Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction

Title: Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction
Authors: Thomaseth, CaterinaFey, DirkSantra, TapeshRukhlenko, Oleksii S.Radde, Nicole E.Kholodenko, Boris N.
Permanent link: http://hdl.handle.net/10197/11037
Date: 1-Nov-2018
Online since: 2019-08-26T13:57:22Z
Abstract: Modular Response Analysis (MRA) is a method to reconstruct signalling networks from steady-state perturbation data which has frequently been used in different settings. Since these data are usually noisy due to multi-step measurement procedures and biological variability, it is important to investigate the effect of this noise onto network reconstruction. Here we present a systematic study to investigate propagation of noise from concentration measurements to network structures. Therefore, we design an in silico study of the MAPK and the p53 signalling pathways with realistic noise settings. We make use of statistical concepts and measures to evaluate accuracy and precision of individual inferred interactions and resulting network structures. Our results allow to derive clear recommendations to optimize the performance of MRA based network reconstruction: First, large perturbations are favorable in terms of accuracy even for models with non-linear steady-state response curves. Second, a single control measurement for different perturbation experiments seems to be sufficient for network reconstruction, and third, we recommend to execute the MRA workflow with the mean of different replicates for concentration measurements rather than using computationally more involved regression strategies.
Funding Details: European Commission Horizon 2020
Type of material: Journal Article
Publisher: Springer Nature
Journal: Scientific Reports
Volume: 8
Issue: Article number 16217
Copyright (published version): 2018 The Authors
Keywords: BioengineeringGeneric Health RelevanceModular Response Analysis (MRA)
DOI: 10.1038/s41598-018-34353-3
Language: en
Status of Item: Peer reviewed
Appears in Collections:Medicine Research Collection

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