Admin Supplement Yoga Instr

An ongoing explosion of microbiological data has led to the development of new mathematical models that help us understand biology at a quantitative level. Biomedical science has not yet benefited to the same extent from mathematical modeling. Growing availability of biomedical data, however, is poised to change that, with many exciting prospects. In particular, quantitative models should allow for personalized medicine that customizes patient treatment protocols and optimally balances costs and benefits of medical interventions. The PIs propose to develop new mathematical and statistical models for the dynamics of subjective pain in patients suffering from chronic pain. They will exploit newly available data combining frequent subjective patient pain reports via mobile phone app with objective data from wearable medical devices. The PIs will develop an array of models based on deterministic and stochastic differential equations as well as nonlinear statistical analysis and machine learning with the following objectives: • Understand quantitatively the fundamental processes underlying subjective pain dynamics • Predict future pain levels in patient populations • Optimally mitigate pain while minimizing medication Methods to be used include the following: • Differential equation models based on existing qualitative biomedical theory • Statistical models derived from newly available data on pain dynamics • Nonlinear dynamical systems theory for analysis of model implications • Hybrid analysis of combined statistical and mechanistic models