STAD extends CAPA-style anomaly detection to time series with polynomial trend and seasonality, with asymptotic consistency theory and finite-sample simulations.
Solving stochastic gene expression models using queueing theory: a tutorial review
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abstract
Stochastic models of gene expression are typically formulated using the chemical master equation, which can be solved exactly or approximately using a repertoire of analytical methods. Here, we provide a tutorial review of an alternative approach based on queueing theory that has rarely been used in the literature of gene expression. We discuss the interpretation of six types of infinite server queues from the angle of stochastic single-cell biology and provide analytical expressions for the stationary and non-stationary distributions and/or moments of mRNA/protein numbers, and bounds on the Fano factor. This approach may enable the solution of complex models which have hitherto evaded analytical solution.
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Detection of collective and point anomalies in the presence of trend and seasonality
STAD extends CAPA-style anomaly detection to time series with polynomial trend and seasonality, with asymptotic consistency theory and finite-sample simulations.