Multi-Scale Cancer Modeling: From Cell Phenotypes to Cancer Spread and Response

总的来说，该项目的目的是：(1)增加对生物物理机制的见解，这些机制将肿瘤和微环境中分子和细胞尺度的变化与肿瘤的生长联系起来；(2)开发技术，使我们能够轻松地将尖端的实验测量纳入多尺度模型，从而更快地确定它们对整体肿瘤进展和治疗反应的影响。(3)研究和量化肿瘤对治疗反应的时空动态；(4)改进构成真正整合建模骨干的体内-芯片开发反馈回路；(4)推动多尺度、整合癌症建模的前沿。

Overall, the project is designed to (1) add insights as to the biophysical mechanisms that link molecular- and cell-scale variations in the tumor and the microenvironment to the tumor's growth, (2) develop techniques that allow us to readily incorporate cutting-edge experimental measurements into the multi-scale model and thus more quickly determine their impact on overall tumor progression and response to therapy, (3) investigate and quantify the spatiotemporal dynamics of tumor response to therapy, (4) improve the in vivo-in silico development feedback loop that forms the backbone of true integrative modeling, and so (4) push the frontier of multi-scale, integrative cancer modeling. In order to quantify the relationships between complex cancer phenomena at different scales, we harness the advantages of both discrete and continuum modeling approaches by employing hybrid modeling. We implement hybrid, multi-scale algorithms as the next stage of cancer modeling in general, and lymphoma and leukemia modeling in particular. This involves dynamically coupling tumor-scale models and molecular/cell-scale models developed by Cristini, Macklin and coworkers with cell signaling and evolutionary/hereditary models developed by Research Project 2. This also requires integration with state of- the-art intravital time-course measurements of tumor growth, vascularization, and response to chemotherapy by Gambhir and co-workers. These experiments will (1) provide us with first-hand biological data that will shape the model development, (2) provide us with precise measurements of key model parameters that uniquely constrain the modeling framework, (3) provide additional, independent tests for model validation and testing, and (4) provide an opportunity for true integrative modeling, where our first round of investigating the calibrated model leads to follow-up experiments to test new cancer biology hypotheses and improve the multiscale model.