title: Bayesian inference of reaction kinetics from single-cell recordings across a heterogeneous cell population.
abstract: Single-cell experimental techniques provide informative data to help uncover dynamical processes inside a cell. Making full use of such data requires dedicated computational methods to estimate biophysical process parameters and states in a model-based manner. In particular, the treatment of heterogeneity or cell-to-cell variability deserves special attention. The present article provides an introduction to one particular class of algorithms which employ marginalization in order to take heterogeneity into account. An overview of alternative approaches is provided for comparison. We treat two frequently encountered scenarios in single-cell experiments, namely, single-cell trajectory data and single-cell distribution data.
authors.affiliation: Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt, Germany.
authors.forename: L
authors.initials: L
authors.lastname: Bronstein
authors.affiliation: Department of Biosystems Sciences and Engineering, ETH Zürich, Basel, Switzerland.
authors.forename: C
authors.initials: C
authors.lastname: Zechner
authors.affiliation: Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt, Germany. Electronic address: heinz.koeppl@bcs.tu-darmstadt.de.
authors.forename: H
authors.initials: H
authors.lastname: Koeppl
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002468:Cell Physiological Phenomena / Q000502:physiology*; D006801:Humans; D007700:Kinetics; D008390:Markov Chains; D059010:Single-Cell Analysis / Q000379:methods* / Q000639:trends; D049490:Systems Biology / Q000379:methods / Q000639:trends
keywords: Bayesian inference; Cell-to-cell variability; Stochastic models