About a century ago, Conrad Röentgen discovered X-rays, and Henri Becquerel discovered a new phenomenon, which Marie and Pierre Curie later coined as radio-activity. Since their seminal work, we have learned much about the physical properties of radiation and its effects on living matter. Alas, the more we discover, the more we appreciate the complexity of the biological processes that are triggered by radiation exposure and eventually lead (or do not lead) to disease. Equipped with modern biological methods of high-throughput experimentation, imaging, and vastly increased computational prowess, we are now entering an era where we can piece some of the multifold aspects of radiation exposure and its sequelae together, and develop a more systemic understanding of radiogenic effects such as radio-carcinogenesis than has been possible in the past. It is evident from the complexity of even the known processes that such an understanding can only be gained if it is supported by mathematical models. At this point, the construction of comprehensive models is hampered both by technical inadequacies and a paucity of appropriate data. Nonetheless, some initial steps have been taken already and the generally increased interest in systems biology may be expected to speed up future progress. In this context, we discuss in this article examples of relatively small, yet very useful models that elucidate selected aspects of the effects of exposure to ionizing radiation and may shine a light on the path before us.