---
res:
  bibo_abstract:
  - Understanding and characterising biochemical processes inside single cells requires
    experimental platforms that allow one to perturb and observe the dynamics of such
    processes as well as computational methods to build and parameterise models from
    the collected data. Recent progress with experimental platforms and optogenetics
    has made it possible to expose each cell in an experiment to an individualised
    input and automatically record cellular responses over days with fine time resolution.
    However, methods to infer parameters of stochastic kinetic models from single-cell
    longitudinal data have generally been developed under the assumption that experimental
    data is sparse and that responses of cells to at most a few different input perturbations
    can be observed. Here, we investigate and compare different approaches for calculating
    parameter likelihoods of single-cell longitudinal data based on approximations
    of the chemical master equation (CME) with a particular focus on coupling the
    linear noise approximation (LNA) or moment closure methods to a Kalman filter.
    We show that, as long as cells are measured sufficiently frequently, coupling
    the LNA to a Kalman filter allows one to accurately approximate likelihoods and
    to infer model parameters from data even in cases where the LNA provides poor
    approximations of the CME. Furthermore, the computational cost of filtering-based
    iterative likelihood evaluation scales advantageously in the number of measurement
    times and different input perturbations and is thus ideally suited for data obtained
    from modern experimental platforms. To demonstrate the practical usefulness of
    these results, we perform an experiment in which single cells, equipped with an
    optogenetic gene expression system, are exposed to various different light-input
    sequences and measured at several hundred time points and use parameter inference
    based on iterative likelihood evaluation to parameterise a stochastic model of
    the system.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Anđela
      foaf_name: Davidović, Anđela
      foaf_surname: Davidović
  - foaf_Person:
      foaf_givenName: Remy P
      foaf_name: Chait, Remy P
      foaf_surname: Chait
      foaf_workInfoHomepage: http://www.librecat.org/personId=3464AE84-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0003-0876-3187
  - foaf_Person:
      foaf_givenName: Gregory
      foaf_name: Batt, Gregory
      foaf_surname: Batt
  - foaf_Person:
      foaf_givenName: Jakob
      foaf_name: Ruess, Jakob
      foaf_surname: Ruess
      foaf_workInfoHomepage: http://www.librecat.org/personId=4A245D00-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0003-1615-3282
  bibo_doi: 10.1371/journal.pcbi.1009950
  bibo_issue: '3'
  bibo_volume: 18
  dct_date: 2022^xs_gYear
  dct_identifier:
  - UT:001044208400004
  dct_isPartOf:
  - http://id.crossref.org/issn/1553-734X
  - http://id.crossref.org/issn/1553-7358
  dct_language: eng
  dct_publisher: Public Library of Science@
  dct_title: Parameter inference for stochastic biochemical models from perturbation
    experiments parallelised at the single cell level@
  fabio_hasPubmedId: '35303737'
...