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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA143907-04
Application #
8381751
Study Section
Special Emphasis Panel (ZCA1-SRLB-9)
Project Start
Project End
Budget Start
2012-08-01
Budget End
2013-07-31
Support Year
4
Fiscal Year
2012
Total Cost
$364,576
Indirect Cost
Name
University of Southern California
Department
Type
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90089
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
Garvey, Colleen M; Gerhart, Torin A; Mumenthaler, Shannon M (2017) Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques. J Vis Exp :
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
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
Ng, Chin F; Frieboes, Hermann B (2017) Model of vascular desmoplastic multispecies tumor growth. J Theor Biol 430:245-282
Leonard, Fransisca; Curtis, Louis T; Yesantharao, Pooja et al. (2016) Enhanced performance of macrophage-encapsulated nanoparticle albumin-bound-paclitaxel in hypo-perfused cancer lesions. Nanoscale 8:12544-52
Baugh, Evan H; Simmons-Edler, Riley; Müller, Christian L et al. (2016) Robust classification of protein variation using structural modelling and large-scale data integration. Nucleic Acids Res 44:2501-13
Ghaffarizadeh, Ahmadreza; Friedman, Samuel H; Macklin, Paul (2016) BioFVM: an efficient, parallelized diffusive transport solver for 3-D biological simulations. Bioinformatics 32:1256-8
Juarez, Edwin F; Lau, Roy; Friedman, Samuel H et al. (2016) Quantifying differences in cell line population dynamics using CellPD. BMC Syst Biol 10:92
Park, Seung-Min; Lee, Jae Young; Hong, Soongweon et al. (2016) Dual transcript and protein quantification in a massive single cell array. Lab Chip 16:3682-8

Showing the most recent 10 out of 120 publications