Inference in hierarchical transcriptional network motifs

Andrea Ocone, Guido Sanguinetti

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


We present a novel inference methodology to reverse engineer the dynamics of transcription factors (TFs) in hierarchical network motifs such as feed-forward loops. The approach we present is based on a continuous time representation of the system where the high level master TF is represented as a two state Markov jump process driving a system of differential equations. We present an approximate variational inference algorithm and show promising preliminary results on a realistic simulated data set.
Original languageEnglish
Title of host publicationMachine Learning in Systems Biology, Proceedings of the Fourth International Workshop of
Number of pages4
Publication statusPublished - 2010


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