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Reconstructing dynamic molecular states from single-cell time series
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. It is a prerequisite for a principled understanding of the inner working of a system. Due to the complexity of intracellular processes experimental techniques that can retrieve such a sufficient summary are beyond reach. For the case of stochastic biomolecular reaction networks we show how to complete 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 approximation 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.
Cited literature [48 references]
https://hal.archives-ouvertes.fr/hal-01362502
Contributor : Loïc Paulevé Connect in order to contact the contributor
Submitted on : Thursday, September 8, 2016 - 9:59:17 PM
Last modification on : Thursday, July 8, 2021 - 3:47:04 AM
Long-term archiving on: : Friday, December 9, 2016 - 1:24:34 PM
Contributor : Loïc Paulevé Connect in order to contact the contributor
Submitted on : Thursday, September 8, 2016 - 9:59:17 PM
Last modification on : Thursday, July 8, 2021 - 3:47:04 AM
Long-term archiving on: : Friday, December 9, 2016 - 1:24:34 PM
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