Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data.

ABSTRACT: Bayesian and non-Bayesian moment-based inference methods are commonly used to estimate the parameters defining stochastic models of gene regulatory networks from noisy single cell or population snapshot data. However, a systematic investigation of the accuracy of the predictions of these methods remains missing. Here, we present the results of such a study using synthetic noisy data of a negative auto-regulatory transcriptional feedback loop, one of the most common building blocks of complex gene regulatory networks. We study the error in parameter estimation as a function of (i) number of cells in each sample; (ii) the number of time points; (iii) the highest-order moment of protein fluctuations used for inference; (iv) the moment-closure method used for likelihood approximation. We find

SUBMITTER: Cao Z 

PROVIDER: S-EPMC6505555 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature