Computational discovery of dynamic cell line specific Boolean networks from multiplex time-course data

Razzaq, Misbah; Paulevé, Loı̈c; Siegel, Anne; Sáez-Rodríguez, Julio; Bourdon, Jérémie; Guziolowski, Carito

doi:10.1371/journal.pcbi.1006538

Cited by 27 publications

(18 citation statements)

References 43 publications

(50 reference statements)

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“…Major problems might occur due to robustness, or saturation effects, all of which disrupt the presumed flow of signal but are an essential part of various biological systems (Fritsche-Guenther et al , 2011). Also, from a Boolean perspective, the response logic treats nodes exclusively as OR gates, whereas certain systems require a more involved logic (Razzaq et al , 2018). But again, the declarative nature of the ASP encoding allows to account for such effects.…”

Section: Discussionmentioning

confidence: 99%

Robust network inference using response logic

Groß

Wongchenko²,

Yan³

et al. 2019

Bioinformatics

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Motivation A major challenge in molecular and cellular biology is to map out the regulatory networks of cells. As regulatory interactions can typically not be directly observed experimentally, various computational methods have been proposed to disentangling direct and indirect effects. Most of these rely on assumptions that are rarely met or cannot be adapted to a given context. Results We present a network inference method that is based on a simple response logic with minimal presumptions. It requires that we can experimentally observe whether or not some of the system’s components respond to perturbations of some other components, and then identifies the directed networks that most accurately account for the observed propagation of the signal. To cope with the intractable number of possible networks, we developed a logic programming approach that can infer networks of hundreds of nodes, while being robust to noisy, heterogeneous or missing data. This allows to directly integrate prior network knowledge and additional constraints such as sparsity. We systematically benchmark our method on KEGG pathways, and show that it outperforms existing approaches in DREAM3 and DREAM4 challenges. Applied to a novel perturbation dataset on PI3K and MAPK pathways in isogenic models of a colon cancer cell line, it generates plausible network hypotheses that explain distinct sensitivities toward various targeted inhibitors due to different PI3K mutants. Availability and implementation A Python/Answer Set Programming implementation can be accessed at github.com/GrossTor/response-logic. Data and analysis scripts are available at github.com/GrossTor/response-logic-projects. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

Robust network inference using response logic

Groß

Wongchenko²,

Yan³

et al. 2019

Bioinformatics

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“…Major problems might occur due to robustness, or saturation effects, all of which disrupt the presumed flow of signal but are an essential part of various biological systems [15]. Also, from a boolean perspective the response logic treats nodes exclusively as OR gates, whereas certain systems require a more involved logic [36]. Another important shortcoming for many questions is that it does not assign any weights nor signs to the inferred links.…”

Section: Discussionmentioning

confidence: 99%

Robust network inference using response logic

Groß

Wongchenko²,

Yan³

et al. 2019

Preprint

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Motivation:A major challenge in molecular and cellular biology is to map out the regulatory networks of cells. As regulatory interactions can typically not be directly observed experimentally, various computational methods have been proposed to disentangling direct and indirect effects. Most of these rely on assumptions that are rarely met or cannot be adapted to a given context. Results:We present a network inference method that is based on a simple response logic with minimal presumptions. It requires that we can experimentally observe whether or not some of the system's components respond to perturbations of some other components, and then identifies the directed networks that most accurately account for the observed propagation of the signal. To cope with the intractable number of possible networks, we developed a logic programming approach that can infer networks of hundreds of nodes, while being robust to noisy, heterogeneous or missing data. This allows to directly integrate prior network knowledge and additional constraints such as sparsity. We systematically benchmark our method on KEGG pathways, and show that it outperforms existing approaches in DREAM3 and DREAM4-challenges. Applied to a perturbation data set on PI3K and MAPK pathways in isogenic models of a colon cancer cell line, it generates plausible network hypotheses that explain distinct sensitivities towards EGFR inhibitors by different PI3K mutants. Availability and Implementation: A Python/Answer Set Programming implementation can be accessed at github.com/GrossTor/response-logic. Contact: nils.bluethgen@charite.de (Incomplete) transitive Closure Response Pattern Response logic Prior knowledge Logic Program Inference Rectification Union Conforming Networks Figure 1: The steps of the response logic approach. The response logic and all additional prior network knowledge are formulated as a logic program. It is first used to rectify the experimentally determined response pattern, and second, takes the resulting (potentially incomplete) transitive closure as input to infer either all individual conforming networks or the union thereof.work structure was resolved. Furthermore, we noticed that even though most network inference problems are embedded within very well studied contexts, the vast majority of reverse-engineering methods predicts networks de novo. Therefore, we additionally aimed for a method that could readily incorporate prior knowledge about presence or absence of certain links or about other known network properties. This resulted in what we call the response logic approach.In the following, we describe the response logic approach in more detail and then benchmark it by (i) assessing the performance using synthetic data derived from KEGG pathways [24], and (ii) comparing its performance to competing methods using the communitywide inference challenges (DREAM) [38]. Finally, we use the approach to study RAS/MAPK/PI3K signalling in a colon cancer cell line, and predict differences in the signalling network topology due to different PI3...

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“…More specifically, it is important to identify the pathways involved in TAM formation and to understand to what extent the macrophage plasticity facilitates this process inside a tumor. On the other hand, despite the wealth of quantitative information from bulk and single-cell sequencing datasets, the inference of regulatory networks based on experimental data remains a difficult challenge, with most approaches proposing a combination of both literature-and data-driven methods [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies

Marku

Verstraete

Raynal

et al. 2020

Cancers

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The tumour microenvironment is the surrounding of a tumour, including blood vessels, fibroblasts, signaling molecules, the extracellular matrix and immune cells, especially neutrophils and monocyte-derived macrophages. In a tumour setting, macrophages encompass a spectrum between a tumour-suppressive (M1) or tumour-promoting (M2) state. The biology of macrophages found in tumours (Tumour Associated Macrophages) remains unclear, but understanding their impact on tumour progression is highly important. In this paper, we perform a comprehensive analysis of a macrophage polarization network, following two lines of enquiry: (i) we reconstruct the macrophage polarization network based on literature, extending it to include important stimuli in a tumour setting, and (ii) we build a dynamical model able to reproduce macrophage polarization in the presence of different stimuli, including the contact with cancer cells. Our simulations recapitulate the documented macrophage phenotypes and their dependencies on specific receptors and transcription factors, while also unravelling the formation of a special type of tumour associated macrophages in an in vitro model of chronic lymphocytic leukaemia. This model constitutes the first step towards elucidating the cross-talk between immune and cancer cells inside tumours, with the ultimate goal of identifying new therapeutic targets that could control the formation of tumour associated macrophages in patients.

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Computational discovery of dynamic cell line specific Boolean networks from multiplex time-course data

Cited by 27 publications

References 43 publications

Robust network inference using response logic

Robust network inference using response logic

Robust network inference using response logic

Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies

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