The factor graph network model for biological systems

We introduce an extended computational framework for studying
biological systems. Our approach combines formalization of
existing qualitative models that are in wide but informal use
today, with probabilistic modeling and integration of high
throughput experimental data. Using our methods, it is possible
to interpret genomewide measurements in the context of prior
knowledge on the system, to assign statistical meaning to the
accuracy of such knowledge and to perform model expansion - the
learning of refined models with improved fit to the experiments.
Our model is represented as a probabilistic factor graph and the
framework accommodates partial measurements of diverse biological
factors. We develop methods for inference and learning in the model.
We compare the performance of standard inference algorithms and
tailor-made ones and show that hidden variables can be reliably
inferred even in the presence of feedback loops and complex logic.
We develop a formulation for the learning problem in our model which
is based on deterministic hypothesis testing, and show how to derive
pvalues for learned model features. We experiment with our model
and algorithms on both simulated and real yeast data. In particular,
we use our method to study the response of S. cerevisiae to hyper-osmotic
shock, and explore uncharacterizd logical relations between important
regulators in the system.

Joint work with Amos Tanay, Daniella raijman, Ron Shamir.