Simulated cancer development in our model is based on the progression of time, with one day currently as the basic time unit corresponding to a physical day, so for any patient age the model gives a prediction of cancer development. For instance, starting with a patient age when cancer incidence is negligible and modeling the unique daily events (dependent upon the initial patient age) for 365 days corresponds to the cancer development at the age equal to the initial patient age plus one year.
We are using our model, currently a single-cell based model, to follow the fate of every cell in a human tissue, uterine epithelium, in order to investigate whether cancer incidence is a rare event and can be captured through simulation of every tissue stem cell that is committed to proliferate and form a clone in tissue regeneration. We start with 100,000 stem cells simultaneously on the first day of a menstrual cycle, and the tissue-specific parameters cause growth and then senescence during the course of the menstrual cycle. Since each cell is simulated separately, born in a particular menstrual cycle or at a particular patient age and inheriting alterations from its ancestors, the model can incorporate the accumulation of carcinogenic events and stimulus that may be specific to the phase of the menstrual cycle or the patient age. To account for the physiological changes that occur during a woman's reproductive life stages, we simulate endometrial cancer development with age-specific values for stem cell number, mutation rate, and hormone levels for 3 stages of age: pre-menopausal (15 - 50yr), peri-menopausal (50-53yr) and postmenopausal women (53-80yr). Additionally, the effects of other environmental factors and genetic alterations on uterine oncogenesis can be quantitatively simulated on a daily time scale if detailed and age-specific information regarding those factors are available for a population or an individual woman. Thus, our model can predict a woman's probability of having endometrial cancer at any age if her age-specific variables are known. The prediction can be made for a population if distribution parameters such as the mean values are available.
However, not all physiological factors are currently modeled: for the first stage of our model, we only consider the uterine epithelial tissue as a collection of billions of cells and have not yet modeled cell-cell interaction in the tissue and the effect of spatial restraint on cell growth.