Parameter inference for stochastic single-cell dynamics from lineage tree data
| Autor: | Kuzmanovska, Irena, Milias-Argeitis, Andreas, Mikelson, Jan, Zechner, Christoph, Khammash, Mustafa Hani |
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| Přispěvatelé: | Molecular Systems Biology |
| Rok vydání: | 2016 |
| Předmět: |
Stochastic systems
Single cell Monte Carlo methods Parameter inference Cell lineages Methodology Article Computational Biology Models Biological Markov Chains lcsh:Biology (General) MONTE-CARLO Cell Lineage Interatrial Block Single-Cell Analysis BAYESIAN-INFERENCE lcsh:QH301-705.5 Monte Carlo Method GENE-EXPRESSION |
| Zdroj: | BMC Systems Biology BMC Systems Biology, 11(52):52. BMC BMC Systems Biology, Vol 11, Iss 1, Pp 1-13 (2017) BMC Systems Biology, 11 |
| ISSN: | 1752-0509 |
| Popis: | Background: With the advance of experimental techniques such as time-lapse fluorescence microscopy, the availability of single-cell trajectory data has vastly increased, and so has the demand for computational methods suitable for parameter inference with this type of data. Most of currently available methods treat single-cell trajectories independently, ignoring the mother-daughter relationships and the information provided by the population structure. However, this information is essential if a process of interest happens at cell division, or if it evolves slowly compared to the duration of the cell cycle. Results: In this work, we propose a Bayesian framework for parameter inference on single-cell time-lapse data from lineage trees. Our method relies on a combination of Sequential Monte Carlo for approximating the parameter likelihood function and Markov Chain Monte Carlo for parameter exploration. We demonstrate our inference framework on two simple examples in which the lineage tree information is crucial: one in which the cell phenotype can only switch at cell division and another where the cell state fluctuates slowly over timescales that extend well beyond the cell-cycle duration. Conclusion: There exist several examples of biological processes, such as stem cell fate decisions or epigenetically controlled phase variation in bacteria, where the cell ancestry is expected to contain important information about the underlying system dynamics. Parameter inference methods that discard this information are expected to perform poorly for such type of processes. Our method provides a simple and computationally efficient way to take into account single-cell lineage tree data for the purpose of parameter inference and serves as a starting point for the development of more sophisticated and powerful approaches in the future. BMC Systems Biology, 11 ISSN:1752-0509 |
| Databáze: | OpenAIRE |
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