computational systems toxicology: recapitulating the
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Thomas B. Knudsen, PhDDevelopmental Systems Biologist, US EPA
National Center for Computational ToxicologyCSS - Virtual Tissue Models Project
[email protected] 0000-0002-5036-596x
Computational Systems Toxicology:
Recapitulating the logistical dynamics of cellular response networks
in virtual tissue models
“Advancing Computational and Systems Toxicology for the effective design of safer chemical and pharmaceutical products”EUROTOX 2017 - Bratislava
DISCLAIMER: The views expressed are those of the presenter and do not necessarily reflect Agency policy.
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In a nutshell …
• Advances in biomedical, engineering, and computational sciences enable HTS profiling of the chemical landscape (ToxCast/Tox21).
• HTS data streams can support integrated approaches to testing and assessment but must be tied in some way to biological understanding (MOAs, AOPs).
• Considerable mechanistic knowledge exists about cellular networks that pattern tissue development (cell signaling).
• Information must be collected, organized, and assimilated into in silico models that link HTS data (in vitro) to apical outcome (in vivo) and back (predictive toxicology).
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Virtual Embryo: an array of systems models to forward- and reverse-engineer
developmental toxicity for mechanistic understanding and predictive toxicology.
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• Modeling biological systems is a major task of systems biology, as most cellular phenomena are governed by interconnected dynamical networks.- cell growth, proliferation, adhesion, differentiation, polarization, motility, apoptosis, …- ECM synthesis, reaction-diffusion gradients, clocks, mechanical boundaries, fluid flow, …
• ABMs recapitulate cellular networks show how complex processes are regulated and how their disruption contributes to disease at a higher level of biological organization.- reconstruct development cell-by-cell, interaction-by-interaction- pathogenesis following synthetic knockdown (cybermorphs)- introduce ToxCast lesions into a computer simulation
- return quantitative predictions of where, when and how the defect arises.
Cellular Agent-Based Models (ABMs)
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1. Reverse-engineering the system: top-down scaling
• Suppose we know an apical outcome (eg, cleft palate), how far can an ABM take us to inferring a key event?
Hutson et al. (2017) Chem Res Toxicol
SEM
s o
f h
um
an
pa
late
by
K S
ulik
, UN
C
Palatal fusion in silicoPalatal fusion in vivo
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Hacking the control network ‘Cybermorphs’
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Cybermorph ToxCast lesion: Captan-induced cleft palate in rabbits
Ass
ay r
esp
on
se
EGF
TGFb
µM concentration
fusion no fusion
OUTPUT: tipping point mapped toHTS concentration response
(4 µM)
Captan in ToxRefDBNOAEL = 10 mg/kg/dayLOAEL = 30 mg/kg/day
OUTPUT: tipping point predicted bycomputational dynamics
(hysteresis switch)
HTTK pregnancy model predicts 2.39 mg/kg/day Captan would achieve a
steady state concentration of 4 µM in the fetal plasma
INPUT: Captan in ToxCast
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2. Forward-engineering the system: bottom-up scaling
• Suppose we know a molecular effect (eg, ToxCast lesion), how far can an ABM take us to hypothesizing an apical outcome?
Saili et al. (2017) manuscript in preparation
BBB Phylogeny BBB Ontogeny - >90 genes, >5 cell types
Mancozeb
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F Ginhoux, Aymeric Silvan – A*STAR, Singapore
Computational dynamics of brain angiogenesis
Tata et al. (2015) Mechanism Devel
VEGF-A gradient: NPCs in subventricular zone
normal mouse, E13.5 microglia-depleted
We are building and testing computer models formulated around novel hypotheses such as ‘chemical
disruption of microglia perturbs brain angiogenesis’.
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In silico cascading dose scenario
CSF1RVEGFR3VEGFR2
Mancozeb in ToxCast
INPUT 0.03 µMOUTPUT: predicted dNEL
INPUT 0.3 µM: AC50 CSF1ROUTPUT: fewer microglia drawn to EC-tip cells
INPUT 2.0 µM: AC80 CSF1R + AC50 VEGFR3OUTPUT: overgrowth of EC-stalk cells
INPUT 6.0 µM: AC95 CSF1R + AC85 VEGFR3 + AC50 VEGFR2OUTPUT: loss of directional sprouting
endothelial tip cellendothelial stalk cellmicroglial cell
Zirlinden et al. (2017) manuscript in preparation10
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SYSTOX
HTS
HTK
SAR
MPS
AOP
ABM
Computational synthesis and integration
computationalchemistry
bioactivity profiles
kinetics &dosimetry
microphysiological systems
pathways& networks
computationaldynamics
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Todd Zurlinden – NCCTKate Saili – NCCTRichard Judson - NCCTNancy Baker – Leidos / NCCTRichard Spencer – ARA / EMVLShane Hutson – Vanderbilt U
Special Thanks
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