Thomas B. Knudsen, PhDDevelopmental Systems Biologist, US EPA

National Center for Computational ToxicologyCSS - Virtual Tissue Models Project

knudsen.thomas@epa.govORCID 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.

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)

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

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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