Mathematical and Statistical Modeling in Cancer Systems Biology
Open Access
- 1 January 2012
- journal article
- Published by Frontiers Media SA in Frontiers in Physiology
- Vol. 3, 227
- https://doi.org/10.3389/fphys.2012.00227
Abstract
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.This publication has 75 references indexed in Scilit:
- Predicting selective drug targets in cancer through metabolic networksMolecular Systems Biology, 2011
- Critical reasoning on causal inference in genome-wide linkage and association studiesTrends in Genetics, 2010
- Cancer Metabolism: Is Glutamine Sweeter than Glucose?Cancer Cell, 2010
- Data Integration for Dynamic and Sustainable Systems Biology Resources: Challenges and Lessons LearnedChemistry & Biodiversity, 2010
- Logic-Based Models for the Analysis of Cell Signaling NetworksBiochemistry, 2010
- Computational reconstruction of tissue‐specific metabolic models: application to human liver metabolismMolecular Systems Biology, 2010
- Applications of genome‐scale metabolic reconstructionsMolecular Systems Biology, 2009
- Understanding NF‐κB signaling via mathematical modelingMolecular Systems Biology, 2008
- [19] Gene Expression Omnibus: Microarray Data Storage, Submission, Retrieval, and AnalysisMethods in Enzymology, 2006
- Gene expression profiling predicts clinical outcome of breast cancerNature, 2002