Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Statistical Applications in Genetics and Molecular Biology

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

6 Issues per year

IMPACT FACTOR 2016: 0.646
5-year IMPACT FACTOR: 1.191

CiteScore 2016: 0.94

SCImago Journal Rank (SJR) 2016: 0.625
Source Normalized Impact per Paper (SNIP) 2016: 0.596

Mathematical Citation Quotient (MCQ) 2016: 0.06

See all formats and pricing
More options …
Volume 10, Issue 1 (Oct 2011)


Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

Bayesian Learning from Marginal Data in Bionetwork Models

Fernando V. Bonassi / Lingchong You / Mike West
Published Online: 2011-10-27 | DOI: https://doi.org/10.2202/1544-6115.1684

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

About the article

Published Online: 2011-10-27

Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.2202/1544-6115.1684.

Export Citation

©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston. Copyright Clearance Center

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Erkan O. Buzbas and Noah A. Rosenberg
Theoretical Population Biology, 2015, Volume 99, Page 31
Bochong Li and Lingchong You
Biophysical Journal, 2014, Volume 107, Number 5, Page 1247
Artémis Llamosi, Andres M. Gonzalez-Vargas, Cristian Versari, Eugenio Cinquemani, Giancarlo Ferrari-Trecate, Pascal Hersen, Gregory Batt, and Jorg Stelling
PLOS Computational Biology, 2016, Volume 12, Number 2, Page e1004706
D. J. Nott, Y. Fan, L. Marshall, and S. A. Sisson
Journal of Computational and Graphical Statistics, 2014, Volume 23, Number 1, Page 65
Yanan Fan, David J. Nott, and Scott A. Sisson
Stat, 2013, Volume 2, Number 1, Page 34

Comments (0)

Please log in or register to comment.
Log in