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Commonality and Stochasticity in Systems Toxicology

Systems Toxicology


  1. Yoko Hirabayashi,
  2. Tohru Inoue

Published Online: 15 SEP 2011

DOI: 10.1002/9780470744307.gat227

General, Applied and Systems Toxicology

General, Applied and Systems Toxicology

How to Cite

Hirabayashi, Y. and Inoue, T. 2011. Commonality and Stochasticity in Systems Toxicology. General, Applied and Systems Toxicology. .

Author Information

  1. National Institute of Health Sciences, National Center for Biology and Safety Research, Tokyo, Japan

Publication History

  1. Published Online: 15 SEP 2011


“Systems toxicology” is “systems biology” applied to general toxicology, which is to elucidate a universal concept of biological interactions between living organisms and xenobiotics by global assays of transcriptomics, proteomics and other various applied omics studies, during various biological steps in in vivo responses, in developmental, pubertal and senescent stages, and at the ontological or phylogenical level, in addition to in vitro cellular responses. The aim of the chapter is to focus on systems toxicology to incorporate a new biological concept that distinguishes commonality and stochasticity from those xenobiotic responses when one incorporates computational toxicological data from the gene chip microarray into systems toxicology. The multiplicity of biological reactions can be better understood when common gene expression profiles and stochastic gene expression profiles would be unsupervisedly analyzed computationally. Previous toxicological data have been analysed frequently with their average endpoints focused on the commonality. However, probabilistic stochasticity may be analysed as specific stochastic clusters that elucidate other aspects of biological diversity in future “systems toxicology”.


  • ageing;
  • aryl hydrocarbon receptor-mediated toxicity;
  • benzene-induced haematotoxicity;
  • epigenetic stochasticity;
  • haematotoxicology;
  • predictable toxicology;
  • radiation-induced myeloid leukaemia;
  • stochastic gene expression;
  • toxicogenomics;
  • toxicoinformatics