Reference : Linear system identification from ensemble snapshot observations
E-prints/Working papers : Already available on another site
Life sciences : Genetics & genetic processes
Physical, chemical, mathematical & earth Sciences : Mathematics
Systems Biomedicine
http://hdl.handle.net/10993/39109
Linear system identification from ensemble snapshot observations
English
Aalto, Atte mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Goncalves, Jorge mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
2019
6
No
[en] Developments in transcriptomics techniques have caused a large demand in tailored computational methods for modelling gene expression dynamics from experimental data. Recently, so-called single-cell experiments have revolutionised genetic studies. These experiments yield gene expression data
in single cell resolution for a large number of cells at a time. However, the cells are destroyed in the measurement process, and so the data consist of snapshots of an ensemble evolving over time, instead of time series. The problem studied in this article is how such data can be used in modelling gene regulatory dynamics. Two different paradigms are studied for linear system identification. The first is based on tracking the evolution of the distribution of cells over time. The second is based on the so-called pseudotime concept, identifying a common trajectory through the state space, along which cells propagate with different rates. Therefore, at any given time, the population contains cells in different stages of the trajectory. Resulting methods are compared in numerical experiments.
European Commission - EC ; Fonds National de la Recherche - FnR ; University of Luxembourg - UL
CropClock, PPPD, OptBioSys
http://hdl.handle.net/10993/39109
https://arxiv.org/abs/1903.06540
FP7 ; 321567 - ERASYSAPP - ERASysAPP - Systems Biology Applications
FnR ; FNR8888477 > Jorge Gonçalves > CropClock > Increasing crops biomass by uncovering the circadian clock network using dynamical models > 01/01/2015 > 30/06/2018 > 2014

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