Dose-Response Modeling: Past, Present, and Future (Part II)

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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
• [phone] - voice
• rconolly_at_ciit.org - 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

• 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
  time-response behaviors in laboratory animals and
  people.
• Environmental risk assessment
• Drug development
• Public health

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Levels of biological organization

• Populations
• Organisms
• Tissues
• Cells
• Organelles
• Molecules

Mechanistic
Descriptive
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Levels of biological organization

• Populations
• Organisms
• Tissues
• Cells
• Organelles
• 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• Incorporate other, unpublished data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
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p21 time-course data and simulation
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Mutations dose-response and model prediction
model calculated values
Mutation Fraction Rate
IR
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Data
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Tissue dosimetry is the front end to a
molecular pathway model
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Implementing a Systems Biology Approach
Assemble the Parts List
Identify How the Pieces Fit Together
Describe the System Quantitatively
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Assembling the Parts List
Anatomy of a Screen Constructing The Assay
LTR
LTR
GFP
Response Elements
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
Cellular Assay (Promoter/RE Reporter)
Gain of function
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Background on siRNA
Long dsRNAs
Dicer-RDE1 complex
19mer
TT
TT
Functional KO
RNA Induced Silencing Complex (RISC) formation
Target mRNA Cleavage
Association With Target mRNA
RNA Unwinding
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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
P
P
P
P
P
P
P
P
P
P
P
Construct putative cellular signaling pathway
Arrayed siRNAs in 384-well plates
Transfect siRNAs into reporter cells
Identify hits
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Identify How the Pieces Fit Together
Anatomy of a Screen Organizing the Pathway
siRNA Knockdown
cDNA Expression
P
P
P
P
P
P
P
P
P
P
P
P
cDNA Expression
siRNA Knockdown
P
P
P
P
P
P
Reduced or No Reporter Activity
Reporter Activity
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Preliminary Results
NFkB cDNA and siRNA Screen
Screen Type siRNA Genes Screened 550
Screen Type cDNA Genes Screened 2,400
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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
Epidermis
Tissue damage
(keratinocytes)
Tissue damage
Dermis
Nerve Endings
A cascade of inflammatory responses (cytokines)
(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
Extracellular
IL-1R
Intracellular
IL-1 specific top module
Secondary messenger
MAPK
Others
Constitutive downstream NF-kB module
NF-kB
IL-6, etc.
Transcriptional factors
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Top IL-1 Signaling Module
IL-1
IL-1R
TAB2
TAK1
TAB1
MyD88
TRAF6
NF-kB module
Degraded
Cytoplasm
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

Increasing IRAKp degradation
IRAKp
TAK1
Time (hrs)
Time (hrs)
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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

IkB
IL-6
_

NF-kB
Smoothened oscillations
Concentration (mM)
Concentration (mM)
Time (hrs)
Add constant input signal
Time (hrs)
Longer delay
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The IBNF-B Signaling Module Temporal Control
and Selective Gene Activation Alexander Hoffmann,
Andre Levchenko, Martin L. Scott, David
Baltimore Science 2981241 1245, 2002
6 hr
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MAPK intracellular signaling cascades
http//www.weizmann.ac.il/Biology/open_day/book/ro
ny_seger.pdf
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MAPK time-course and bifurcation after a short
pulse of PDGF
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IL-1 MAPK crosstalk and NFkB activation
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Gain-of-function screen
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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 gt 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