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Application of Systems Biology in Neurotoxicological Studies During Development

Systems Toxicology

Genomic Technology

  1. Cheng Wang,
  2. Lei Guo,
  3. Tucker A. Patterson,
  4. William Slikker

Published Online: 15 SEP 2011

DOI: 10.1002/9780470744307.gat208

General, Applied and Systems Toxicology

General, Applied and Systems Toxicology

How to Cite

Wang, C., Guo, L., Patterson, T. A. and Slikker, W. 2011. Application of Systems Biology in Neurotoxicological Studies During Development. General, Applied and Systems Toxicology. .

Author Information

  1. National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA

Publication History

  1. Published Online: 15 SEP 2011


Systems biology has been defined as the iterative and integrative study of biological systems as they respond to perturbations. This chapter highlights the application of the systems biology approach to enhance the understanding of complex biological processes such as neurodegeneration in the developing brain. Although not yet fully delineated, the working model for anesthetic (e.g., ketamine)-induced neurodegeneration during development involves the modulation of normally occurring brain-sculpting mechanisms that control CNS development. Exposure of the developing mammal to anesthetics such as ketamine perturbs the endogenous N-methyl-d-aspartate (NMDA) receptor system and results in enhanced neuronal cell death. The working model is that prolonged ketamine exposure produces up-regulation of NMDA receptors and subsequent over-stimulation of the glutamatergic system by endogenous glutamate, triggering enhanced apoptosis of developing neurons. When the nervous system was perturbed with ketamine-induced anesthesia and gene expression changes were monitored, NMDA receptor genes were significantly up-regulated and this finding was confirmed by in situ hybridization studies. Systems biology, as applied to toxicology, provides a framework in which information can be arranged in the form of a biological model and various global datasets can be collected and integrated to determine whether they support the model. Discrepancies can be identified and hypotheses-driven studies conducted in order to address them. Thus, data generated via iteration of this process can be used to reformulate the model in light of the new data.


  • systems biology;
  • toxicology;
  • development;
  • genomics;
  • proteomics;
  • metabolomics