Bayesian learning from marginal data in bionetwork models.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Bonassi, FV; You, L; West, M

Published Date

  • October 27, 2011

Published In

Volume / Issue

  • 10 / 1

PubMed ID

  • 23089812

Pubmed Central ID

  • 23089812

Electronic International Standard Serial Number (EISSN)

  • 1544-6115

International Standard Serial Number (ISSN)

  • 2194-6302

Digital Object Identifier (DOI)

  • 10.2202/1544-6115.1684

Language

  • eng