Growing heterogeneous tumors in silico.
An in silico tool that can be utilized in the clinic to predict neoplastic progression and propose individualized treatment strategies is the holy grail of computational tumor modeling. Building such a tool requires the development and successful integration of a number of biophysical and mathematical models. In this paper, we work toward this long-term goal by formulating a cellular automaton model of tumor growth that accounts for several different inter-tumor processes and host-tumor interactions. In particular, the algorithm couples the remodeling of the microvasculature with the evolution of the tumor mass and considers the impact that organ-imposed physical confinement and environmental heterogeneity have on tumor size and shape. Furthermore, the algorithm is able to account for cell-level heterogeneity, allowing us to explore the likelihood that different advantageous and deleterious mutations survive in the tumor cell population. This computational tool we have built has a number of applications in its current form in both predicting tumor growth and predicting response to treatment. Moreover, the latent power of our algorithm is that it also suggests other tumor-related processes that need to be accounted for and calls for the conduction of new experiments to validate the model's predictions.
Gevertz
Jana
J
Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA. torquato@princeton.edu
Torquato
S
S
D000465:Algorithms; D000818:Animals; D049109:Cell Proliferation; D006801:Humans; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000378:metabolism*; D009369:Neoplasms / Q000473:pathology* / Q000503:physiopathology*; D015398:Signal Transduction
D009363:Neoplasm Proteins