| Abstract: |
| Modeling is routinely used across multiple disciplines to priortize designs before testing and implementation, but this is seldom done in the medical and pharmaceutical industries because reliable models are difficult to build and the systems are extremely complex and incompletly understood. My lab is building models and approaches for capturing mechanisms that drive behavior of single mammalian cells as a key foundational component of such efforts. In particular, we have been building hybrid models comprising large-scale ordinary differential equation systems with stochastic components to capture low molecule number fluctuations with Poisson-like descriptions. A major simulation bottleneck in such large-scale cellular modeling is often not solving the equations themselves, but rather communication between submodels with different formalisms. In this talk, I`ll present some of our latest work in these areas, highlighting some of the interesting biological findings arising from such modeling, as well as what I believe are key numerical and computational method barriers that if solved would catalyze growth in the field. |
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