Edinburgh Research Explorer

Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3

Research output: Chapter in Book/Report/Conference proceedingChapter

Original languageEnglish
Title of host publicationGene Regulatory Networks: Methods and Protocols
EditorsGuido Sanguinetti, Vân Anh Huynh-Thu
Place of PublicationNew York, NY
PublisherSpringer New York LLC
Pages217-233
Number of pages17
ISBN (Electronic)978-1-4939-8882-2
ISBN (Print)978-1-4939-8881-5
DOIs
Publication statusPublished - 2019

Abstract

Inference of gene regulatory networks (GRNs) from time series data is a well-established field in computational systems biology. Most approaches can be broadly divided in two families: model-based and model-free methods. These two families are highly complementary: model-based methods seek to identify a formal mathematical model of the system. They thus have transparent and interpretable semantics but rely on strong assumptions and are rather computationally intensive. On the other hand, model-free methods have typically good scalability. Since they are not based on any parametric model, they are more flexible than model-based methods, but also less interpretable.

In this chapter, we describe Jump3, a hybrid approach that bridges the gap between model-free and model-based methods. Jump3 uses a formal stochastic differential equation to model each gene expression but reconstructs the GRN topology with a nonparametric method based on decision trees. We briefly review the theoretical and algorithmic foundations of Jump3, and then proceed to provide a step-by-step tutorial of the associated software usage.

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