation after a
short pulse of PDGF
Growth factor
PKC
MAPKKK
AA
MAPKK
PLA2
MAPK
MKP
Input pulse
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IL-1 MAPK crosstalk and NFkB activation
IL-1
IL-1R
MyD88
P
IRAK
P
TAB2
IRAK gene
TAB1
TRAF6
TAK1
IRAK
IRAK
MAP3K1
P
Degraded
P
IκK
IκK
NFκB module
NFκB-dependent
transcription
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Fold Induction
Gain-of-function screen
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30
25
20
15
10
5
0
0.001
0 ng MAP3K1
10 ng MAP3K1
30 ng MAP3K1
0.01
0.1
1
10
[IL-1a] ng/ml
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Model prediction
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Future directions
• Computational modeling and data collection at higher
levels of biological organization
– Cells
• Intercellular communication
– Tissues
– Organisms
• NIH initiatives
• Environmental health risk, drugs ==> in vivo
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Summary
• Biological organization and systems
• Molecular pathways
– identification
– Computational modeling
• Data
– Gain-of-function
– Loss-of-function
• Skin irritation example
– 3 modules
– Crosstalk
– Targeted data collection
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Acknowledgements
• Colleagues who worked on the clonal growth risk
assessment
– Fred Miller, Julian Preston, Paul Schlosser, Julie
Kimbell, Betsy Gross, Suresh Moolgavkar, Georg
Luebeck, Derek Janszen, Mercedes Casanova, Henry
Heck, John Overton, Steve Seilkop
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Acknowledgements
• CIIT Centers for Health Research
–
–
–
–
Rusty Thomas
Maggie Zhao
Qiang Zhang
Mel Andersen
• Purdue
– Yanan Zheng
• Wright State University
– Jim McDougal
• Funding
– DOE
– ACC
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End
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