Using steady-state formula to estimate time-dependent parameters of stochastic gene transcription models

In this study, we developed a method to estimate time-dependent parameters in a classical two-state model without prior assumptions regarding the system parameters. At each measured time point, the method fitted the dynamic distribution data using a steady-state distribution formula, in which the estimated constant parameters were approximated as time-dependent parameter values at the measured time point. The accuracy of this method can be guaranteed for RNA molecules with relatively high degradation rates and genes with relatively slow responses to induction. We quantify the accuracy of the method and implemented this method on two sets of dynamic distribution data from prokaryotic and eukaryotic cells, and revealed the temporal modulation of transcription burst size in response to environmental changes.PMID:38280446 | DOI:10.1016/j.biosystems.2024.105128

Source: Biosystems - January 27, 2024 Category: Biotechnology Authors: Congrun Zhang Feng Jiao Source Type: research