Science Inventory

5.22 - Systems Toxicology and Virtual Tissue Models


Knudsen, T. AND G. Daston. 5.22 - Systems Toxicology and Virtual Tissue Models. Chapter 5, Comprehensive Toxicology, 3rd Edition. Elsevier Online, New York, NY, 5:351-362, (2018).


This is an invited update of a chapter in the COMPREHENSIVE TOXICOLOGY, 3rd Edition Edited by Charlene McQueen. The chapter will be included in the ‘Developmental Toxicology’ volume (Eds: Dana Dolinoy and Kamin Johnson).


Cells in developing tissues must integrate and respond to highly dynamic information in the form of metabolic intermediates, genetic signals, and molecular gradients. Understanding the cellular decision-making process, and how these decisions may be perturbed by genetic errors or environmental disruptions to invoke abnormal development, requires a fundamental understanding of how developing tissues are organized and controlled at a systems level. Computational (in silico) models have applications for mechanistic understanding and predictive modeling of developmental toxicity. Such models incorporate extensive knowledge of system structure, network state relations, kinetic parameters, and cellular computation. These models would utilize high-content data from genome-based studies, and high-throughput data from in vitro screens, to understand many of the genes, signaling pathways, and biochemical reactions that orchestrate a morphogenetic series of events in the embryo. Dynamic ‘virtual tissue models’ (VTMs) that utilize cross-scale information about developmental processes and quantitative data on prenatal developmental toxicities could someday be used to simulate how embryos might react to chemical exposure and better characterize chemical mode of action for complex exposure scenarios in silico. This chapter will review the field of virtual tissue models and computational systems toxicology with emphasis on prenatal developmental toxicity.

Record Details:

Product Published Date: 05/01/2018
Record Last Revised: 04/23/2019
OMB Category: Other
Record ID: 344815