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Bayesian Learning from Marginal Data in Bionetwork Models

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  • Bonassi Fernando V.
  • You Lingchong
  • West Mike

Abstract

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.

Suggested Citation

  • Bonassi Fernando V. & You Lingchong & West Mike, 2011. "Bayesian Learning from Marginal Data in Bionetwork Models," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-27, October.
  • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:49
    DOI: 10.2202/1544-6115.1684
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    References listed on IDEAS

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    1. Mark Hallen & Bochong Li & Yu Tanouchi & Cheemeng Tan & Mike West & Lingchong You, 2011. "Computation of Steady-State Probability Distributions in Stochastic Models of Cellular Networks," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-16, October.
    2. Lucas Joseph & Carvalho Carlos & West Mike, 2009. "A Bayesian Analysis Strategy for Cross-Study Translation of Gene Expression Biomarkers," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-28, February.
    3. Nunes Matthew A & Balding David J, 2010. "On Optimal Selection of Summary Statistics for Approximate Bayesian Computation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-16, September.
    4. Timothy S. Gardner & Charles R. Cantor & James J. Collins, 2000. "Construction of a genetic toggle switch in Escherichia coli," Nature, Nature, vol. 403(6767), pages 339-342, January.
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    Cited by:

    1. Espen Bernton & Pierre E. Jacob & Mathieu Gerber & Christian P. Robert, 2019. "Approximate Bayesian computation with the Wasserstein distance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 235-269, April.
    2. Artémis Llamosi & Andres M Gonzalez-Vargas & Cristian Versari & Eugenio Cinquemani & Giancarlo Ferrari-Trecate & Pascal Hersen & Gregory Batt, 2016. "What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-18, February.
    3. Buzbas, Erkan O. & Rosenberg, Noah A., 2015. "AABC: Approximate approximate Bayesian computation for inference in population-genetic models," Theoretical Population Biology, Elsevier, vol. 99(C), pages 31-42.

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