ArticleJournal of the Royal Society, Interface2016
Reconstructing dynamic molecular states from single-cell time series.
Article in Journal of the Royal Society, Interface, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Space.Proceedings of machine learning research · 2026Article
- Advanced methods for gene network identification and noise decomposition from single-cell data.Nature communications · 2024Article
- Promoters adopt distinct dynamic manifestations depending on transcription factor context.Molecular systems biology · 2021Article
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Authors and funding
6 authors.
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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.
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