Format

Send to

Choose Destination
Cell Syst. 2016 Nov 23;3(5):480-490.e13. doi: 10.1016/j.cels.2016.11.001.

Analysis of Cell Lineage Trees by Exact Bayesian Inference Identifies Negative Autoregulation of Nanog in Mouse Embryonic Stem Cells.

Author information

1
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.
2
Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland.
3
Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.
4
Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.
5
Department of Microbiology, Oslo University Hospital, 0450 Oslo, Norway.
6
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.
7
Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany. Electronic address: carsten.marr@helmholtz-muenchen.de.
8
Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland. Electronic address: manfred.claassen@imsb.biol.ethz.ch.

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.

KEYWORDS:

Bayesian inference; autoregulation; lineage trees; model selection; mouse embryonic stem cells; nanog; parameter inference; particle filtering; state space inference; stochastic modeling

PMID:
27883891
DOI:
10.1016/j.cels.2016.11.001
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center