Bayesian Learning from Marginal Data in Bionetwork Models : Statistical Applications in Genetics and Molecular Biology

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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.


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1544-6115
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Bayesian Learning from Marginal Data in Bionetwork Models

Fernando V. Bonassi1 / Lingchong You2 / Mike West3

1Duke University

2Duke University

3Duke University

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 10, Issue 1, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: 10.2202/1544-6115.1684, October 2011

Publication History

Published Online:
2011-10-27

In studies of dynamic molecular networks in systems biology, experiments are increasingly exploiting technologies such as flow cytometry to generate data on marginal distributions of a few network nodes at snapshots in time. For example, levels of intracellular expression of a few genes, or cell surface protein markers, can be assayed at a series of interim time points and assumed steady-states under experimentally stimulated growth conditions in small cellular systems. Such marginal data on a small number of cellular markers will typically carry very limited information on the parameters and structure of dynamic network models, though experiments will typically be designed to expose variation in cellular phenotypes that are inherently related to some aspects of model parametrization and structure. Our work addresses statistical questions of how to integrate such data with dynamic stochastic models in order to properly quantify the information—or lack of information—it carries relative to models assumed. We present a Bayesian computational strategy coupled with a novel approach to summarizing and numerically characterizing biological phenotypes that are represented in terms of the resulting sample distributions of cellular markers. We build on Bayesian simulation methods and mixture modeling to define the approach to linking mechanistic mathematical models of network dynamics to snapshot data, using a toggle switch example integrating simulated and real data as context.

Keywords: approximate Bayesian computation (ABC); biological signatures; dynamic stochastic network models; flow cytometry data; posterior simulation; synthetic gene circuit; systems biology; toggle switch model

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[1]
D. J. Nott, Y. Fan, L. Marshall, and S. A. Sisson
Journal of Computational and Graphical Statistics, 2014, Volume 23, Number 1, Page 65
[2]
Yanan Fan, David J. Nott, and Scott A. Sisson
Stat, 2013, Volume 2, Number 1, Page 34

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