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Understanding melanopsin using bayesian generative models – an Introduction

Benedikt V. Ehinger, Dennis Eickelbeck, Katharina Spoida, Stefan Herlitze, Peter König
doi: https://doi.org/10.1101/043273
Benedikt V. Ehinger
1Institute of Cognitive Science, University of Osnabrück, Albrechtstr. 28, 49076 Osnabrück, Germany
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Dennis Eickelbeck
2Department of General Zoology and Neurobiology, ND7/31, Ruhr-University Bochum, Universitätsstr. 150, D-44780 Bochum, Germany
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Katharina Spoida
2Department of General Zoology and Neurobiology, ND7/31, Ruhr-University Bochum, Universitätsstr. 150, D-44780 Bochum, Germany
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Stefan Herlitze
2Department of General Zoology and Neurobiology, ND7/31, Ruhr-University Bochum, Universitätsstr. 150, D-44780 Bochum, Germany
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Peter König
1Institute of Cognitive Science, University of Osnabrück, Albrechtstr. 28, 49076 Osnabrück, Germany
3Dept. of Neurophysiology and Pathophysiology, University Medical Center Hamburg Eppendorf, 20246 Hamburg, Germany
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1. Abstract

Understanding biological processes implies a quantitative description. In recent years a new tool set, Bayesian hierarchical modeling, has seen rapid development. We use these methods to model kinetics of a specific protein in a neuroscience context: melanopsin. Melanopsin is a photoactive protein in retinal ganglion cells. Due to its photoactivity, melanopsin is widely used in optogenetic experiments and an important component in the elucidation of neuronal interactions. Thus it is important to understand the relevant processes and develop mechanistic models. Here, with a focus on methodological aspects, we develop, implement, fit and discuss Bayesian generative models of melanopsin dynamics.

We start with a sketch of a basic model and then translate it into formal probabilistic language. As melanopsin occurs in at least two states, a resting and a firing state, a basic model is defined by a non-stationary two state hidden Markov process. Subsequently we add complexities in the form of (1) a hierarchical extension to fit multiple cells; (2) a wavelength dependency, to investigate the response at different color of light stimulation; (3) an additional third state to investigate whether melanopsin is bi‐ or tri-stable; (4) differences between different sub-types of melanopsin as found in different species. This application of modeling melanopsin dynamics demonstrates several benefits of Bayesian methods. They directly model uncertainty of parameters, are flexible in the distributions and relations of parameters in the modeling, and allow including prior knowledge, for example parameter values based on biochemical data.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 11, 2016.
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Understanding melanopsin using bayesian generative models – an Introduction
Benedikt V. Ehinger, Dennis Eickelbeck, Katharina Spoida, Stefan Herlitze, Peter König
bioRxiv 043273; doi: https://doi.org/10.1101/043273
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Understanding melanopsin using bayesian generative models – an Introduction
Benedikt V. Ehinger, Dennis Eickelbeck, Katharina Spoida, Stefan Herlitze, Peter König
bioRxiv 043273; doi: https://doi.org/10.1101/043273

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