Mathematical models addressing important aspects of hematological malignancies have recently facilitated an improved understanding of the involved complex biological processes and the prediction of potential targets for therapeutic approaches. These models investigate a wide spectrum of topics ranging from metabolic processes, gene regulatory networks and signal transduction up to the behavior of cell populations. However, despite this range of biological processes, the modeling strategies share many common features. Biological knowledge is translated into abstract descriptions representing complex networks and the parameters of these mathematical models are derived from literature data or estimated from experimental measurements. The established mathematical models are used to interrogate key properties of the investigated system by model simulations. These predictions are validated based on previously published or novel experiments. Additionally, new drug targets are predicted or novel insights into biological processes are provided. Here, we summarize the strategies employed to establish four mathematical models that address different processes in leukemia and lymphoma cells. Furthermore, we show how these systems biology approaches could contribute to elucidate the pathobiology of hematological malignancies.