Modelling signalling networks from perturbation data

Dorel, Mathurin; Klinger, Bertram; Groß, Torsten; Sieber, Anja; Prahallad, Anirudh; Bosdriesz, Evert; Wessels, Lodewyk; Blüthgen, Nils

doi:10.1093/bioinformatics/bty473

Cited by 33 publications

(38 citation statements)

References 24 publications

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“…The package relies on standard maximum flow algorithms from the Networkx package (Hagberg et al, 2008). Technically, it would be possible to cope with nonidentifiabilities numerically, as was done previously (Gardner et al, 2003;Bonneau et al, 2006;Tegner et al, 2003;Dorel et al, 2018) or even through the analysis of example networks. For the latter, we could set unknown parameters to random values and numerically compute the according ranks in the identifiability conditions Equation 9 and Equation 10.…”

Section: Discussionmentioning

confidence: 99%

“…They are continuously improved, e.g. to reduce the effect of noise, incorporate heterogeneous data sets, or allow for the analysis of single cell data (Greenfield et al, 2013;Santra et al, 2018;Klinger and Blüthgen, 2018;Santra et al, 2013;Kang et al, 2015;Dorel et al, 2018) and have thus become a standard research tool. Nevertheless, identifiability (Hengl et al, 2007;Godfrey and DiStefano, 1985) of the inferred network parameters within a specific perturbation setup has not yet been rigorously analysed, even though a limited number of practically feasible perturbations renders many systems underdetermined (De Smet and Marchal, 2010;Meinshausen et al, 2016;Bonneau et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Identifiability and experimental design in perturbation studies

Groß

Blüthgen

2020

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Motivation:A common strategy to infer and quantify interactions between components of a biological system is to deduce them from the network's response to targeted perturbations. Such perturbation experiments are often challenging and costly. Therefore, optimising the experimental design is essential to achieve a meaningful characterisation of biological networks. However, it remains difficult to predict which combination of perturbations allows to infer specific interaction strengths in a given network topology. Yet, such a description of identifiability is necessary to select perturbations that maximize the number of inferable parameters. Results: We show analytically that the identifiability of network parameters can be determined by an intuitive maximum flow problem. Furthermore, we used the theory of matroids to describe identifiability relationships between sets of parameters in order to build identifiable effective network models. Collectively, these results allowed to device strategies for an optimal design of the perturbation experiments. We benchmarked these strategies on a database of human pathways. Remarkably, full network identifiability was achieved with on average less than a third of the perturbations that are needed in a random experimental design. Moreover, we determined perturbation combinations that additionally decreased experimental effort compared to single-target perturbations. In summary, we provide a framework that allows to infer a maximal number of interaction strengths with a minimal number of perturbation experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifiability and experimental design in perturbation studies

Groß

Blüthgen

2020

Preprint

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“…The developed model-based approach for systems identification has several advantages over Modular Response Analysis and similar approaches 24–26,53–55 . In contrast to standard network inference method, the model-based approach describes the biochemical mechanisms of the identified network interactions and can be used to analyse the dynamics of the system and its control.…”

Section: Discussionmentioning

confidence: 99%

Model-based identification of the crosstalks and feedbacks that determine the doxorubicin response dynamics of the JNK-p38-p53 network

Tuffery

Halász

Fey

2020

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Cellular responses to perturbations and drugs are determined by interconnected networks, rather than linear pathways. Individually, the JNK, p38 and p53 stress and DNA-damage response networks are well understood and regulate critical cell-fate decisions, such as apoptosis, in response to many chemotherapeutical agents, such as doxorubicin. To better understand how interactions between these pathways determine the dynamic behaviour of the entire network, we constructed a data-driven mathematical model. This model contains mechanistic details about the kinase cascades that activate JNK, p38, AKT and p53, and free parameters that describe possible interactions between these pathways. Fitting this model to experimental time-course perturbation data (five time-courses with six time-points under five different conditions), identified specific network interactions that can explain the observed network responses. JNK emerged as an important control node. JNK exhibited a positive feedback loop, was tightly controlled by negative feedback and crosstalk from p38 and AKT, respectively, and was the strongest activator of p53. Compared to static network reconstruction methods, such as modular response analysis, the model-based approach identifies biochemical mechanisms and explains the dynamic control of cell signalling.

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“…For example, phosphoprotein measurements with perturbations (Klinger et al, 2013;Dorel et al, 2018;Morris et al, 2011Morris et al, , 2010 can be used to fit transition rates, the same way as it was done with simulated data in Fig. 7.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, if quantitative data is available for a model's variables or states it is possible with ExaS-toLog to perform parameter fitting of the model's transition rates. If a model's nodes are proteins, quantitative phosphoprotein data is an ideal data type, often used in another semi-mechanistic modeling approach, modular response analysis (Dorel et al, 2018;Klinger et al, 2013). As described above, the stationary solutions are complex rational functions (ratios of polynomials) of transition rates (Fig.…”

Section: Exastolog Toolbox: Calculation Of Solutions Visualization Amentioning

confidence: 99%

Exact calculation of stationary solution and parameter sensitivity analysis of stochastic continuous time Boolean models

Koltai

Noël

Zinovyev

et al. 2019

Preprint

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Motivation: Solutions to stochastic Boolean models are usually estimated by Monte Carlo simulations, but as the state space of these models can be enormous, there is an inherent uncertainty about the accuracy of Monte Carlo estimates and whether simulations have reached all asymptotic solutions. Moreover, these models have timescale parameters (transition rates) that the probability values of stationary solutions depend on in complex ways that have not been analyzed yet in the literature. These two fundamental uncertainties call for an exact calculation method for this class of models. Results: We show that the stationary probability values of the attractors of stochastic (asynchronous) continuous time Boolean models can be exactly calculated. The calculation does not require Monte Carlo simulations, instead it uses an exact matrix calculation method previously applied in the context of chemical kinetics. Using this approach, we also analyze the under-explored question of the effect of transition rates on the stationary solutions and show the latter can be sensitive to parameter changes. The analysis distinguishes processes that are robust or, alternatively, sensitive to parameter values, providing both methodological and biological insights.

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Modelling signalling networks from perturbation data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 33 publications

References 24 publications

Identifiability and experimental design in perturbation studies

Identifiability and experimental design in perturbation studies

Model-based identification of the crosstalks and feedbacks that determine the doxorubicin response dynamics of the JNK-p38-p53 network

Exact calculation of stationary solution and parameter sensitivity analysis of stochastic continuous time Boolean models

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