Reconstructing static and dynamic models of signaling pathways using Modular Response Analysis

Santra, Tapesh; Rukhlenko, Oleksii S.; Zhernovkov, Vadim; Kholodenko, Boris N.

doi:10.1016/j.coisb.2018.02.003

Cited by 25 publications

(35 citation statements)

References 36 publications

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“…MRA modeling (Kholodenko et al, 2002;Santra et al, 2018) is a widely used technique to learn the coupling between the modules of a biological system from perturbation data sets (Fig. 1).…”

Section: Discussionmentioning

confidence: 99%

“…Depending on the application, this framework can be applied to different molecular species and processes, e.g., protein or metabolite concentrations, protein phosphorylation levels, etc. (Santra et al, 2018). Now, in full generality, we assume that there are modules whose activities are given by ∈ ℝ .…”

Section: Mathematical Derivationmentioning

confidence: 99%

“…It relies on the perturbation of individual components, the so-called modules. Various developments of MRA and related methods were recently reviewed (Santra et al, 2018) but, despite its success, MRA is not accessible through a standard software package to the best of our knowledge. We thus reasoned that it would help the community to implement an R library (aiMeRA) that everyone could use without specific skills.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

aiMeRA: A generic modular response analysis R package and its application to estrogen and retinoic acid receptors crosstalk

Jiménez-Domínguez

Ravel

Jalaguier

et al. 2020

Preprint

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“…MRA modeling (Kholodenko et al, 2002;Santra et al, 2018) is a widely used technique to learn the coupling between the modules of a biological system from perturbation data sets (Fig. 1).…”

Section: Discussionmentioning

confidence: 99%

Section: Mathematical Derivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

aiMeRA: A generic modular response analysis R package and its application to estrogen and retinoic acid receptors crosstalk

Jiménez-Domínguez

Ravel

Jalaguier

et al. 2020

Preprint

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show abstract

“…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%

Identifiability and experimental design in perturbation studies

Groß

Blüthgen

2020

Preprint

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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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“…Modular response analysis (MRA) has used perturbation data to infer networks and dynamics [7,3]. A recent review describing the development of this technique can be found in [9]. This method relies on using perturbation data to reconstruct the Jacobian matrix from which the modular structure of the network can be inferred.…”

Section: Introductionmentioning

confidence: 99%

Inferring Reaction Networks using Perturbation Data

Choi¹,

Hellerstein

Wiley

et al. 2018

Preprint

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In this paper we examine the use of perturbation data to infer the underlying mechanistic dynamic model. The approach uses an evolutionary strategy to evolve networks based on a fitness criterion that measures the difference between the experimentally determined set of perturbation data and proposed mechanistic models. At present we only deal with reaction networks that use mass-action kinetics employing uni-uni, bi-uni, uni-bi and bi-bi reactions. The key to our approach is to split the algorithm into two phases. The first phase focuses on evolving network topologies that are consistent with the perturbation data followed by a second phase that evolves the parameter values. This results in almost an exact match between the evolved network and the original network from which the perturbation data was generated from. We test the approach on four models that include linear chain, feed-forward loop, cyclic pathway and a branched pathway. Currently the algorithm is implemented using Python and libRoadRunner but could at a later date be rewritten in a compiled language to improve performance. Future studies will focus on the impact of noise in the perturbation data on convergence and variability in the evolved parameter values and topologies. In addition we will investigate the effect of nonlinear rate laws on generating unique solutions.

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Reconstructing static and dynamic models of signaling pathways using Modular Response Analysis

Cited by 25 publications

References 36 publications

aiMeRA: A generic modular response analysis R package and its application to estrogen and retinoic acid receptors crosstalk

aiMeRA: A generic modular response analysis R package and its application to estrogen and retinoic acid receptors crosstalk

Identifiability and experimental design in perturbation studies

Inferring Reaction Networks using Perturbation Data

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