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。为了量化不同尺度的复杂癌症现象之间的关系，我们利用混合建模方法来利用离散和连续建模方法的优点。我们实现混合，多尺度算法作为癌症建模的下一阶段，特别是淋巴瘤和白血病建模。这包括动态耦合肿瘤尺度模型和分子/细胞尺度模型，由Cristini， Macklin和同事开发的细胞信号和进化/遗传模型由研究项目2开发。这也需要结合Gambhir和他的同事对肿瘤生长、血管形成和化疗反应的最先进的活体时间过程测量。这些实验将(1)为我们提供第一手的生物学数据，这些数据将塑造模型的发展；(2)为我们提供关键模型参数的精确测量，这些参数独特地约束了建模框架；(3)为模型验证和测试提供额外的、独立的测试；(4)为真正的综合建模提供机会。我们对校准模型的第一轮调查将导致后续实验，以测试新的癌症生物学假设并改进多尺度模型。

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.