Mathematical modeling involves the use of mathematical equations and relationships to represent biological phenomena. Complementary to this type of modeling is the use of computer simulations to represent these modeling approaches in multiple dimensions. These approaches serve two purposes. First, they provide a basic framework for the interrogation and integration of data, often providing insight into the type and quality needed for addressing a hypothesis or experimental design. This feature is especially useful when trying to integrate or analyze the large datasets generally associated with systems biology. Second, and more importantly, these models or simulations should allow one to predict the biological state under investigation and predict how the natural process will behave in various circumstances. These problems center on the understanding of the behavior of biological systems whose function is governed by the spatial and temporal ordering of multiple interacting components at the molecular, cellular, and tissue levels. We will also develop bioinformatics and bioimaging models to integrate and analyze the data generated from Component 1, and make use of the information obtained from data analysis, biological knowledge to build in silico models to model TIC behavior, cancer cell apoptosis, cell migration, cell cycle changes and drug treatment response. The goal of this component is to take advantage of our combined expertise in cell biology and computational modeling to develop coherent experimental protocols and construct biomathematical models for understanding the mechanism underiying breast cancer stem cell evolution, i.e., how one stem cell evolves into breast tumor with various sizes and compositions in cell microenvironment. Our hypothesis is that that TIC behavior during tumor development can be simulated using a robust, multiscale mathematical/computational model of TIC behavior during breast cancer development. Further, that these models can be built to reflect not only the molecular, cellular, and tissue-level dynamics, but also to allow prediction of the response of TIC to experimental therapeutics.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA149196-04
Application #
8505402
Study Section
Special Emphasis Panel (ZCA1-SRLB-C)
Project Start
Project End
Budget Start
2013-03-01
Budget End
2014-02-28
Support Year
4
Fiscal Year
2013
Total Cost
$470,906
Indirect Cost
Name
Methodist Hospital Research Institute
Department
Type
DUNS #
185641052
City
Houston
State
TX
Country
United States
Zip Code
77030
Neelakantan, Deepika; Zhou, Hengbo; Oliphant, Michael U J et al. (2017) EMT cells increase breast cancer metastasis via paracrine GLI activation in neighbouring tumour cells. Nat Commun 8:15773
Chen, Suyun; Ibrahim, Nuhad K; Yan, Yuanqing et al. (2017) Complete Metabolic Response on Interim 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography to Predict Long-Term Survival in Patients with Breast Cancer Undergoing Neoadjuvant Chemotherapy. Oncologist 22:526-534
Liang, Diana H; Choi, Dong Soon; Ensor, Joe E et al. (2016) The autophagy inhibitor chloroquine targets cancer stem cells in triple negative breast cancer by inducing mitochondrial damage and impairing DNA break repair. Cancer Lett 376:249-58
Dong, J; Zhao, W; Shi, A et al. (2016) The PR status of the originating cell of ER/PR-negative mouse mammary tumors. Oncogene 35:4149-54
Dobrolecki, Lacey E; Airhart, Susie D; Alferez, Denis G et al. (2016) Patient-derived xenograft (PDX) models in basic and translational breast cancer research. Cancer Metastasis Rev 35:547-573
Tanei, Tomonori; Choi, Dong Soon; Rodriguez, Angel A et al. (2016) Antitumor activity of Cetuximab in combination with Ixabepilone on triple negative breast cancer stem cells. Breast Cancer Res 18:6
Yeung, Tsz-Lun; Leung, Cecilia S; Li, Fuhai et al. (2016) Targeting Stromal-Cancer Cell Crosstalk Networks in Ovarian Cancer Treatment. Biomolecules 6:3
Hein, S M; Haricharan, S; Johnston, A N et al. (2016) Luminal epithelial cells within the mammary gland can produce basal cells upon oncogenic stress. Oncogene 35:1461-7
Park, Jun Hyoung; Vithayathil, Sajna; Kumar, Santosh et al. (2016) Fatty Acid Oxidation-Driven Src Links Mitochondrial Energy Reprogramming and Oncogenic Properties in Triple-Negative Breast Cancer. Cell Rep 14:2154-2165
Zhao, Zhen; Zhu, Xiaoping; Cui, Kemi et al. (2016) In Vivo Visualization and Characterization of Epithelial-Mesenchymal Transition in Breast Tumors. Cancer Res 76:2094-2104

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