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Reconstructing dynamic molecular states from single-cell time series

Huang, L. and Hansen, A. S. and Pauleve, L. and Unger, M. and Zechner, C. and Koeppl, H. (2016):
Reconstructing dynamic molecular states from single-cell time series.
In: Journal of The Royal Society Interface, Royal Society Publishing, ISSN 1742-5689, [Online-Edition: http://rsif.royalsocietypublishing.org/content/13/122/201605...],
[Article]

Abstract

The notion of state for a system is prevalent in the quantitative sciences and refers to the minimal system summary sufficient to describe the time evolution of the system in a self-consistent manner. This is a prerequisite for a principled understanding of the inner workings of a system. Owing to the complexity of intracellular processes, experimental techniques that can retrieve a sufficient summary are beyond our reach. For the case of stochastic biomolecular reaction networks, we show how to convert the partial state information accessible by experimental techniques into a full system state using mathematical analysis together with a computational model. This is intimately related to the notion of conditional Markov processes and we introduce the posterior master equation and derive novel approximations to the corresponding infinite-dimensional posterior moment dynamics. We exemplify this state reconstruction approach using both in silico data and single-cell data from two gene expression systems in Saccharomyces cerevisiae, where we reconstruct the dynamic promoter and mRNA states from noisy protein abundance measurements.

Item Type: Article
Erschienen: 2016
Creators: Huang, L. and Hansen, A. S. and Pauleve, L. and Unger, M. and Zechner, C. and Koeppl, H.
Title: Reconstructing dynamic molecular states from single-cell time series
Language: English
Abstract:

The notion of state for a system is prevalent in the quantitative sciences and refers to the minimal system summary sufficient to describe the time evolution of the system in a self-consistent manner. This is a prerequisite for a principled understanding of the inner workings of a system. Owing to the complexity of intracellular processes, experimental techniques that can retrieve a sufficient summary are beyond our reach. For the case of stochastic biomolecular reaction networks, we show how to convert the partial state information accessible by experimental techniques into a full system state using mathematical analysis together with a computational model. This is intimately related to the notion of conditional Markov processes and we introduce the posterior master equation and derive novel approximations to the corresponding infinite-dimensional posterior moment dynamics. We exemplify this state reconstruction approach using both in silico data and single-cell data from two gene expression systems in Saccharomyces cerevisiae, where we reconstruct the dynamic promoter and mRNA states from noisy protein abundance measurements.

Journal or Publication Title: Journal of The Royal Society Interface
Publisher: Royal Society Publishing
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
Date Deposited: 15 Sep 2016 06:27
Official URL: http://rsif.royalsocietypublishing.org/content/13/122/201605...
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