The growing importance of integrative modern biomedical research has created a strong demand for investigators who have the background and training in both the mathematical sciences and biological/medical sciences. The goal of this training program is to recruit and to train students interested in attaining such combined foundations, to educate the next generation of bio mathematical researchers with the skill to solve biomedical issues. Trainees are recruited from pre-doctoral students already admitted to academic departments/units and counseled by participating faculty members. Thirty-five faculty members in over a dozen departments or research units at UCLA participate in this program. The interests of these faculty represent a broad range of biomedical research activities that bridge mathematical modeling and the biological sciences, with special strength in theoretical biophysics, genetics, medical imaging, neurosciences, pharmacology, and physiology with the ability to participate in clinical and translational research activities. The emphasis of the trainng program will be on the early years of trainees'graduate study, providing students an integrated foundation in quantitative methodology, biological training, and research experience in mathematical modeling applications in biology and medicine to serve as a gateway to suitable dissertation research topics and future career paths.
The goals of the systems and integrative biology training program at UCLA is to train students to be proficient in both mathematical and biological/medical sciences, in order to help solve sophisticated problems in the increasingly complex world of biomedical research.
|Brown, Robert; Pasaniuc, Bogdan (2014) Enhanced methods for local ancestry assignment in sequenced admixed individuals. PLoS Comput Biol 10:e1003555|
|Shen, Jia; Sheng, Xiangpeng; Chang, Zenan et al. (2014) Iron metabolism regulates p53 signaling through direct heme-p53 interaction and modulation of p53 localization, stability, and function. Cell Rep 7:180-93|
|Kerr, Wesley T; Hwang, Eric S; Raman, Kaavya R et al. (2014) Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation. Int Workshop Pattern Recognit Neuroimaging :1-4|
|Kerr, Wesley T; Douglas, Pamela K; Anderson, Ariana et al. (2014) The utility of data-driven feature selection: re: Chu et al. 2012. Neuroimage 84:1107-10|
|Chang, Joshua C; Chou, Tom (2014) Iterative graph cuts for image segmentation with a nonlinear statistical shape prior. J Math Imaging Vis 49:87-97|
|Crawford, Forrest W; Minin, Vladimir N; Suchard, Marc A (2014) Estimation for general birth-death processes. J Am Stat Assoc 109:730-747|
|Roussotte, Florence F; Daianu, Madelaine; Jahanshad, Neda et al. (2014) Neuroimaging and genetic risk for Alzheimer's disease and addiction-related degenerative brain disorders. Brain Imaging Behav 8:217-33|
|Chang, Joshua C; Brennan, Kevin C; He, Dongdong et al. (2013) A mathematical model of the metabolic and perfusion effects on cortical spreading depression. PLoS One 8:e70469|
|Crawford, Forrest W; Suchard, Marc A (2013) Diversity, disparity, and evolutionary rate estimation for unresolved Yule trees. Syst Biol 62:439-55|
|Park, Miran; Loverdo, Claude; Schreiber, Sebastian J et al. (2013) Multiple scales of selection influence the evolutionary emergence of novel pathogens. Philos Trans R Soc Lond B Biol Sci 368:20120333|
Showing the most recent 10 out of 47 publications