DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models

PloS One
Alex GreenfieldRichard Bonneau

Abstract

Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge. We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test-based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively) that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations. Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-...Continue Reading

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Methods Mentioned

BETA
gene knock-out
genetic knock-out
single-gene knock-out
gene knock-outs

Related Concepts

Likelihood Functions
Gene Modules
Experimental Design
Genes
Genome
Learning
Evaluation
Gene Knockout Techniques
Inference
Drug Interactions

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