Skip to main content
Article thumbnail
Location of Repository

Gene network reconstruction from transcriptional dynamics under kinetic model uncertainty: a case for the second derivative

By David R. Bickel, Zahra Montazeri, Pei-Chun Hsieh, Mary Beatty, Shai J. Lawit and Nicholas J. Bate

Abstract

Motivation: Measurements of gene expression over time enable the reconstruction of transcriptional networks. However, Bayesian networks and many other current reconstruction methods rely on assumptions that conflict with the differential equations that describe transcriptional kinetics. Practical approximations of kinetic models would enable inferring causal relationships between genes from expression data of microarray, tag-based and conventional platforms, but conclusions are sensitive to the assumptions made

Topics: Original Papers
Publisher: Oxford University Press
OAI identifier: oai:pubmedcentral.nih.gov:2654806
Provided by: PubMed Central
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://www.pubmedcentral.nih.g... (external link)
  • Suggested articles

    Citations

    1. (2006). Inference of gene regulatory networks and compound mode of action from time course gene expression profiles.
    2. (2004). Degrees of differential gene expression: detecting biologically significant expression differences and estimating their magnitudes.
    3. (2005). Probabilities of spurious connections in gene networks: application to expression time series.
    4. (2006). The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo.
    5. (2007). A predictive model for transcriptional control of physiology in a free living cell.
    6. (2006). Objective Bayesian variable selection.
    7. (2008). Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network.
    8. (1999). Modeling gene expression with differential equations.
    9. (2004). Model uncertainty.
    10. (2001). Comment on ‘Statistical Modeling:TheTwo Cultures’(Leo Breiman).
    11. deHoon,M.J.L.etal.(2002)Inferringgeneregulatorynetworksfromtime-orderedgene expression data using differential equations.
    12. (2005). Comparison of computational methods for the identification of cell cycle-regulated genes.
    13. (2000). Using Bayesian networks to analyze expression data.
    14. (2005). Genome-wide estimation of transcript concentrations from spotted cDNA microarray data.
    15. (2007). Use of short representative sequences for structural and functional genomic studies.
    16. (2005). Reverse-engineering transcription control networks.
    17. (2003). Inferring genetic networks and identifying compound mode of action via expression profiling.
    18. (1966). Signal Detection Theory and Psychophysics.J o h n
    19. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
    20. (2002). The druggable genome.
    21. (2006). Serial analysis of gene expression.
    22. (2003). Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks.
    23. (1948). Theory of Probability. 2nd edn.
    24. (2001). Bayesian Networks and Decision Graphs.
    25. (2007). Bayesian variable selection and data integration for biological regulatory networks.
    26. (2004). Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli by using network component analysis.
    27. (1995). A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion.
    28. (2003). Inferring gene networks from time series microarray data using dynamic Bayesian networks.
    29. (2007). Inferring cellular networks - A review.
    30. (2000). Causality.
    31. (2004). The selective values of alleles in a molecular network model are context dependent.
    32. (2003). Selecting differentially expressed genes from microarray experiments.
    33. (2005). Causal protein-signaling networks derived from multiparameter single-cell data.
    34. (2007). Computer systems and methods for associating genes with traits using cross species data. United States Patent
    35. (2005). empirical Bayes approach to inferring large-scale gene association networks.
    36. (1978). Estimating the dimension of a model.
    37. (2003). Identifying genes altered by a drug in temporal microarray data: a case study.
    38. (1998). Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.
    39. (2003). Modeling networks of molecular interactions in the living cell: structure, dynamics, and applications.
    40. (2006). Stochastic Modelling for Systems Biology.
    41. (2002). Local identifiability: when can genetic networks be identified from microarray data?

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.