scholarly journals Augmenting Signaling Pathway Reconstructions

2020 ◽  
Author(s):  
Tobias Rubel ◽  
Anna Ritz
Keyword(s):  
Signaling Pathways ◽  
Signaling Pathway ◽  
Protein Interactions ◽  
Cellular Response ◽  
Ground Truth ◽  
Protein Interaction Data ◽  
Reconstruction Method ◽  
Reconstruction Algorithms ◽  
Molecular Systems ◽  
Pathway Reconstruction

AbstractSignaling pathways drive cellular response, and understanding such pathways is fundamental to molecular systems biology. A mounting volume of experimental protein interaction data has motivated the development of algorithms to computationally reconstruct signaling pathways. However, existing methods suffer from low recall in recovering protein interactions in ground truth pathways, limiting our confidence in any new predictions for experimental validation. We present the Pathway Reconstruction AUGmenter (PRAUG), a higher-order function for producing high-quality pathway reconstruction algorithms. PRAUG modifies any existing pathway reconstruction method, resulting in augmented algorithms that outperform their un-augmented counterparts for six different algorithms across twenty-nine diverse signaling pathways. The algorithms produced by PRAUG collectively reveal potential new proteins and interactions involved in the Wnt and Notch signaling pathways. PRAUG offers a valuable framework for signaling pathway prediction and discovery.

scholarly journals Stop Bickering! Reconciling Signaling Pathway Databases with Network Topologies

2021 ◽  
Author(s):  
Tobias Rubel ◽  
Pramesh Singh ◽  
Anna Ritz
Keyword(s):  
Signaling Pathways ◽  
Signaling Pathway ◽  
Cellular Responses ◽  
Topological Characterization ◽  
Molecular Systems ◽  
Structural Differences ◽  
Topological Measures ◽  
Network Topologies ◽  
Connected Subgraphs ◽  
Pathway Databases

A major goal of molecular systems biology is to understand the coordinated function of genes or proteins in response to cellular signals and to understand these dynamics in the context of disease. Signaling pathway databases such as KEGG, NetPath, NCI-PID, and Panther describe the molecular interactions involved in different cellular responses. While the same pathway may be present in different databases, prior work has shown that the particular proteins and interactions differ across database annotations. However, to our knowledge no one has attempted to quantify their structural differences. It is important to characterize artifacts or other biases within pathway databases, which can provide a more informed interpretation for downstream analyses. In this work, we consider signaling pathways as graphs and we use topological measures to study their structure. We find that topological characterization using graphlets (small, connected subgraphs) distinguishes signaling pathways from appropriate null models of interaction networks. Next, we quantify topological similarity across pathway databases. Our analysis reveals that the pathways harbor database-specific characteristics implying that even though these databases describe the same pathways, they tend to be systematically different from one another. We show that pathway-specific topology can be uncovered after accounting for database-specific structure. This work present the first step towards elucidating common pathway structure beyond their specific database annotations.

scholarly journals Integrating Protein Localization with Automated Signaling Pathway Reconstruction

2019 ◽  
Author(s):  
Ibrahim Youssef ◽  
Jeffrey Law ◽  
Anna Ritz
Keyword(s):  
Signal Transduction ◽  
Signaling Pathways ◽  
Protein Interactions ◽  
Cellular Localization ◽  
Protein Localization ◽  
Dynamic Program ◽  
Reconstruction Algorithms ◽  
Ppi Networks ◽  
Cellular Compartments ◽  
Localization Information

AbstractUnderstanding cellular responses via signal transduction is a core focus in systems biology. Tools to automatically reconstruct signaling pathways from protein-protein interactions (PPIs) can help biologists generate testable hypotheses about signaling. However, automatic reconstruction of signaling pathways suffers from many interactions with the same confidence score leading to many equally good candidates. Further, some reconstructions are biologically misleading due to ignoring protein localization information. We proposeLocPL, a method to improve the automatic reconstruction of signaling pathways from PPIs by incorporating information about protein localization in the reconstructions. The method relies on a dynamic program to ensure that the proteins in a reconstruction are localized in cellular compartments that are consistent with signal transduction from the membrane to the nucleus.LocPLand existing reconstruction algorithms are applied to two PPI networks and assessed using both global and local definitions of accuracy.LocPLproduces more accurate and biologically meaningful reconstructions on a versatile set of signaling pathways.LocPLis a powerful tool to automatically reconstruct signaling pathways from PPIs that leverages cellular localization information about proteins. The underlying dynamic program and signaling model are flexible enough to study cellular signaling under different settings of signaling flow across the cellular compartments.

