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Principles and strategies for developing network models in cancer.
1Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, New York, NY 10027, USA. dpeer@biology.columbia.edu
Cell
|March 19, 2011
Summary
Biologists can now reconstruct cell molecular networks using genome-wide data. This study details principles and strategies for inferring these networks, including differential networks for cancer research and drug response prediction.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- High-throughput technologies generate vast amounts of genome-wide data.
- Understanding functional molecular networks is crucial for cell biology.
- Cancer research requires identifying genes and processes driving disease phenotypes.
Purpose of the Study:
- To outline principles and strategies for inferring network models from genomic data.
- To describe approaches for inferring differential networks in cancer.
- To discuss the application of network-level understanding in cancer therapeutics.
Main Methods:
- Inferring network models from genomic data using outlined principles and strategies.
- Employing experimental and computational approaches to infer differential networks.
- Analyzing cancer data to identify disease-driving genes and processes.
Main Results:
- Established three underlying principles and six strategies for network inference.
- Demonstrated methods for inferring differential networks using cancer as a model.
- Identified key genes and processes driving cancer phenotypes through network analysis.
Conclusions:
- Network inference from genomic data offers powerful tools for biological research.
- Differential network analysis is effective for understanding disease mechanisms like cancer.
- A network-level perspective can guide personalized drug response prediction and cancer treatment.
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