Breast cancer is the most commonly diagnosed cancer, and the second leading cause of cancer deaths among American women. As such, breast cancer has been identified as a public health priority in the United States. Despite this clinical and social importance, we are only now beginning to understand the molecular, cellular, and developmental mechanisms underlying breast cancer initiation and progression in enough detail to allow rudimentary predictions of treatment response to be made. Mathematical modeling and computational simulation offers the extraordinary promise of integrating huge quantities of diverse data into coherent developmental and predictive models capable of informing "personalized medicine" decisions. However, these models can only be as good as our biological understanciing of the parameters involved in breast cancer development, and as the experimental data on which they are based. Our educational and training plan is designed to fill a need for an organized training process in combined biological and mathematical/computational modeling of breast cancer for postdoctoral fellows and undergraduates. In subsequent funding cycles, we intend to extend this training program to include graduate student trainees. Our proposed training program brings together, in a formal way, researchers from clinical, translational, and basic science areas of experimental breast cancer research, as well as researchers in the areas of mathematical modeling, computational biology and bioinformatics with a keen interest in working toward a common goal of understanding breast cancer biology. In this proposal, we will establish two unique multidisciplinary training programs, one for undergraduates and one for postdoctoral trainees. The undergraduate program will be entitled the "Multidisciplinary Summer Undergraduate Training Program in Experimental and Mathematical Modeling of Cancer." This program will be geared toward individuals exploring their interest in cancer research and will serve to introduce undergraduates to various aspects of experimental biology and to mathematical/computational modeling approaches and analyses. The multidisciplinary postdoctoral training program will be geared toward recruitment and training of individuals holding Ph.D. or M.D./Ph.D. degrees in mathematical modeling, computational biology, biostatistics/bioinformatics, or a related advanced degree who are interested in gaining significant experimental experience and deeper conceptual insight into breast cancer development in a laboratory or clinical setting. Prospective candidates will be teamed both with a basic science/clinical mentor, and a mathematical modeling/computational biology mentor, for the development of a suitable research project addressing an important unanswered question in breast cancer biology from a combined experimental and mathematical/computational perspective.

Agency
National Institute of Health (NIH)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA149196-05
Application #
8628783
Study Section
Special Emphasis Panel (ZCA1)
Project Start
Project End
Budget Start
Budget End
Support Year
5
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Methodist Hospital Research Institute
Department
Type
DUNS #
City
Houston
State
TX
Country
United States
Zip Code
77030
Sinha, Vidya C; Qin, Lan; Li, Yi (2015) A p53/ARF-dependent anticancer barrier activates senescence and blocks tumorigenesis without impacting apoptosis. Mol Cancer Res 13:231-8
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
Tang, Lei; van de Ven, Anne L; Guo, Dongmin et al. (2014) Computational modeling of 3D tumor growth and angiogenesis for chemotherapy evaluation. PLoS One 9:e83962
Haricharan, S; Hein, S M; Dong, J et al. (2014) Contribution of an alveolar cell of origin to the high-grade malignant phenotype of pregnancy-associated breast cancer. Oncogene 33:5729-39
Wei, Wei; Tweardy, David J; Zhang, Mei et al. (2014) STAT3 signaling is activated preferentially in tumor-initiating cells in claudin-low models of human breast cancer. Stem Cells 32:2571-82
Brocato, Terisse; Dogra, Prashant; Koay, Eugene J et al. (2014) Understanding Drug Resistance in Breast Cancer with Mathematical Oncology. Curr Breast Cancer Rep 6:110-120
Yu, Peng; Shaw, Chad A (2014) An efficient algorithm for accurate computation of the Dirichlet-multinomial log-likelihood function. Bioinformatics 30:1547-54
Dave, Bhuvanesh; Granados-Principal, Sergio; Zhu, Rui et al. (2014) Targeting RPL39 and MLF2 reduces tumor initiation and metastasis in breast cancer by inhibiting nitric oxide synthase signaling. Proc Natl Acad Sci U S A 111:8838-43
Jin, Guangxu; Wong, Stephen T C (2014) Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov Today 19:637-44
Yin, Zheng; Sailem, Heba; Sero, Julia et al. (2014) How cells explore shape space: a quantitative statistical perspective of cellular morphogenesis. Bioessays 36:1195-203

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