Abstract: Most organisms exhibit various endogenous oscillating behaviors, which provides crucial information about how the internal biochemical processes are connected and regulated. Along with physical experiments, studying such periodicity of organisms often utilizes computer experiments relying on ordinary differential equations (ODE) because configuring the internal processes is difficult. Simultaneously utilizing both experiments, however, poses a significant statistical challenge due to its ill behavior in high dimension, identifiability, and numerical instability. This article devises a new Bayesian calibration strategy for oscillating biochemical models. The proposed methodology can efficiently estimate the computer experiments’ (ODE) parameters that match the physical experiments. The proposed framework is illustrated with circadian oscillations observed in a model filamentous fungus, Neurospora crassa.<\/p>\n","protected":false},"excerpt":{"rendered":"
Abstract: Most organisms exhibit various endogenous oscillating behaviors, which provides crucial information about how the internal biochemical processes are connected and regulated. Along with physical experiments, studying such periodicity of … <\/p>\n