Cargando…

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

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 rem...

Descripción completa

Detalles Bibliográficos
Autores principales: Cao, Zhixing, Grima, Ramon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505555/
https://www.ncbi.nlm.nih.gov/pubmed/30940028
http://dx.doi.org/10.1098/rsif.2018.0967
_version_ 1783416780095488000
author Cao, Zhixing
Grima, Ramon
author_facet Cao, Zhixing
Grima, Ramon
author_sort Cao, Zhixing
collection PubMed
description 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 that for sample sizes typical of flow cytometry experiments, parameter estimation by maximizing the likelihood is as accurate as using Bayesian methods but with a much reduced computational time. We also show that the choice of moment-closure method is the crucial factor determining the maximum achievable accuracy of moment-based inference methods. Common likelihood approximation methods based on the linear noise approximation or the zero cumulants closure perform poorly for feedback loops with large protein–DNA binding rates or large protein bursts; this is exacerbated for highly heterogeneous cell populations. By contrast, approximating the likelihood using the linear-mapping approximation or conditional derivative matching leads to highly accurate parameter estimates for a wide range of conditions.
format Online
Article
Text
id pubmed-6505555
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher The Royal Society
record_format MEDLINE/PubMed
spelling pubmed-65055552019-05-21 Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data Cao, Zhixing Grima, Ramon J R Soc Interface Life Sciences–Mathematics interface 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 that for sample sizes typical of flow cytometry experiments, parameter estimation by maximizing the likelihood is as accurate as using Bayesian methods but with a much reduced computational time. We also show that the choice of moment-closure method is the crucial factor determining the maximum achievable accuracy of moment-based inference methods. Common likelihood approximation methods based on the linear noise approximation or the zero cumulants closure perform poorly for feedback loops with large protein–DNA binding rates or large protein bursts; this is exacerbated for highly heterogeneous cell populations. By contrast, approximating the likelihood using the linear-mapping approximation or conditional derivative matching leads to highly accurate parameter estimates for a wide range of conditions. The Royal Society 2019-04 2019-04-03 /pmc/articles/PMC6505555/ /pubmed/30940028 http://dx.doi.org/10.1098/rsif.2018.0967 Text en © 2019 The Authors. http://creativecommons.org/licenses/by/4.0/ 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.
spellingShingle Life Sciences–Mathematics interface
Cao, Zhixing
Grima, Ramon
Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data
title Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data
title_full Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data
title_fullStr Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data
title_full_unstemmed Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data
title_short Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data
title_sort accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data
topic Life Sciences–Mathematics interface
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505555/
https://www.ncbi.nlm.nih.gov/pubmed/30940028
http://dx.doi.org/10.1098/rsif.2018.0967
work_keys_str_mv AT caozhixing accuracyofparameterestimationforautoregulatorytranscriptionalfeedbackloopsfromnoisydata
AT grimaramon accuracyofparameterestimationforautoregulatorytranscriptionalfeedbackloopsfromnoisydata