BACKGROUND: Complex diseases, such as Type 2 Diabetes, are generally caused by 
multiple factors, which hamper effective drug discovery. To combat these 
diseases, combination regimens or combination drugs provide an alternative way, 
and are becoming the standard of treatment for complex diseases. However, most 
of existing combination drugs are developed based on clinical experience or 
test-and-trial strategy, which are not only time consuming but also expensive.
RESULTS: In this paper, we presented a novel network-based systems biology 
approach to identify effective drug combinations by exploiting high throughput 
data. We assumed that a subnetwork or pathway will be affected in the networked 
cellular system after a drug is administrated. Therefore, the affected 
subnetwork can be used to assess the drug's overall effect, and thereby help to 
identify effective drug combinations by comparing the subnetworks affected by 
individual drugs with that by the combination drug. In this work, we first 
constructed a molecular interaction network by integrating protein interactions, 
protein-DNA interactions, and signaling pathways. A new model was then developed 
to detect subnetworks affected by drugs. Furthermore, we proposed a new score to 
evaluate the overall effect of one drug by taking into account both efficacy and 
side-effects. As a pilot study we applied the proposed method to identify 
effective combinations of drugs used to treat Type 2 Diabetes. Our method 
detected the combination of Metformin and Rosiglitazone, which is actually 
Avandamet, a drug that has been successfully used to treat Type 2 Diabetes.
CONCLUSIONS: The results on real biological data demonstrate the effectiveness 
and efficiency of the proposed method, which can not only detect effective 
cocktail combination of drugs in an accurate manner but also significantly 
reduce expensive and tedious trial-and-error experiments.