| Abstract: |
| Markov processes are widely used to model the dynamics of biological processes evolving on networks. Complexity reduction for such models aims to capture the essential dynamics of the process via a simpler representation, with minimal loss of accuracy. The stochastic shielding approximation is a novel dimension reduction method that has been used to simplify stochastic network models arising in neuroscience, such as randomly gated ion channel models, but applies broadly to many biological systems. In this talk, I will describe the stochastic shielding approximation and our related edge importance measure which allows us to rank each noise source according to its contribution to the observed variability. The approximation works by replacing the lowest ranked Markovian transitions with deterministic ones, and doesn`t significantly affect the variability of the observed variables. I will also explore the robustness of the method under conditions of timescale separation and population sparsity. |
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