scholarly journals SteadyCellPhenotype: A web-based tool for the modeling of biological networks with ternary logic

2021 ◽  
Author(s):  
Adam C Knapp ◽  
Luis Sordo Vieira ◽  
Reinhard Laubenbacher ◽  
Julia Chifman
Keyword(s):  
Biological Networks ◽  
Steady States ◽  
Stochastic Simulations ◽  
Ternary Logic ◽  
Web Based ◽  
Deterministic Simulations ◽  
Publication Quality

We introduce SteadyCellPhenotype, a browser based interface for the analysis of ternary biological networks. It includes tools for deterministically finding all steady states of a network, as well as the simulation and visualization of trajectories with publication quality graphics. Stochastic simulations allow us to approximate the size of the basin for attractors and deterministic simulations of trajectories nearby specified points allow us to explore the behavior of the system in that neighborhood.

scholarly journals Interactive Clustered Heat Map Builder: An easy web-based tool for creating sophisticated clustered heat maps

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 1750 ◽  
Author(s):  
Michael C. Ryan ◽  
Mark Stucky ◽  
Chris Wakefield ◽  
James M. Melott ◽  
Rehan Akbani ◽  
...  
Keyword(s):  
Molecular Profiling ◽  
Study Data ◽  
Linear Process ◽  
Heat Map ◽  
Web Based ◽  
Heat Maps ◽  
Map Generation ◽  
Genome Scale ◽  
Quality Maps ◽  
Publication Quality

Clustered heat maps are the most frequently used graphics for visualization and interpretation of genome-scale molecular profiling data in biology.  Construction of a heat map generally requires the assistance of a biostatistician or bioinformatics analyst capable of working in R or a similar programming language to transform the study data, perform hierarchical clustering, and generate the heat map.  Our web-based Interactive Heat Map Builder can be used by investigators with no bioinformatics experience to generate high-caliber, publication quality maps.  Preparation of the data and construction of a heat map is rarely a simple linear process.  Our tool allows a user to move back and forth iteratively through the various stages of map generation to try different options and approaches.  Finally, the heat map the builder creates is available in several forms, including an interactive Next-Generation Clustered Heat Map that can be explored dynamically to investigate the results more fully.

scholarly journals HD-eXplosion: visualization of hydrogen–deuterium exchange data as chiclet and volcano plots with statistical filtering

2020 ◽  
Author(s):  
Naifu Zhang ◽  
Xiaohe Yu ◽  
Xinchao Zhang ◽  
Sheena D’Arcy
Keyword(s):  
Source Code ◽  
Deuterium Exchange ◽  
Hydrogen Deuterium Exchange ◽  
Web Based ◽  
Data Presentation ◽  
Software Packages ◽  
Volcano Plots ◽  
Hydrogen Deuterium ◽  
Exchange Data ◽  
Publication Quality

Abstract Summary Hydrogen–Deuterium eXchange coupled to mass spectrometry is a powerful tool for the analysis of protein dynamics and interactions. Bottom-up experiments looking at deuterium uptake differences between various conditions are the most common. These produce multi-dimensional data that can be challenging to depict in a single visual format. Each user must also set significance thresholds to define meaningful differences and make these apparent in data presentation. To assist in this process, we have created HD-eXplosion, an open-source, web-based application for the generation of chiclet and volcano plots with statistical filters. HD-eXplosion fills a void in available software packages and produces customizable plots that are publication quality. Availability and implementation The HD-eXplosion application is available at http://hd-explosion.utdallas.edu. The source code can be found at https://github.com/HD-Explosion.

scholarly journals On Crowd-verification of Biological Networks

2013 ◽  
Vol 7 ◽  
pp. BBI.S12932 ◽  
Author(s):  
Sam Ansari ◽  
Jean Binder ◽  
Stephanie Boue ◽  
Anselmo Di Fabio ◽  
William Hayes ◽  
...  
Keyword(s):  
Biological Networks ◽  
Network Models ◽  
Biological Information ◽  
Current Status ◽  
Biological Knowledge ◽  
Web Based ◽  
Domain Experts ◽  
Graphical Interfaces ◽  
Continuous Use ◽  
Complexity Increase

Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.

scholarly journals Data-dependent visualization of biological networks in the web-browser with NDExEdit

2021 ◽  
Author(s):  
Florian Auer ◽  
Simone Mayer ◽  
Frank Kramer
Keyword(s):  
Biological Networks ◽  
Data Model ◽  
Biological Knowledge ◽  
Network Data ◽  
Web Tool ◽  
Bioinformatics Analyses ◽  
Web Browser ◽  
Web Based ◽  
Biological Entities ◽  
The Web