scholarly journals Performance Assessment of the Network Reconstruction Approaches on Various Interactomes

2021 ◽  
Vol 8 ◽  
Author(s):  
M. Kaan Arici ◽  
Nurcan Tuncbag
Keyword(s):  
Protein Interactions ◽  
Structural Information ◽  
Network Reconstruction ◽  
Heat Diffusion ◽  
Reconstruction Algorithms ◽  
Personalized Pagerank ◽  
Cancer Driver ◽  
Pathway Reconstruction ◽  
Main Challenge ◽  
Steiner Forest

Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.

scholarly journals Large tumor suppressor 2, LATS2, activates JNK in a kinase-independent mechanism through ASK1

2018 ◽  
Vol 10 (6) ◽  
pp. 549-558 ◽  
Author(s):  
Lauren Rusnak ◽  
Cong Tang ◽  
Qi Qi ◽  
Xiulei Mo ◽  
Haian Fu
Keyword(s):  
Tumor Suppressor ◽  
Signaling Pathway ◽  
Protein Interactions ◽  
Cellular Response ◽  
Regulatory System ◽  
Mapk Signaling ◽  
Mitogen Activated Protein Kinase ◽  
Hippo Signaling ◽  
Large Tumor ◽  
Large Tumor Suppressor

Abstract Apoptosis signal-regulating kinase 1 (ASK1) is an important mediator of the cell stress response pathways. Because of its central role in regulating cell death, the activity of ASK1 is tightly regulated by protein–protein interactions and post-translational modifications. Deregulation of ASK1 activity has been linked to human diseases, such as neurological disorders and cancer. Here we describe the identification and characterization of large tumor suppressor 2 (LATS2) as a novel binding partner for ASK1. LATS2 is a core kinase in the Hippo signaling pathway and is commonly downregulated in cancer. We found that LATS2 interacts with ASK1 and increases ASK1-mediated signaling to promote apoptosis and activate the JNK mitogen-activated protein kinase (MAPK). This change in MAPK signaling is dependent on the catalytic activity of ASK1 but does not require LATS2 kinase activity. This work identifies a novel role for LATS2 as a positive regulator of the ASK1–MKK–JNK signaling pathway and establishes a kinase-independent function of LATS2 that may be part of the intricate regulatory system for cellular response to diverse stress signals.

scholarly journals Automating parameter selection to avoid implausible biological pathway models

2019 ◽  
Author(s):  
Chris S. Magnano ◽  
Anthony Gitter
Keyword(s):  
Level Structure ◽  
Ground Truth ◽  
Parameter Tuning ◽  
Parameter Selection ◽  
Biological Pathways ◽  
Reconstruction Algorithms ◽  
Direct Evaluation ◽  
Link Type ◽  
Pathway Reconstruction ◽  
Omic Data

AbstractA common way to integrate and analyze large amounts of biological “omic” data is through pathway reconstruction: using condition-specific omic data to create a subnetwork of a generic background network that represents some process or cellular state. A challenge in pathway reconstruction is that adjusting pathway reconstruction algorithms’ parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway reconstruction, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising algorithm to tune pathway reconstruction algorithms to minimize biologically implausible predictions. We leverage background knowledge in pathway databases to select pathways whose high-level structure resembles that of manually curated biological pathways. At the core of this method is a graphlet decomposition metric, which measures topological similarity to curated biological pathways. In order to evaluate pathway parameter advising, we compare its performance in avoiding implausible networks and reconstructing pathways from the NetPath database with other parameter selection methods across four pathway reconstruction algorithms. We also demonstrate how pathway parameter advising can guide construction of an influenza host factor network. Pathway parameter advising is method-agnostic; it is applicable to any pathway reconstruction algorithm with tunable parameters. Our pathway parameter advising software is available on GitHub at https://github.com/gitter-lab/pathway-parameter-advising and PyPI at https://pypi.org/project/pathwayParameterAdvising/.

