Linear system identifiability from single-cell data

Atte Aalto, Francois Lamolin, Jorge Goncalves

Research output: Contribution to journalArticleResearchpeer-review

Abstract

In this brief article, we prove a result on identifiability of a discrete-time linear system from measurements of the mean and the covariance of the state distribution, obtained from population snapshot observations over time. We show that three time points of such measurements are sufficient for unique identifiability. This is in stark contrast to identifiability from time series data, in which case n + 1 measurements are required for identifiability of an n-dimensional system. Robustness of the identifiability is investigated by numerical experiments. The work is motivated by the problem of gene regulatory network inference from single-cell data and will serve as a foundation for the development of modelling algorithms.
Original languageEnglish
Article number105287
JournalSystems and Control Letters
Volume165
DOIs
Publication statusPublished - Jul 2022

Keywords

  • System identifiability
  • Systems biology
  • Single-cell data
  • Population snapshot
  • Gene regulatory networks
  • Linear stochastic systems

Fingerprint

Dive into the research topics of 'Linear system identifiability from single-cell data'. Together they form a unique fingerprint.

Cite this