Simon Kasif
Computer systems specification, analysis and diagnosis are arguably one of the most practically important topics in computer science and engineering. This research produced widely used tools and environments that allow to specify and analyze hardware systems and networks as well as recognize and correct unexpected behaviors, network intrusion or anomalies.
Medicine, on surface presents similar problems. Our cells are provided with basic instructions that when executed properly enable living organisms to function correctly.
As a result of genetic, epigenetic or environmental perturbations our cells exhibit aberrations in their functional behaviors leading to major diseases such as cancer or diabetes.
However, biology is very complex and it is often difficult to formalize the entire repertoire of "normal" behaviors at the molecular level. At the clinical level it is also difficult to formally define an "abnormal" or disease phenotype (e.g. Diabetes, Alzheimer's).
We describe the new paradigm of network signatures of disease that allow us to recognize "anomalies" at the molecular network level. Specifically, we review on-going research in this area including Gene Network Enrichment Analysis and show how it enables biomedical researchers to identify and confirm disregulated molecular processes in diabetes, insulin resistance that allude recognition by standard methods. Applications of the paradigm to network signatures of cancer is also discussed.
This work has the potential to lead to new diagnostic or prognostic biomarkers as well as new drug targets.