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    di Bernardo Lab - Systems and Synthetic Biology Lab
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    Time Series Network Identification TSNI

    The manuscript describing TSNI can be downloaded here:

    • Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Mukesh Bansal, Giusy Della Gatta, Diego di Bernardo. Bioinformatics -- September 2006 -- 22(7), p. 815-822 Abstract
    • Direct targets of the Trp63 transcription factor revealed by a combination of gene expression profiling and reverse engineering. Giusy Della Gatta, Mukesh Bansal, Alberto Ambesi-Impiombato, Dario Antonini, Caterina Missero, Diego di Bernardo. Genome Research, 2008 abstractPDF.


    TSNI matlab code can be downloaded from here.

    Below the command-line to run TSNI and a short description of its arguments:

    [A,B,total_states] = tsni(data,time_points,perturbation,principle_components)

    data: no_genes X time_points format, in which first time points is scaled to zero.
    time_points: Time points at which measurement are done. It is a row vector.
    perturbation: It is a row vector and it is an input to the system. Size is 1 X no_time_points.
    principle_components: Number of principle componets to use. If you dont know how many principle components to use, put it to 0.
    A: Recovered network. It is a fully connected network.
    B: Targets of perturbations. It is the score of each gene to be the target of perturbation.
    total_states: Number of principle components used.
    % There is a parameter in code which is smoothing parameter. Default % value is 0.8. It can be changed to see how the profiles of the gene % changes. It shouldn't be too low.

    This software is distributed only for non-commercial purposes and only for acedemic use - Commercial users please contact Diego di Bernardo (dibernardoATtigem.it)