Document Detail


Mathematical and statistical modeling in cancer systems biology.
MedLine Citation:
PMID:  22754537     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
Cancer is a major health problem with high mortality rates. In the post-genome era, investigators have access to massive amounts of rapidly accumulating high-throughput data in publicly available databases, some of which are exclusively devoted to housing Cancer data. However, data interpretation efforts have not kept pace with data collection, and gained knowledge is not necessarily translating into better diagnoses and treatments. A fundamental problem is to integrate and interpret data to further our understanding in Cancer Systems Biology. Viewing cancer as a network provides insights into the complex mechanisms underlying the disease. Mathematical and statistical models provide an avenue for cancer network modeling. In this article, we review two widely used modeling paradigms: deterministic metabolic models and statistical graphical models. The strength of these approaches lies in their flexibility and predictive power. Once a model has been validated, it can be used to make predictions and generate hypotheses. We describe a number of diverse applications to Cancer Biology, including, the system-wide effects of drug-treatments, disease prognosis, tumor classification, forecasting treatment outcomes, and survival predictions.
Authors:
Rachael Hageman Blair; David L Trichler; Daniel P Gaille
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Publication Detail:
Type:  Journal Article     Date:  2012-06-28
Journal Detail:
Title:  Frontiers in physiology     Volume:  3     ISSN:  1664-042X     ISO Abbreviation:  Front Physiol     Publication Date:  2012  
Date Detail:
Created Date:  2012-07-03     Completed Date:  2012-10-02     Revised Date:  2013-08-13    
Medline Journal Info:
Nlm Unique ID:  101549006     Medline TA:  Front Physiol     Country:  Switzerland    
Other Details:
Languages:  eng     Pagination:  227     Citation Subset:  -    
Affiliation:
Department of Biostatistics, State University of New York at Buffalo Buffalo, NY, USA.
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