Fast and efficient dynamic nested effects models
TL;DR: A computationally attractive extension of NEMs is introduced that enables the analysis of perturbation time series data, hence allowing to discriminate between direct and indirect signaling and to resolve feedback loops.
read more
Abstract: Motivation: Targeted interventions in combination with the measurement of secondary effects can be used to computationally reverse engineer features of upstream non-transcriptional signaling cascades. Nested effect models (NEMs) have been introduced as a statistical approach to estimate the upstream signal flow from downstream nested subset structure of perturbation effects. The method was substantially extended later on by several authors and successfully applied to various datasets. The connection of NEMs to Bayesian Networks and factor graph models has been highlighted.
Results: Here, we introduce a computationally attractive extension of NEMs that enables the analysis of perturbation time series data, hence allowing to discriminate between direct and indirect signaling and to resolve feedback loops.
Availability: The implementation (R and C) is part of the Supplement to this article.
Contact: frohlich@bit.uni-bonn.de
Supplementary information: Supplementary data are available at Bioinformatics online.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
biRte: Bayesian inference of context-specific regulator activities and transcriptional networks.
TL;DR: Frohlich et al. as mentioned in this paper proposed a method to predict the influence of regulators (transcription factors, miRNAs) on gene expression by combining Bayesian inference of regulator activities with network reverse engineering.
Reconstructing evolving signalling networks by hidden Markov nested effects models
TL;DR: In this paper, hidden Markov nested effects models (HM-NEMs) are proposed to model the evolving network by a Markov chain on a state space of signalling networks, which are derived from NEMs of indirect perturbation data.
15
Improved pathway reconstruction from RNA interference screens by exploiting off-target effects
Sumana Srivatsa,Sumana Srivatsa,Jack Kuipers,Jack Kuipers,Fabian Schmich,Fabian Schmich,Simone Eicher,Mario Emmenlauer,Christoph Dehio,Niko Beerenwinkel,Niko Beerenwinkel +10 more
TL;DR: This work presents an extension of NEMs called probabilistic combinatorial nested effects models (pc‐NEMs), which capitalize on the ancillary siRNA off‐target effects for network reconstruction from combinatorsial gene knockdown data.
12
Modeling of dynamic systems with Petri nets and fuzzy logic
Lukas Windhager
- 19 Apr 2013
TL;DR: The Petri Netz and Fuzzy Logik (PNFL) Ansatz erlaubt eine naturlichsprachlich-basierte Beschreibung of biologischen Entitaten sowie eine Wenn-Dann-Regel-Basierte Definition of Reaktionen.
10
References
Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans
Andrew Fire,SiQun Xu,Mary K. Montgomery,Steven A. Kostas,Steven A. Kostas,Samuel E. Driver,Craig C. Mello +6 more
TL;DR: To their surprise, it was found that double-stranded RNA was substantially more effective at producing interference than was either strand individually, arguing against stochiometric interference with endogenous mRNA and suggesting that there could be a catalytic or amplification component in the interference process.
16.7K
Causality: models, reasoning, and inference
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
14.9K
Causality: Models, Reasoning and Inference
Abstract: 1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5. Causality and structural models in the social sciences 6. Simpson's paradox, confounding, and collapsibility 7. Structural and counterfactual models 8. Imperfect experiments: bounds and counterfactuals 9. Probability of causation: interpretation and identification Epilogue: the art and science of cause and effect.
5.3K
An introduction to systems biology : design principles of biological circuits
Uri Alon
- 07 Jul 2006
TL;DR: The Robustness Principle can Distinguish Between Mechanisms of Fruit Fly Patterning and Kinetic Proofreading of the Genetic Code can reduce Error Rates of Molecular Recognition Recognition recognition.
Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
Karen Sachs,Karen Sachs,Karen Sachs,Omar D. Perez,Omar D. Perez,Omar D. Perez,Dana Pe'er,Dana Pe'er,Dana Pe'er,Douglas A. Lauffenburger,Douglas A. Lauffenburger,Douglas A. Lauffenburger,Garry P. Nolan,Garry P. Nolan,Garry P. Nolan +14 more
TL;DR: Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.
2.1K