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.
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Lirong Huang, Loïc Paulevé, Christoph Zechner, Michael Unger, Anders S. Hansen, et al.. Reconstructing dynamic molecular states from single-cell time series. Journal of the Royal Society Interface, the Royal Society, 2016, 13 (122), ⟨10.1098/rsif.2016.0533⟩. ⟨hal-01362502⟩

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