Hierarchical Bayesian models of transcriptional and translational regulation processes with delays.

Autor:	Cortez MJ; Department of Mathematics, University of Houston, Houston, TX 77204, USA.; Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, Laguna 4031, Philippines., Hong H; Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea.; Biomedical Mathematics Group, Institute for Basic Science, Daejeon 34126, Korea., Choi B; Biomedical Mathematics Group, Institute for Basic Science, Daejeon 34126, Korea.; Division of Big Data Science, Korea University Sejong Campus, Sejong 30019, Korea., Kim JK; Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea.; Biomedical Mathematics Group, Institute for Basic Science, Daejeon 34126, Korea., Josić K; Department of Mathematics, University of Houston, Houston, TX 77204, USA.; Department of Biology and Biochemistry, University of Houston, Houston, TX 77204, USA.
Jazyk:	angličtina
Zdroj:	Bioinformatics (Oxford, England) [Bioinformatics] 2021 Dec 22; Vol. 38 (1), pp. 187-195.
DOI:	10.1093/bioinformatics/btab618
Abstrakt:	Motivation: Simultaneous recordings of gene network dynamics across large populations have revealed that cell characteristics vary considerably even in clonal lines. Inferring the variability of parameters that determine gene dynamics is key to understanding cellular behavior. However, this is complicated by the fact that the outcomes and effects of many reactions are not observable directly. Unobserved reactions can be replaced with time delays to reduce model dimensionality and simplify inference. However, the resulting models are non-Markovian, and require the development of new inference techniques. Results: We propose a non-Markovian, hierarchical Bayesian inference framework for quantifying the variability of cellular processes within and across cells in a population. We illustrate our approach using a delayed birth-death process. In general, a distributed delay model, rather than a popular fixed delay model, is needed for inference, even if only mean reaction delays are of interest. Using in silico and experimental data we show that the proposed hierarchical framework is robust and leads to improved estimates compared to its non-hierarchical counterpart. We apply our method to data obtained using time-lapse microscopy and infer the parameters that describe the dynamics of protein production at the single cell and population level. The mean delays in protein production are larger than previously reported, have a coefficient of variation of around 0.2 across the population, and are not strongly correlated with protein production or growth rates. Availability and Implementation: Accompanying code in Python is available at https://github.com/mvcortez/Bayesian-Inference. Contact: kresimir.josic@gmail.com or jaekkim@kaist.ac.kr or cbskust@korea.ac.kr. Supplementary Information: Supplementary data are available at Bioinformatics online. (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
Databáze:	MEDLINE
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