ArticleScientific reports2018
Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Testing and overcoming the limitations of modular response analysis.Briefings in bioinformatics · 2025Article
- Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets.Cancers · 2024Article
- Predicting anti-cancer drug combination responses with a temporal cell state network model.PLoS computational biology · 2023Article
- Modular response analysis reformulated as a multilinear regression problem.Bioinformatics (Oxford, England) · 2023Article
- Network inference from perturbation time course data.NPJ systems biology and applications · 2022Article
- Control of cell state transitions.Nature · 2022Article
- Data-driven network models for genetic circuits from time-series data with incomplete measurements.Journal of the Royal Society, Interface · 2021Article
- Nested information processing in the living world.Annals of the New York Academy of Sciences · 2021Review
- A systematic analysis of signaling reactivation and drug resistance.Cell reports · 2021Article
- An R package for generic modular response analysis and its application to estrogen and retinoic acid receptor crosstalk.Scientific reports · 2021Article
- Mapping connections in signaling networks with ambiguous modularity.NPJ systems biology and applications · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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