Networks are a common methodology used to capture increasingly complex associations between biological entities. They serve as a resource of biological knowledge for bioinformatics analyses, and also comprise the subsequent results. However, the interpretation of biological networks is challenging and requires suitable visualizations dependent on the contained information. The most prominent software in the field for the visualization of biological networks is Cytoscape, a desktop modeling environment also including many features for analysis. A further challenge when working with networks is their distribution. Within a typical collaborative workflow, even slight changes of the network data force one to repeat the visualization step as well. Also, just minor adjustments to the visual representation not only need the networks to be transferred back and forth. Collaboration on the same resources requires specific infrastructure to avoid redundancies, or worse, the corruption of the data. A well-established solution is provided by the NDEx platform where users can upload a network, share it with selected colleagues or make it publicly available. NDExEdit is a web-based application where simple changes can be made to biological networks within the browser, and which does not require installation. With our tool, plain networks can be enhanced easily for further usage in presentations and publications. Since the network data is only stored locally within the web browser, users can edit their private networks without concerns of unintentional publication. The web tool is designed to conform to the Cytoscape Exchange (CX) format as a data model, which is used for the data transmission by both tools, Cytoscape and NDEx. Therefore the modified network can be exported as a compatible CX file, additionally to standard image formats like PNG and JPEG.

scholarly journals The Personalized Cancer Network Explorer (PeCaX) as a visual analytics tool to support molecular tumor boards

2021 ◽  
Author(s):  
Mirjam Figaschewski ◽  
Bilge Sürün ◽  
Thorsten Tiede ◽  
Oliver Kohlbacher
Keyword(s):  
Biological Networks ◽  
Visual Analytics ◽  
Patient Specific ◽  
Visual Interpretation ◽  
Web Based ◽  
Specific Tumor ◽  
Tumor Boards ◽  
Personalized Oncology ◽  
Personalized Cancer ◽  
Cancer Network

Personalized oncology represents a shift in cancer treatment from conventional methods to target specific therapies where the decisions are made based on the patient specific tumor profile. Selection of the optimal therapy relies on a complex interdisciplinary analysis and interpretation of these variants by experts in molecular tumor boards. With up to hundreds of somatic variants identified in a tumor, this process requires visual analytics tools to guide and accelerate the annotation process. The Personal Cancer Network Explorer (PeCaX) is a visual analytics tool supporting the efficient annotation, navigation, and interpretation of somatic genomic variants through functional annotation, drug target annotation, and visual interpretation within the context of biological networks. Starting with somatic variants in a VCF file, PeCaX enables users to explore these variants through a web-based graphical user interface. The most protruding feature of PeCaX is the combination of clinical variant annotation and gene- drug networks with an interactive visualization. This reduces the time and effort the user needs to invest to get to a treatment suggestion and helps to generate new hypotheses. PeCaX is being provided as a platform-independent containerized software package for local or institution-wide deployment. PeCaX is available for download at https://github.com/KohlbacherLab/PeCaX-docker.

scholarly journals Interactive Clustered Heat Map Builder: An easy web-based tool for creating sophisticated clustered heat maps

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1750
Author(s):  
Michael C. Ryan ◽  
Mark Stucky ◽  
Chris Wakefield ◽  
James M. Melott ◽  
Rehan Akbani ◽  
...  
Keyword(s):  
Molecular Profiling ◽  
Study Data ◽  
Linear Process ◽  
Heat Map ◽  
Web Based ◽  
Heat Maps ◽  
Map Generation ◽  
Genome Scale ◽  
Quality Maps ◽  
Publication Quality

Clustered heat maps are the most frequently used graphics for visualization and interpretation of genome-scale molecular profiling data in biology.  Construction of a heat map generally requires the assistance of a biostatistician or bioinformatics analyst capable of working in R or a similar programming language to transform the study data, perform hierarchical clustering, and generate the heat map.  Our web-based Interactive Heat Map Builder can be used by investigators with no bioinformatics experience to generate high-caliber, publication quality maps.  Preparation of the data and construction of a heat map is rarely a simple linear process.  Our tool allows a user to move back and forth iteratively through the various stages of map generation to try different options and approaches.  Finally, the heat map the builder creates is available in several forms, including an interactive Next-Generation Clustered Heat Map that can be explored dynamically to investigate the results more fully.

scholarly journals Enhancement of COPD biological networks using a web-based collaboration interface

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 32 ◽  
Author(s):  
◽  
Stéphanie Boué ◽  
Brett Fields ◽  
Julia Hoeng ◽  
Jennifer Park ◽  
...  
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Network Models ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Web Based ◽  
Data Driven Approach ◽  
Transcriptomics Data ◽  
Model Set ◽  
Biological Representation

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website (https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.

scholarly journals Predicting genetic interactions from Boolean models of biological networks

2015 ◽  
Author(s):  
Laurence Calzone ◽  
Emmanuel Barillot ◽  
Andrei Zinovyev
Keyword(s):  
Biological Networks ◽  
Genetic Interaction ◽  
Initial Conditions ◽  
Genetic Interactions ◽  
Interaction Networks ◽  
Stochastic Simulations ◽  
Model Organisms ◽  
Loss Of Function ◽  
Boolean Models ◽  
Computational Tools

Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed.

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