title: Analysis of Cell Lineage Trees by Exact Bayesian Inference Identifies Negative Autoregulation of Nanog in Mouse Embryonic Stem Cells.
abstract: Many cellular effectors of pluripotency are dynamically regulated. In principle, regulatory mechanisms can be inferred from single-cell observations of effector activity across time. However, rigorous inference techniques suitable for noisy, incomplete, and heterogeneous data are lacking. Here, we introduce stochastic inference on lineage trees (STILT), an algorithm capable of identifying stochastic models that accurately describe the quantitative behavior of cell fate markers observed using time-lapse microscopy data collected from proliferating cell populations. STILT performs exact Bayesian parameter inference and stochastic model selection using a particle-filter-based algorithm. We use STILT to investigate the autoregulation of Nanog, a heterogeneously expressed core pluripotency factor, in mouse embryonic stem cells. STILT rejects the possibility of positive Nanog autoregulation with high confidence; instead, model predictions indicate weak negative feedback. We use STILT for rational experimental design and validate model predictions using novel experimental data. STILT is available for download as an open source framework from http://www.imsb.ethz.ch/research/claassen/Software/stilt---stochastic-inference-on-lineage-trees.html.
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland; Department of Mathematics, Technische Universität München, 85748 Garching, Germany.
authors.forename: Justin
authors.initials: J
authors.lastname: Feigelman
authors.affiliation: Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland.
authors.forename: Stefan
authors.initials: S
authors.lastname: Ganscha
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.
authors.forename: Simon
authors.initials: S
authors.lastname: Hastreiter
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Schwarzfischer
authors.affiliation: Department of Microbiology, Oslo University Hospital, 0450 Oslo, Norway.
authors.forename: Adam
authors.initials: A
authors.lastname: Filipczyk
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.
authors.forename: Timm
authors.initials: T
authors.lastname: Schroeder
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Department of Mathematics, Technische Universität München, 85748 Garching, Germany.
authors.forename: Fabian J
authors.initials: FJ
authors.lastname: Theis
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany. Electronic address: carsten.marr@helmholtz-muenchen.de.
authors.forename: Carsten
authors.initials: C
authors.lastname: Marr
authors.affiliation: Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland. Electronic address: manfred.claassen@imsb.biol.ethz.ch.
authors.forename: Manfred
authors.initials: M
authors.lastname: Claassen
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002454:Cell Differentiation; D019070:Cell Lineage; D018398:Homeodomain Proteins; D006706:Homeostasis; D051379:Mice; D008954:Models, Biological; D000066450:Mouse Embryonic Stem Cells; D000071317:Nanog Homeobox Protein
chemical_list: D018398:Homeodomain Proteins; D000071317:Nanog Homeobox Protein
keywords: Bayesian inference; autoregulation; lineage trees; model selection; mouse embryonic stem cells; nanog; parameter inference; particle filtering; state space inference; stochastic modeling