Computational models of cancer chemotherapy enhance the understanding of in vitro, in vivo, and clinical trial data and have the potential to contribute to the design of rational treatment regimens. In particular, mechanistic, predictive models are superior to statistical, phenomenological descriptions of data. Mechanistic models based on functional data from tumor biopsies will enable the response to treatment to be predicted for a specific patient, in contrast to statistical models in which the probability of response for a given patient may differ substantially from the population average. This review summarizes mathematical models developed to improve the design of treatment regimens using cell-cycle phase-specific chemotherapy. It starts with simple models of dose response, then moves to more complex models of scheduling cell-cycle phase-specific drugs, and finally discusses mechanistic models that incorporate both genetic drug resistance and cell cycle-mediated drug resistance. This last class of models will be most useful in designing treatment regimens tailored for individual patients.