Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system.

/pdf/using-graph-theory-to-analyze-biological-networks-26d85b1a4n.pdf

Using graph theory to analyze biological networks

Background
Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in the use of models as well as the development of improved software systems and the availability of better, cheaper computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in these repositories should be extensively tested and encoded in community-supported and standardised formats. In addition, the models and their components should be cross-referenced with other resources in order to allow their unambiguous identification.

/pdf/biomodels-database-an-enhanced-curated-and-annotated-2ipsovu23z.pdf

Biomodels database: an enhanced curated and annotated resource for published quantitative kinetic models

The analysis and interpretation of relationships between biological molecules, networks and concepts is becoming a major bottleneck in systems biology. Very often the pure amount of data and their heterogeneity provides a challenge for the visualization of the data. There are a wide variety of graph representations available, which most often map the data on 2D graphs to visualize biological interactions. These methods are applicable to a wide range of problems, nevertheless many of them reach a limit in terms of user friendliness when thousands of nodes and connections have to be analyzed and visualized. In this study we are reviewing visualization tools that are currently available for visualization of biological networks mainly invented in the latest past years. We comment on the functionality, the limitations and the specific strengths of these tools, and how these tools could be further developed in the direction of data integration and information sharing.

/pdf/a-survey-of-visualization-tools-for-biological-network-3jp6a3gqud.pdf

A survey of visualization tools for biological network analysis

Translational research, the effort to couple the results of basic research to clinical applications, depends on the ability to effectively answer questions using information that spans multiple disciplines. The Semantic Web, with its emphasis on combining information using standard representation languages, access to that information via standard web protocols, and technologies to leverage computation, such as in the form of inference and distributable query, offers a social and technological basis for assembling, integrating and making available biomedical knowledge at Web scale. In this article, we discuss the use of Semantic Web technology for assembling and querying biomedical knowledge from multiple sources and disciplines. We present the Neurocommons prototype knowledge base, a demonstration intended to show the feasibility and benefits of using these technologies. The prototype knowledge base can be used to experiment with and assess the scalability of current tools and methods for creating such a resource, and to elicit issues that will need to be addressed in order to expand the scope and use of it. We demonstrate the utility of the knowledge base by reviewing a few example queries that provide answers to precise questions relevant to the understanding of disease. All components of the knowledge base are freely available at http://neurocommons.org/, enabling readers to reconstruct the knowledge base and experiment with this new technology.

Life sciences on the Semantic Web: the Neurocommons and beyond

MOTIVATION: Research in systems biology is carried out through a combination of experiments and models. Several data standards have been adopted for representing models (Systems Biology Markup Language) and various types of relevant experimental data (such as FuGE and those of the Proteomics Standards Initiative). However, until now, there has been no standard way to associate a model and its entities to the corresponding datasets, or vice versa. Such a standard would provide a means to represent computational simulation results as well as to frame experimental data in the context of a particular model. Target applications include model-driven data analysis, parameter estimation, and sharing and archiving model simulations. RESULTS: We propose the Systems Biology Results Markup Language (SBRML), an XML-based language that associates a model with several datasets. Each dataset is represented as a series of values associated with model variables, and their corresponding parameter values. SBRML provides a flexible way of indexing the results to model parameter values, which supports both spreadsheet-like data and multidimensional data cubes. We present and discuss several examples of SBRML usage in applications such as enzyme kinetics, microarray gene expression and various types of simulation results.

/pdf/sbrml-a-markup-language-for-associating-systems-biology-data-3pfbnyssso.pdf

SBRML: a markup language for associating systems biology data with models.

At present, there are over 215 Internet-accessible databases that store biological pathway data. Biologists often need to use information from many of these to support their research, but since each has its own representation conventions and data access methods, integrating data from multiple databases is very difficult. A widely-adopted biological pathway data exchange format will help. BioPAX (Biological Pathway Exchange - http://www.biopax.org) enables the integration of diverse pathway resources by defining an open file format specification for the exchange of biological pathway data. By utilizing the BioPAX format, the problem of data integration reduces to a semantic mapping between the data models of each resource and the data model defined by BioPAX. Widespread adoption of BioPAX for data exchange will increase access to and uniformity of pathway data from varied sources, thus increasing the efficiency of computational pathway research. This document describes BioPAX Level 2, which expands the scope of BioPAX to include representation of molecular binding interactions, protein post-translational modifications, basic experimental descriptions, and hierarchical pathways. Adding coverage of these features will allow BioPAX to represent the bulk of the data in the PSI-MI Level 2 format (http://psidev.sourceforge.net/mi/rel2/doc/) and lays the groundwork for better support of signal transduction and molecular states.

/pdf/biopax-biological-pathways-exchange-language-level-2-version-4aw5loa4dl.pdf

BioPAX - Biological Pathways Exchange Language Level 2, Version 1.0 Documentation

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Eric Brauner

Author Tools

Chat about Author

Papers

BioPAX - Biological Pathways Exchange Language Level 2, Version 1.0 Documentation