Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data
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Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time-series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.
Burton , J , Manning , C S , Rattray , M , Papalopulu , N & Kursawe , J 2021 , ' Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data ' , Journal of the Royal Society Interface , vol. 18 , no. 182 , 20210393 . https://doi.org/10.1098/rsif.2021.0393
Journal of the Royal Society Interface
Copyright © 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
DescriptionThis work was supported by a Wellcome Trust Four-Year PhD Studentship in Basic Science to J.B. (219992/Z/19/Z) and a Wellcome Trust Senior Research Fellowship to N.P. (090868/Z/09/Z). C.M. was supported by a Sir Henry Wellcome Fellowship (103986/Z/14/Z) and University of Manchester Presidential Fellowship. M.R.’s work was supported by a Wellcome Trust Investigator Award (204832/B/16/Z).
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