SignaLink 2 – a signaling pathway resource with multi-layered regulatory networks

Fazekas, Dávid; Koltai, Mihály; Türei, Dénes; Módos, Dezső; Pálfy, Máté; Dúl, Zoltán; Zsákai, Lilian; Szalay-Bekő, Máté; Lenti, Katalin; Farkas, Illés J.; Vellai, Tibor; Csermely, Péter; Korcsmáros, Tamás

doi:10.1186/1752-0509-7-7

Cited by 184 publications

(177 citation statements)

References 73 publications

(76 reference statements)

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“…These causal models can be derived purely from experimental measurements, using so-called data-driven reverse engineering (or network inference) methods (40). Causal models can also be obtained by training a network of putative links to data [based on prior knowledge, which is often available for signaling pathways through resources such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) (44), Pathway Commons (45), or SignaLink (46)], typically experiments that combine stimuli and inhibitors in various ways to parameterize the system.…”

Section: Causal Modelingmentioning

confidence: 99%

Modeling Signaling Networks to Advance New Cancer Therapies

Sáez-Rodríguez

MacNamara

Cook

2015

Annu. Rev. Biomed. Eng.

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Cell signaling pathways control cells' responses to their environment through an intricate network of proteins and small molecules partitioned by intracellular structures, such as the cytoskeleton and nucleus. Our understanding of these pathways has been revised recently with the advent of more advanced experimental techniques; no longer are signaling pathways viewed as linear cascades of information flowing from membrane-bound receptors to the nucleus. Instead, such pathways must be understood in the context of networks, and studying such networks requires an integration of computational and experimental approaches. This understanding is becoming more important in designing novel therapies for diseases such as cancer. Using the MAPK (mitogen-activated protein kinase) and PI3K (class I phosphoinositide-3' kinase) pathways as case studies of cellular signaling, we give an overview of these pathways and their functions. We then describe, using a number of case studies, how computational modeling has aided in understanding these pathways' deregulation in cancer, and how such understanding can be used to optimally tailor current therapies or help design new therapies against cancer.

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Section: Causal Modelingmentioning

confidence: 99%

Modeling Signaling Networks to Advance New Cancer Therapies

Sáez-Rodríguez

MacNamara

Cook

2015

Annu. Rev. Biomed. Eng.

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“…Interactions that model gene suppression or other types of negative connections have been omitted from the PKN. Before using the FIN other networks were considered including the Human Signaling Network, 55 Signalink, 56 and the network by Kirouac et al 57 The FIN was chosen because it offered the best coverage of the transcription factors for which there is an available regulon, while being the sparsest of all, facilitating the performance of the ILP algorithm.…”

Section: Construction Of Prior Knowledge Network (Pkn)mentioning

confidence: 99%

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

et al. 2015

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Identification of signaling pathways that are functional in a specific biological context is a major challenge in systems biology, and could be instrumental to the study of complex diseases and various aspects of drug discovery. Recent approaches have attempted to combine gene expression data with prior knowledge of protein connectivity in the form of a PPI network, and employ computational methods to identify subsets of the protein-protein-interaction (PPI) network that are functional, based on the data at hand. However, the use of undirected networks limits the mechanistic insight that can be drawn, since it does not allow for following mechanistically signal transduction from one node to the next. To address this important issue, we used a directed, signaling network as a scaffold to represent protein connectivity, and implemented an Integer Linear Programming (ILP) formulation to model the rules of signal transduction from one node to the next in the network. We then optimized the structure of the network to best fit the gene expression data at hand. We illustrated the utility of ILP modeling with a case study of drug induced lung injury. We identified the modes of action of 200 lung toxic drugs based on their gene expression profiles and, subsequently, merged the drug specific pathways to construct a signaling network that captured the mechanisms underlying Drug Induced Lung Disease (DILD). We further demonstrated the predictive power and biological relevance of the DILD network by applying it to identify drugs with relevant pharmacological mechanisms for treating lung injury. Insight, innovation, integrationIn this manuscript we introduce a novel approach for the identification of signaling pathways that are functional in a specific biological context, by leveraging gene expression data and prior knowledge of protein connectivity. In more detail, we introduce a linear programming formulation to model signal transduction from one node to the next in a Prior Knowledge Network (PKN), and by minimizing the mismatch between model predictions and experimental data, we are able to identify subsets of the PKN that are most probably functional in the specific biological context. More specifically, we address the problem of identifying the modes of action of drugs that have been reported to induce respiratory side effects, based on their gene expression profiles, and subsequently, merge the drug specific pathways together to construct a signaling network that captures the signaling mechanisms underlying Drug Induced Lung Disease (DILD). Moreover, to demonstrate the predictive power and biological relevance of the DILD network, we use it to suggest potential drug repositioning for treating lung injury.

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“…We constructed this GRN as follows: First, we retrieved a reliable human GRN from the study by Gerstein et al 20 and SignaLink version 2 21 . Second, we compiled lists of DEG groups for each time point and for each direction of transcriptional change (up-regulation and down-regulation).…”

Section: Construction Of the Integrated Obesity-associated Grnmentioning

confidence: 99%