|
Irit Gat-Viks, Amos Tanay, Daniela Raijman and Ron Shamir |
Journal of Computational Biology 2006 Mar;13(2):165-81
|
Biological systems are traditionaly studied by focusing on specific subsystems, building intuitive models on regulatory relations in them and refining them using results from carefully designed experiments. Modern experimental techniques provide massive data on the global behavior of biological systems, but current methods are not systematically using these large datasets for refining existing knowledge.Here we introduce an extended computational framework that combines formalization of existing qualitative models 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 study the performance of several probabilistic inference algorithms and show that hidden model variables can be reliably inferred even in the presence of feedback loops and complex logic. We show how to refine prior knowledge on combinatorial regulatory relations using hypothesis testing, and 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. |
|
for comments or questions: iritg@post.tau.ac.il
