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

总体而言，该项目旨在(1)增加对将肿瘤和微环境中的分子和细胞尺度变化与肿瘤生长联系起来的生物物理机制的见解，(2)开发技术，使我们能够容易地将尖端的实验测量结合到多尺度模型中，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)调查和量化肿瘤对治疗的反应的时空动力学，(4)改进形成真正综合建模的骨干的体内电子发育反馈回路，以及因此(4)推动多尺度的前沿，综合癌症模型。为了量化不同尺度的复杂癌症现象之间的关系，我们利用离散和连续建模方法的优势，使用混合建模。我们实施混合、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。这涉及到将Cristini、Macklin及其同事开发的肿瘤尺度模型和分子/细胞尺度模型与研究项目2开发的细胞信号和进化/遗传模型动态耦合。这还需要与Gambhir及其同事对肿瘤生长、血管形成和化疗反应的最先进的生命中时间进程测量相结合。这些实验将(1)为我们提供将塑造模型开发的第一手生物数据，(2)为我们提供对唯一约束建模框架的关键模型参数的精确测量，(3)为模型验证和测试提供额外的、独立的测试，以及(4)为真正的综合建模提供机会，在此期间，我们将对经过校准的模型进行第一轮调查，并进行后续实验以测试新的癌症生物学假说并改进多尺度模型。

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.