Reconstructing dynamic molecular states from single-cell time series

Lirong Huang 1 Loïc Paulevé 2 Christoph Zechner 3 Michael Unger 4 Anders S. Hansen 5 Heinz Koeppl 6
1 Institute of Molecular Systems Biology
2 BioInfo - LRI - Bioinformatique (LRI)
LRI - Laboratoire de Recherche en Informatique
3 D-BSSE - Department of Biosystems Science and Engineering [ETH Zürich]
4 IBC - Institute of Biochemistry
5 Department of Chemistry and Chemical Biology
6 Bioinspired Communication Systems Lab
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.
Keywords : Optimal filtering continuous-time Markov chains chemical master equation moment dynamics gene expression
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Submitted on : Thursday, September 8, 2016 - 9:59:17 PM
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INSMI | TDS-MACS | UMR8623 | INRIA | LRI-BIOINFO | CENTRALESUPELEC | UNIV-PARIS-SACLAY

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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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