A guide to gene regulatory network inference for obtaining predictive solutions: Underlying assumptions and fundamental biological and data constraints. (December 2018)

• Record Type: Journal Article
• Authors: Barbosa, Sara; Niebel, Bastian; Wolf, Sebastian; Mauch, Klaus; Takors, Ralf
• Is Part Of: Bio systems. Volume 174(2018)
• Page Start: 37
• Page End: 48
• Publication Date: 2018-12
• Subjects: Gene Regulatory Network Inference -- Regression -- Information theory -- Bayesian networks -- Boolean networks -- Neural networks; Biological systems -- Periodicals; Biology -- Periodicals; Biology -- Periodicals; Evolution -- Periodicals; Biologie -- Périodiques; Évolution -- Périodiques; 570
• DOI: 10.1016/j.biosystems.2018.10.008
• Languages: English
• ISSNs: 0303-2647

Abstract

Abstract: The study of biological systems at a system level has become a reality due to the increasing powerful computational approaches able to handle increasingly larger datasets. Uncovering the dynamic nature of gene regulatory networks in order to attain a system level understanding and improve the predictive power of biological models is an important research field in systems biology. The task itself presents several challenges, since the problem is of combinatorial nature and highly depends on several biological constraints and also the intended application. Given the intrinsic interdisciplinary nature of gene regulatory network inference, we present a review on the currently available approaches, their challenges and limitations. We propose guidelines to select the most appropriate method considering the underlying assumptions and fundamental biological and data constraints.