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.
We bring together a team of leading experts in mathematical modeling of cancer and multiple scales (PI Cristini) with a team of cancer biologists with innovative, cutting-edge in vivo tumor imaging methods (PI Gambhir) to conduct integrative modeling, where experiments shape model development, and subsequent simulations generate testable hypotheses for further experiments.
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|Tipton, Laura; Müller, Christian L; Kurtz, Zachary D et al. (2018) Fungi stabilize connectivity in the lung and skin microbial ecosystems. Microbiome 6:12|
|Lee, Jung-Rok; Appelmann, Iris; Miething, Cornelius et al. (2018) Longitudinal Multiplexed Measurement of Quantitative Proteomic Signatures in Mouse Lymphoma Models Using Magneto-Nanosensors. Theranostics 8:1389-1398|
|Lee, Jung-Rok; Chan, Carmel T; Ruderman, Daniel et al. (2017) Longitudinal Monitoring of Antibody Responses against Tumor Cells Using Magneto-nanosensors with a Nanoliter of Blood. Nano Lett 17:6644-6652|
|Yan, Huaming; Romero-Lopez, Monica; Frieboes, Hermann B et al. (2017) Multiscale Modeling of Glioblastoma Suggests that the Partial Disruption of Vessel/Cancer Stem Cell Crosstalk Can Promote Tumor Regression Without Increasing Invasiveness. IEEE Trans Biomed Eng 64:538-548|
|Yan, Huaming; Romero-López, Mónica; Benitez, Lesly I et al. (2017) 3D Mathematical Modeling of Glioblastoma Suggests That Transdifferentiated Vascular Endothelial Cells Mediate Resistance to Current Standard-of-Care Therapy. Cancer Res 77:4171-4184|
|Tennill, Thomas A; Gross, Mitchell E; Frieboes, Hermann B (2017) Automated analysis of co-localized protein expression in histologic sections of prostate cancer. PLoS One 12:e0178362|
|Ware, Matthew J; Curtis, Louis T; Wu, Min et al. (2017) Pancreatic adenocarcinoma response to chemotherapy enhanced with non-invasive radio frequency evaluated via an integrated experimental/computational approach. Sci Rep 7:3437|
|Ng, Chin F; Frieboes, Hermann B (2017) Model of vascular desmoplastic multispecies tumor growth. J Theor Biol 430:245-282|
|Garvey, Colleen M; Gerhart, Torin A; Mumenthaler, Shannon M (2017) Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques. J Vis Exp :|
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