Systems Biology Applied to Cancer Research

Complex diseases such as cancer have multiple origins and are therefore difficult to understand and cure. Highly parallel technologies such as DNA microarrays are now available These provide a data deluge which needs to be tninedfor relevant information and integrated to existing knowledge at different scales. Systems Biology is a recentfield which intends to overcome these challenges by combining different disciplines and provide an analytical framework Sonic of these challenges are discussed in this chapter.

[1]  W. Bamlet,et al.  Study design considerations in clinical outcome research of lung cancer using microarray analysis. , 2004, Lung cancer.

[2]  A. Dupuy,et al.  Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. , 2007, Journal of the National Cancer Institute.

[3]  Yudong D. He,et al.  A Gene-Expression Signature as a Predictor of Survival in Breast Cancer , 2002 .

[4]  Y. Lazebnik Can a biologist fix a radio? — or, what I learned while studying apoptosis , 2004, Biochemistry (Moscow).

[5]  Ziv Bar-Joseph,et al.  Analyzing time series gene expression data , 2004, Bioinform..

[6]  Natal A. W. van Riel,et al.  Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments , 2006, Briefings Bioinform..

[7]  Y. Lazebnik Can a biologist fix a radio? — or, what i learned while studying apoptosis , 2004, Biochemistry (Moscow).

[8]  D. Ransohoff Rules of evidence for cancer molecular-marker discovery and validation , 2004, Nature Reviews Cancer.

[9]  Robert Tibshirani,et al.  A simple method for assessing sample sizes in microarray experiments , 2006, BMC Bioinformatics.

[10]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[11]  P. Lambin,et al.  The hypoxic proteome is influenced by gene-specific changes in mRNA translation. , 2005, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[12]  M. Le Borgne,et al.  Topology and static response of interaction networks in molecular biology , 2006, Journal of The Royal Society Interface.

[13]  Ash A. Alizadeh,et al.  Gene Expression Signature of Fibroblast Serum Response Predicts Human Cancer Progression: Similarities between Tumors and Wounds , 2004, PLoS biology.

[14]  T. Speed,et al.  Design issues for cDNA microarray experiments , 2002, Nature Reviews Genetics.

[15]  Glenn Fung,et al.  Impact of supervised gene signatures of early hypoxia on patient survival. , 2007, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[16]  J. Foekens,et al.  Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer , 2005, The Lancet.

[17]  John Quackenbush Microarray data normalization and transformation , 2002, Nature Genetics.

[18]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[19]  G. Churchill Fundamentals of experimental design for cDNA microarrays , 2002, Nature Genetics.

[20]  Trevor Hastie,et al.  Gene Expression Programs in Response to Hypoxia: Cell Type Specificity and Prognostic Significance in Human Cancers , 2006, PLoS medicine.

[21]  G. Churchill,et al.  Statistical design and the analysis of gene expression microarray data. , 2007, Genetical research.