## The Factor Graph Network Model for Biological Systems (2005)

Venue: | Proc. of RECOMB 2005 |

Citations: | 5 - 1 self |

### BibTeX

@INPROCEEDINGS{Gat-viks05thefactor,

author = {Irit Gat-viks and Amos Tanay and Daniela Raijman and Ron Shamir},

title = {The Factor Graph Network Model for Biological Systems},

booktitle = {Proc. of RECOMB 2005},

year = {2005},

pages = {31--47},

publisher = {Springer}

}

### OpenURL

### Abstract

Abstract. 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 hyperosmotic shock, and explore uncharacterized logical relations between important regulators in the system. 1

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Citation Context ...the steady state model to the dynamic model. Another possible extension is the consideration of other classes of regulation functions (for example, we can consider continuous or ranked function as in =-=[26, 22, 12, 16]-=-).sFig. 1. An overview of the factor graph network model. A) Knowledge on the logical regulation functions is formalized as conditional probabilities. B) Continuous measurements and logical states are... |

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Citation Context ...itivity, by slowing down the production of some central components in it. A second group of discrepancies involves genes that are targets of the Hog1 downstream regulators Sko1, Hot1, Msn1 and Msn2,4 =-=[19, 21, 20]-=-. In many cases, the literature does not specify the logical relations among the regulators and each of their regulatees, and this lack of knowledge is manifested as discrepancies. We thus used our mo... |

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Citation Context ...nalysis that predicts the system’s behavior in various conditions. Most importantly, our framework allows the learning of a refined model with improved fit to the experimental data. In previous works =-=[25, 8]-=- we have introduced the notions of model refinement and expansion and studied it when applied to discrete deterministic models. Here we study these problems in the more general settings of probabilist... |

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