Abstract
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 language | English |
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| Title of host publication | Machine Learning in Systems Biology, Proceedings of the Fourth International Workshop of |
| Pages | 47-50 |
| Number of pages | 4 |
| Publication status | Published - 2010 |