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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|Renfrew, P Douglas; Craven, Timothy W; Butterfoss, Glenn L et al. (2014) A rotamer library to enable modeling and design of peptoid foldamers. J Am Chem Soc 136:8772-82|
|Bendall, Sean C; Davis, Kara L; Amir, El-Ad David et al. (2014) Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157:714-25|
|Gaudillière, Brice; Fragiadakis, Gabriela K; Bruggner, Robert V et al. (2014) Clinical recovery from surgery correlates with single-cell immune signatures. Sci Transl Med 6:255ra131|
|Wu, Min; Frieboes, Hermann B; Chaplain, Mark A J et al. (2014) The effect of interstitial pressure on therapeutic agent transport: coupling with the tumor blood and lymphatic vascular systems. J Theor Biol 355:194-207|
|Bruggner, Robert V; Bodenmiller, Bernd; Dill, David L et al. (2014) Automated identification of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci U S A 111:E2770-7|
|Angelo, Michael; Bendall, Sean C; Finck, Rachel et al. (2014) Multiplexed ion beam imaging of human breast tumors. Nat Med 20:436-42|
|Holman, Jerry D; Tabb, David L; Mallick, Parag (2014) Employing ProteoWizard to Convert Raw Mass Spectrometry Data. Curr Protoc Bioinformatics 46:13.24.1-9|
|Wang, Zhihui; Deisboeck, Thomas S; Cristini, Vittorio (2014) Development of a sampling-based global sensitivity analysis workflow for multiscale computational cancer models. IET Syst Biol 8:191-7|
|Lao, Brooke Bullock; Drew, Kevin; Guarracino, Danielle A et al. (2014) Rational design of topographical helix mimics as potent inhibitors of protein-protein interactions. J Am Chem Soc 136:7877-88|
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