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Bioinformatics. 2015 Feb 1;31(3):355-62. doi: 10.1093/bioinformatics/btu619. Epub 2014 Sep 30.

Inference of protein kinetics by stochastic modeling and simulation of fluorescence recovery after photobleaching experiments.

Author information

1
Department of Biology, School of Medicine, University of Patras, 26505, Rio, Patras, Greece, Institut für Automatik, ETH Zürich, 8092 Zürich, Switzerland and INRIA Grenoble-Rhône-Alpes, Montbonnot, 38334 Saint-Ismier Cedex, France Department of Biology, School of Medicine, University of Patras, 26505, Rio, Patras, Greece, Institut für Automatik, ETH Zürich, 8092 Zürich, Switzerland and INRIA Grenoble-Rhône-Alpes, Montbonnot, 38334 Saint-Ismier Cedex, France.
2
Department of Biology, School of Medicine, University of Patras, 26505, Rio, Patras, Greece, Institut für Automatik, ETH Zürich, 8092 Zürich, Switzerland and INRIA Grenoble-Rhône-Alpes, Montbonnot, 38334 Saint-Ismier Cedex, France.

Abstract

MOTIVATION:

Fluorescence recovery after photobleaching (FRAP) is a functional live cell imaging technique that permits the exploration of protein dynamics in living cells. To extract kinetic parameters from FRAP data, a number of analytical models have been developed. Simplifications are inherent in these models, which may lead to inexhaustive or inaccurate exploitation of the experimental data. An appealing alternative is offered by the simulation of biological processes in realistic environments at a particle level. However, inference of kinetic parameters using simulation-based models is still limited.

RESULTS:

We introduce and demonstrate a new method for the inference of kinetic parameter values from FRAP data. A small number of in silico FRAP experiments is used to construct a mapping from FRAP recovery curves to the parameters of the underlying protein kinetics. Parameter estimates from experimental data can then be computed by applying the mapping to the observed recovery curves. A bootstrap process is used to investigate identifiability of the physical parameters and determine confidence regions for their estimates. Our method circumvents the computational burden of seeking the best-fitting parameters via iterative simulation. After validation on synthetic data, the method is applied to the analysis of the nuclear proteins Cdt1, PCNA and GFPnls. Parameter estimation results from several experimental samples are in accordance with previous findings, but also allow us to discuss identifiability issues as well as cell-to-cell variability of the protein kinetics.

IMPLEMENTATION:

All methods were implemented in MATLAB R2011b. Monte Carlo simulations were run on the HPC cluster Brutus of ETH Zurich.

CONTACT:

lygeros@control.ee.ethz.ch or lygerou@med.upatras.gr

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
25273108
DOI:
10.1093/bioinformatics/btu619
[Indexed for MEDLINE]

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