Combination therapy is the hallmark of therapies for cancer, viral or microbial infections, hypertension and other diseases involving complex biological networks. Synergistic drug combinations, which are more effective than predicted from summing effects of individual drugs, often achieve increased therapeutic index. Because drug-effect is dose-dependent, multiple doses of an individual drug need to be examined, yielding rapidly increasing number of combinations and a challenging high dimensional mathematical and statistical problem. The lack of proper system approach for discovery, the design and analysis methods for multi-drug combination studies have resulted in many missed therapeutic opportunities. Although system biology holds the promise to unveil complex interactions within biological systems, the knowledge on network remains predominantly at the level of topology. We propose a novel two-stage procedure starting with an initial selection by utilizing an in silico model built upon experimental data of single drugs and current system biology information to obtain maximum likelihood estimate. In this talk, I will present the maximal power experimental design on multi-drug combinations, statistical modeling of the joint dose effect, and its statistical properties and show how applied mathematics can help the statistical modeling of drug combinations in systems biology. The development of Vorinostat combined with Ara-C will be discussed as an example.