A Bayesian approach to modelling heterogeneous calcium responses in cell populations

Tilunaite, Agne; Croft, Wayne; Russell, Noah A.; Bellamy, Tomas C.; Thul, Ruediger

Abstract

Calcium responses have been observed as spikes of the whole-cell calcium concentration in numerous cell types and are essential for translating extracellular stimuli into cellular responses. While there are several suggestions for how this encoding is achieved, we still lack a comprehensive theory. To achieve this goal it is necessary to reliably predict the temporal evolution of calcium spike sequences for a given stimulus. Here, we propose a modelling framework that allows us to quantitatively describe the timing of calcium spikes. Using a Bayesian approach, we show that Gaussian processes model calcium spike rates with high fidelity and perform better than standard tools such as peri-stimulus time histograms and kernel smoothing. We employ our modelling concept to analyse calcium spike sequences from dynamically-stimulated HEK293T cells. Under these conditions, different cells often experience diverse stimuli time courses, which is a situation likely to occur in vivo. This single cell variability and the concomitant small number of calcium spikes per cell pose a significant modelling challenge, but we demonstrate that Gaussian processes can successfully describe calcium spike rates in these circumstances. Our results therefore pave the way towards a statistical description of heterogeneous calcium oscillations in a dynamic environment

Citation

Tilunaite, A., Croft, W., Russell, N. A., Bellamy, T. C., & Thul, R. (2017). A Bayesian approach to modelling heterogeneous calcium responses in cell populations. PLoS Computational Biology, 13(10), Article e1005794. https://doi.org/10.1371/journal.pcbi.1005794

• Journal Article Type: Article
• Acceptance Date: Sep 27, 2017
• Publication Date: Oct 6, 2017
• Deposit Date: Oct 20, 2017
• Publicly Available Date: Oct 20, 2017
• Journal: PLoS Computational Biology
• Print ISSN: 1553-734X
• Electronic ISSN: 1553-7358
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 10
• Article Number: e1005794.
• DOI: https://doi.org/10.1371/journal.pcbi.1005794
• Public URL: https://nottingham-repository.worktribe.com/output/886690
• Publisher URL: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005794
• Contract Date: Oct 20, 2017