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Scalable inference using PMCMC and parallel tempering for high-throughput measurements of biomolecular reaction networks

Bronstein, L. and Koeppl, H. (2016):
Scalable inference using PMCMC and parallel tempering for high-throughput measurements of biomolecular reaction networks.
In: 55th IEEE Conference on Decision and Control, Las Vegas, December 2016, [Online-Edition: http://ieeexplore.ieee.org/document/7798361/#full-text-secti...],
[Conference or Workshop Item]

Abstract

Inferring quantities of interest from fluorescence microscopy time-lapse measurements of cells is a key step in parameterizing models of biomolecular reaction networks, and also in comparing different models. In this article, we propose a method which performs inference in continuous-time Markov chain models and thus takes into account the discrete nature of molecule counts. It targets the important situation of inference from many measured cells. Our method, a complement to a recently proposed approach, is based on particle Markov chain Monte Carlo and can be argued to have improved scaling behavior as the number of measured cells increases. We numerically demonstrate the performance of our algorithm on simulated data.

Item Type: Conference or Workshop Item
Erschienen: 2016
Creators: Bronstein, L. and Koeppl, H.
Title: Scalable inference using PMCMC and parallel tempering for high-throughput measurements of biomolecular reaction networks
Language: English
Abstract:

Inferring quantities of interest from fluorescence microscopy time-lapse measurements of cells is a key step in parameterizing models of biomolecular reaction networks, and also in comparing different models. In this article, we propose a method which performs inference in continuous-time Markov chain models and thus takes into account the discrete nature of molecule counts. It targets the important situation of inference from many measured cells. Our method, a complement to a recently proposed approach, is based on particle Markov chain Monte Carlo and can be argued to have improved scaling behavior as the number of measured cells increases. We numerically demonstrate the performance of our algorithm on simulated data.

Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
18 Department of Electrical Engineering and Information Technology
Event Title: 55th IEEE Conference on Decision and Control
Event Location: Las Vegas
Event Dates: December 2016
Date Deposited: 02 Sep 2016 06:19
Official URL: http://ieeexplore.ieee.org/document/7798361/#full-text-secti...
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