The factor graph network model for biological systems

Irit Gat-Viks*, Amos Tanay, Daniela Raijman, Ron Shamir

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


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 learn 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 elements. 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 p-values for learned model features. We test our methodology 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 uncharacterized logical relations between important regulators in the system.

Original languageEnglish
Pages (from-to)31-47
Number of pages17
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
StatePublished - 2005
Event9th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2005 - Cambridge, MA, United States
Duration: 14 May 200518 May 2005


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