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Inference in hierarchical transcriptional network motifs

Inference in hierarchical transcriptional network motifs

This video was recorded at 4th International Workshop on Machine Learning in Systems Biology (MLSB), Edinburgh 2010. 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.
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