scholarly journals Automating parameter selection to avoid implausible biological pathway models

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Chris S. Magnano ◽  
Anthony Gitter
Keyword(s):  
Level Structure ◽  
Ground Truth ◽  
Reconstruction Algorithm ◽  
Parameter Tuning ◽  
Parameter Selection ◽  
Biological Pathways ◽  
Reconstruction Algorithms ◽  
Direct Evaluation ◽  
Pathway Reconstruction ◽  
Omic Data

AbstractA common way to integrate and analyze large amounts of biological “omic” data is through pathway reconstruction: using condition-specific omic data to create a subnetwork of a generic background network that represents some process or cellular state. A challenge in pathway reconstruction is that adjusting pathway reconstruction algorithms’ parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway reconstruction, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising algorithm to tune pathway reconstruction algorithms to minimize biologically implausible predictions. We leverage background knowledge in pathway databases to select pathways whose high-level structure resembles that of manually curated biological pathways. At the core of this method is a graphlet decomposition metric, which measures topological similarity to curated biological pathways. In order to evaluate pathway parameter advising, we compare its performance in avoiding implausible networks and reconstructing pathways from the NetPath database with other parameter selection methods across four pathway reconstruction algorithms. We also demonstrate how pathway parameter advising can guide reconstruction of an influenza host factor network. Pathway parameter advising is method agnostic; it is applicable to any pathway reconstruction algorithm with tunable parameters.

scholarly journals Integrating protein localization with automated signaling pathway reconstruction

2019 ◽  
Vol 20 (S16) ◽  
Author(s):  
Ibrahim Youssef ◽  
Jeffrey Law ◽  
Anna Ritz
Keyword(s):  
Signal Transduction ◽  
Signaling Pathways ◽  
Protein Interactions ◽  
Cellular Localization ◽  
Protein Localization ◽  
Dynamic Program ◽  
Reconstruction Algorithms ◽  
Ppi Networks ◽  
Cellular Compartments ◽  
Localization Information

Abstract Background Understanding cellular responses via signal transduction is a core focus in systems biology. Tools to automatically reconstruct signaling pathways from protein-protein interactions (PPIs) can help biologists generate testable hypotheses about signaling. However, automatic reconstruction of signaling pathways suffers from many interactions with the same confidence score leading to many equally good candidates. Further, some reconstructions are biologically misleading due to ignoring protein localization information. Results We propose LocPL, a method to improve the automatic reconstruction of signaling pathways from PPIs by incorporating information about protein localization in the reconstructions. The method relies on a dynamic program to ensure that the proteins in a reconstruction are localized in cellular compartments that are consistent with signal transduction from the membrane to the nucleus. LocPL and existing reconstruction algorithms are applied to two PPI networks and assessed using both global and local definitions of accuracy. LocPL produces more accurate and biologically meaningful reconstructions on a versatile set of signaling pathways. Conclusion LocPL is a powerful tool to automatically reconstruct signaling pathways from PPIs that leverages cellular localization information about proteins. The underlying dynamic program and signaling model are flexible enough to study cellular signaling under different settings of signaling flow across the cellular compartments.

Targeted Delivery of Natural Bioactives and Lipid-nanocargos against Signaling Pathways Involved in Skin Cancer

2020 ◽  
Vol 27 ◽  
Author(s):  
Mohammad Kashif Iqubal ◽  
Aiswarya Chaudhuri ◽  
Ashif Iqubal ◽  
Sadaf Saleem ◽  
Madan Mohan Gupta ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Signaling Pathways ◽  
Signaling Pathway ◽  
Targeted Delivery ◽  
Skin Pigmentation ◽  
Wnt Signaling Pathway ◽  
Human Beings ◽  
Radiation Chemical ◽  
Cell Leukemia Virus Type ◽  
Human T Cell

