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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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
