Document Detail


In silico approaches to mechanistic and predictive toxicology: an introduction to bioinformatics for toxicologists.
MedLine Citation:
PMID:  11951993     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Bioinformatics, or in silico biology, is a rapidly growing field that encompasses the theory and application of computational approaches to model, predict, and explain biological function at the molecular level. This information rich field requires new skills and new understanding of genome-scale studies in order to take advantage of the rapidly increasing amount of sequence, expression, and structure information in public and private databases. Toxicologists are poised to take advantage of the large public databases in an effort to decipher the molecular basis of toxicity. With the advent of high-throughput sequencing and computational methodologies, expressed sequences can be rapidly detected and quantitated in target tissues by database searching. Novel genes can also be isolated in silico, while their function can be predicted and characterized by virtue of sequence homology to other known proteins. Genomic DNA sequence data can be exploited to predict target genes and their modes of regulation, as well as identify susceptible genotypes based on single nucleotide polymorphism data. In addition, highly parallel gene expression profiling technologies will allow toxicologists to mine large databases of gene expression data to discover molecular biomarkers and other diagnostic and prognostic genes or expression profiles. This review serves to introduce to toxicologists the concepts of in silico biology most relevant to mechanistic and predictive toxicology, while highlighting the applicability of in silico methods using select examples.
Authors:
Mark R Fielden; Jason B Matthews; Kirsten C Fertuck; Robert G Halgren; Tim R Zacharewski
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Publication Detail:
Type:  Journal Article; Review    
Journal Detail:
Title:  Critical reviews in toxicology     Volume:  32     ISSN:  1040-8444     ISO Abbreviation:  Crit. Rev. Toxicol.     Publication Date:  2002 Mar 
Date Detail:
Created Date:  2002-04-15     Completed Date:  2002-10-01     Revised Date:  2004-11-17    
Medline Journal Info:
Nlm Unique ID:  8914275     Medline TA:  Crit Rev Toxicol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  67-112     Citation Subset:  IM    
Affiliation:
Department of Biochemistry and Molecular Biology, National Food Safety and Toxicology Center, Michigan State University, East Lansing 48824, USA.
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MeSH Terms
Descriptor/Qualifier:
Cluster Analysis
Computational Biology / methods*,  trends
Databases, Factual
Expressed Sequence Tags
Genotype
Humans
Models, Molecular
Oligonucleotide Array Sequence Analysis / methods*
Toxicology*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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