: At present, skin cancer is a widespread malignancy in human beings. Among diverse population types, Caucasian populations are much more prone in comparison to darker skin populations due to the comparative lack of skin pigmentation. Skin cancer is divided into malignant and non-melanoma skin cancer, which is additionally categorized as basal and squamous cell carcinoma. The exposure to ultraviolet radiation, chemical carcinogen (polycyclic aromatic hydrocarbons, arsenic, tar, etc.), and viruses (herpes virus, human papillomavirus, and human T-cell leukemia virus type-1) are major contributing factors of skin cancer. There are distinct pathways available through which skin cancer develops, such as the JAKSTAT pathway, Akt pathway, MAPKs signaling pathway, Wnt signaling pathway, to name a few. Currently, several targeted treatments are available, such as monoclonal antibodies, which have dramatically changed the line of treatment of this disease but possess major therapeutic limitations. Thus, recently many phytochemicals have been evaluated either alone or in combination with the existing synthetic drugs to overcome their limitations and have found to play a promising role in the prevention and treatment. In this review, complete tracery of skin cancer, starting from the signaling pathways involved, newer developed drugs with their targets and limitations along with the emerging role of natural products alone or in combination as potent anticancer agents and their molecular mechanism involved has been discussed. Apart from this, various nanocargos have also been mentioned here, which can play a significant role in the management and treatment of different types of skin cancer.

Biological role of AKT, and regulation of AKT signaling pathway by thymoquinone: perspectives in cancer therapeutics

2020 ◽  
Vol 20 ◽  
Author(s):  
Md. Junaid ◽  
Yeasmin Akter ◽  
Syeda Samira Afrose ◽  
Mousumi Tania ◽  
Md. Asaduzzaman Khan
Keyword(s):  
Clinical Trials ◽  
Signaling Pathways ◽  
Signaling Pathway ◽  
Inhibitory Effect ◽  
Akt Signaling ◽  
Review Article ◽  
Therapeutic Drug ◽  
Akt Signaling Pathway ◽  
Anti Cancer

Background: AKT/PKB is an important enzyme with numerous biological functions, and its overexpression is related to the carcinogenesis. AKT stimulates different signaling pathways that are downstream of activated tyrosine kinases and phosphatidylinositol 3-kinase, hence functions as an important target for anti-cancer drugs. Objective: In this review article, we have interpreted the role of AKT signaling pathways in cancer and natural inhibitory effect of Thymoquinone (TQ) in AKT and its possible mechanism. Method: We have collected the updated information and data on AKT, their role in cancer and inhibitory effect of TQ in AKT signaling pathway from google scholar, PubMed, Web of Science, Elsevier, Scopus and many more. Results: There are many drugs already developed, which can target AKT, but very few among them have passed clinical trials. TQ is a natural compound, mainly found in black cumin, which has been found to have potential anti-cancer activities. TQ targets numerous signaling pathways, including AKT, in different cancers. In fact, many studies revealed that AKT is one of the major targets of TQ. The preclinical success of TQ suggests its clinical studies on cancer. Conclusion: This review article summarizes the role of AKT in carcinogenesis, its potent inhibitors in clinical trials, and how TQ acts as an inhibitor of AKT and TQ’s future as a cancer therapeutic drug.

scholarly journals Fractional Differential Equation-Based Instantaneous Frequency Estimation for Signal Reconstruction

2021 ◽  
Vol 5 (3) ◽  
pp. 83
Author(s):  
Bilgi Görkem Yazgaç ◽  
Mürvet Kırcı
Keyword(s):  
Differential Equation ◽  
Fractional Differential Equation ◽  
Frequency Estimation ◽  
Signal Reconstruction ◽  
Reconstruction Method ◽  
Reconstruction Algorithms ◽  
Instantaneous Frequency Estimation ◽  
Proposed Model ◽  
Fractional Differential ◽  
Instantaneous Frequencies

In this paper, we propose a fractional differential equation (FDE)-based approach for the estimation of instantaneous frequencies for windowed signals as a part of signal reconstruction. This approach is based on modeling bandpass filter results around the peaks of a windowed signal as fractional differential equations and linking differ-integrator parameters, thereby determining the long-range dependence on estimated instantaneous frequencies. We investigated the performance of the proposed approach with two evaluation measures and compared it to a benchmark noniterative signal reconstruction method (SPSI). The comparison was provided with different overlap parameters to investigate the performance of the proposed model concerning resolution. An additional comparison was provided by applying the proposed method and benchmark method outputs to iterative signal reconstruction algorithms. The proposed FDE method received better evaluation results in high resolution for the noniterative case and comparable results with SPSI with an increasing iteration number of iterative methods, regardless of the overlap parameter.

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