Abstract
A key goal of systems biology is to elucidate molecular mechanisms associated with physiologic and pathologic phenotypes based on the systematic and genome-wide understanding of cell context-specific molecular interaction models. To this end, reverse engineering approaches have been used to systematically dissect regulatory interactions in a specific tissue, based on the availability of large molecular profile datasets, thus improving our mechanistic understanding of complex diseases, such as cancer. In this paper, we introduce high-order Algorithm for the Reconstruction of Accurate Cellular Network (hARACNe), an extension of the ARACNe algorithm for the dissection of transcriptional regulatory networks. ARACNe uses the data processing inequality (DPI), from information theory, to detect and prune indirect interactions that are unlikely to be mediated by an actual physical interaction. Whereas ARACNe considers only first-order indirect interactions, i.e. those mediated by only one extra regulator, hARACNe considers a generalized form of indirect interactions via two, three or more other regulators. We show that use of higher-order DPI resulted in significantly improved performance, based on transcription factor (TF)-specific ChIP-chip data, as well as on gene expression profile following RNAi-mediated TF silencing.
Highlights
Cellular phenotypes are determined by a complex web of physical interactions between gene products [1]
Our analysis shows that hARACNe can systematically eliminate false positive interactions that were missed by DPI1 logic of ARACNe, significantly improving inferred
[16] gene expression profile (GEP) from human B-cell lymphoma, including normal samples from naive, memory and germinal centre B cells isolated from human tonsils and patient derived tumour samples, including diffused large B-cell lymphoma (DLBCL), follicular lymphoma and chronic lymphocytic leukaemia (CLL)
Summary
Cellular phenotypes are determined by a complex web of physical interactions between gene products [1] Modelling these relationships helps to organize the list of parts encoded in the genome into functional genetic networks, a crucial step towards the understanding of mechanisms contributing to normal cell physiology as well as of their dysregulation in disease. While the caveats associated with the use of gene expression data for transcriptional network inference have been well documented and studied throughout the past decade [11], a preponderance of studies and high-impact discoveries in systems biology have established such approaches as widely accepted tools in a systems biologist’s arsenal [6,12,13,14,15,16,17,18].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
