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Moment-based inference predicts bimodality in transient gene expression.

Zechner, C. and Ruess, J. and Krenn, P. and Pelet, S. and Peter, M. and Lygeros, J. and Koeppl, H. (2012):
Moment-based inference predicts bimodality in transient gene expression.
In: Proceedings of the National Academy of Sciences of the United States of America, pp. 8340-8345, 109, (21), [Online-Edition: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3361437/?tool=pm...],
[Article]

Abstract

Recent computational studies indicate that the molecular noise of a cellular process may be a rich source of information about process dynamics and parameters. However, accessing this source requires stochastic models that are usually difficult to analyze. Therefore, parameter estimation for stochastic systems using distribution measurements, as provided for instance by flow cytometry, currently remains limited to very small and simple systems. Here we propose a new method that makes use of low-order moments of the measured distribution and thereby keeps the essential parts of the provided information, while still staying applicable to systems of realistic size. We demonstrate how cell-to-cell variability can be incorporated into the analysis obviating the need for the ubiquitous assumption that the measurements stem from a homogeneous cell population. We demonstrate the method for a simple example of gene expression using synthetic data generated by stochastic simulation. Subsequently, we use time-lapsed flow cytometry data for the osmo-stress induced transcriptional response in budding yeast to calibrate a stochastic model, which is then used as a basis for predictions. Our results show that measurements of the mean and the variance can be enough to determine the model parameters, even if the measured distributions are not well-characterized by low-order moments only--e.g., if they are bimodal.

Item Type: Article
Erschienen: 2012
Creators: Zechner, C. and Ruess, J. and Krenn, P. and Pelet, S. and Peter, M. and Lygeros, J. and Koeppl, H.
Title: Moment-based inference predicts bimodality in transient gene expression.
Language: English
Abstract:

Recent computational studies indicate that the molecular noise of a cellular process may be a rich source of information about process dynamics and parameters. However, accessing this source requires stochastic models that are usually difficult to analyze. Therefore, parameter estimation for stochastic systems using distribution measurements, as provided for instance by flow cytometry, currently remains limited to very small and simple systems. Here we propose a new method that makes use of low-order moments of the measured distribution and thereby keeps the essential parts of the provided information, while still staying applicable to systems of realistic size. We demonstrate how cell-to-cell variability can be incorporated into the analysis obviating the need for the ubiquitous assumption that the measurements stem from a homogeneous cell population. We demonstrate the method for a simple example of gene expression using synthetic data generated by stochastic simulation. Subsequently, we use time-lapsed flow cytometry data for the osmo-stress induced transcriptional response in budding yeast to calibrate a stochastic model, which is then used as a basis for predictions. Our results show that measurements of the mean and the variance can be enough to determine the model parameters, even if the measured distributions are not well-characterized by low-order moments only--e.g., if they are bimodal.

Journal or Publication Title: Proceedings of the National Academy of Sciences of the United States of America
Volume: 109
Number: 21
Uncontrolled Keywords: Computer Simulation, Flow Cytometry, Fungal,Fungal: physiology,Gene Expression Regulation, Genetic, Glycerol, Glycerol: metabolism, Mitogen-Activated Protein Kinases, Mitogen-Activated Protein Kinases: genetics,Models,Physiological,Physiological: genetics,Saccharomyces cerevisiae,Saccharomyces cerevisiae Proteins,Saccharomyces cerevisiae Proteins: genetics,Saccharomyces cerevisiae: genetics,Signal Transduction,Signal Transduction: genetics,Stochastic Processes,Stress,Water-Electrolyte Balance,Water-Electrolyte Balance: genetics
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
Date Deposited: 04 Apr 2014 11:41
Official URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3361437/?tool=pm...
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