Speaker
Description
In this talk, we examine switching behavior in stochastic reaction networks, where molecular copy numbers fluctuate between multiple distinct states. Such switching occurs when intrinsic noise drives transitions between multiple stable or quasi-stable states, rather than through deterministic oscillations. Stochastic switching has been observed in various biological phenomena, including gene expression, cell fate decisions, and biochemical oscillators with noise-induced transitions.
We focus on two models exhibiting this behavior, emphasizing that while both display state switching, their underlying mechanisms differ significantly. Using stochastic simulations, we analyze and compare the dynamics of the two models, tuning parameters so that their trajectories appear similar. From these oscillatory time series, we extract key features and apply several measures and classification techniques to determine whether the models can be distinguished.
Additional preliminary results, including extensions to other network architectures and parameter regimes, will also be discussed. This work is conducted jointly with Dongli Deng at UMBC.
