Inference of transition probabilities between the attractors in Boolean networks with perturbation

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    This paper investigates the inference of Boolean networks with perturbation (BNp) from simulated data and observed data. We interpret the discretised gene expression levels as attractor states of the underlying network and use the sequence of attractor states to determine the model. We consider the case where a complete sequence of attractors is known and the case where the known attractor states are arrived at by sampling from an underlying sequence of attractors. We apply the resulting algorithm to the interferon regulatory network using gene expression data taken from murine bone-derived macrophage cells infected with cytomegalovirus.
    LanguageEnglish
    Title of host publicationUnknown Host Publication
    Number of pages4
    DOIs
    Publication statusPublished - 2009
    EventGenomic Signal Processing and Statistics, 2009. GENSIPS 2009. IEEE International Workshop -
    Duration: 1 Jan 2009 → …

    Conference

    ConferenceGenomic Signal Processing and Statistics, 2009. GENSIPS 2009. IEEE International Workshop
    Period1/01/09 → …

    Fingerprint

    Gene expression
    Interferons
    Macrophages
    Bone
    Sampling

    Keywords

    • logic
    • boolean network with perturbation
    • bnp

    Cite this

    @inproceedings{845c806bb8c8403eb9239aae016e6507,
    title = "Inference of transition probabilities between the attractors in Boolean networks with perturbation",
    abstract = "This paper investigates the inference of Boolean networks with perturbation (BNp) from simulated data and observed data. We interpret the discretised gene expression levels as attractor states of the underlying network and use the sequence of attractor states to determine the model. We consider the case where a complete sequence of attractors is known and the case where the known attractor states are arrived at by sampling from an underlying sequence of attractors. We apply the resulting algorithm to the interferon regulatory network using gene expression data taken from murine bone-derived macrophage cells infected with cytomegalovirus.",
    keywords = "logic, boolean network with perturbation, bnp",
    author = "Steven Watterson",
    year = "2009",
    doi = "10.1109/GENSIPS.2009.5174376",
    language = "English",
    booktitle = "Unknown Host Publication",

    }

    Watterson, S 2009, Inference of transition probabilities between the attractors in Boolean networks with perturbation. in Unknown Host Publication. Genomic Signal Processing and Statistics, 2009. GENSIPS 2009. IEEE International Workshop, 1/01/09. https://doi.org/10.1109/GENSIPS.2009.5174376

    Inference of transition probabilities between the attractors in Boolean networks with perturbation. / Watterson, Steven.

    Unknown Host Publication. 2009.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    AB - This paper investigates the inference of Boolean networks with perturbation (BNp) from simulated data and observed data. We interpret the discretised gene expression levels as attractor states of the underlying network and use the sequence of attractor states to determine the model. We consider the case where a complete sequence of attractors is known and the case where the known attractor states are arrived at by sampling from an underlying sequence of attractors. We apply the resulting algorithm to the interferon regulatory network using gene expression data taken from murine bone-derived macrophage cells infected with cytomegalovirus.

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