This Title All WIREs
How to cite this WIREs title:
WIREs Syst Biol Med
Impact Factor: 4.192

Accelerating cancer systems biology research through Semantic Web technology

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Abstract Cancer systems biology is an interdisciplinary, rapidly expanding research field in which collaborations are a critical means to advance the field. Yet the prevalent database technologies often isolate data rather than making it easily accessible. The Semantic Web has the potential to help facilitate web‐based collaborative cancer research by presenting data in a manner that is self‐descriptive, human and machine readable, and easily sharable. We have created a semantically linked online Digital Model Repository (DMR) for storing, managing, executing, annotating, and sharing computational cancer models. Within the DMR, distributed, multidisciplinary, and inter‐organizational teams can collaborate on projects, without forfeiting intellectual property. This is achieved by the introduction of a new stakeholder to the collaboration workflow, the institutional licensing officer, part of the Technology Transfer Office. Furthermore, the DMR has achieved silver level compatibility with the National Cancer Institute's caBIG, so users can interact with the DMR not only through a web browser but also through a semantically annotated and secure web service. We also discuss the technology behind the DMR leveraging the Semantic Web, ontologies, and grid computing to provide secure inter‐institutional collaboration on cancer modeling projects, online grid‐based execution of shared models, and the collaboration workflow protecting researchers' intellectual property. WIREs Syst Biol Med 2013, 5:135–151. doi: 10.1002/wsbm.1200 This article is categorized under: Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Mechanistic Models

This WIREs title offers downloadable PowerPoint presentations of figures for non-profit, educational use, provided the content is not modified and full credit is given to the author and publication.

Download a PowerPoint presentation of all images

A high‐level architecture of the Digital Model Repository (DMR). CViT's DMR is built on top of an RDF data store that stores data in an IBM DB2 database. RDF allows CViT to semantically annotate the content of the repository and store hyperlinks to other resources. The CViT.org website provides the graphical user interface to the repository. Through CViT.org, scientists can add new models, share models with other researchers, and discuss model simulations. The DMR grid service provides a caBIG silver‐level compliant interface to the DMR, which allows caBIG client applications to securely upload and access models and model metadata within the repository. The Computational Model Execution Framework (CMEF) server (bottom left) provides capabilities to execute the models stored in the repository on a computational grid.

[ Normal View | Magnified View ]

Snapshot of CollaboRank displaying registered institutions' sharing scores.

[ Normal View | Magnified View ]

Snapshots of the CViT mashup (a) and Digital Model Repository (DMR) graph (b).

[ Normal View | Magnified View ]

Extended elements of the Digital Model Repository (DMR) domain model to support the CMEF. The Computational Model Execution Framework (CMEF) domain model extends from the CViT DMR domain model to introduce additional classes to support the requirements of the CMEF.

[ Normal View | Magnified View ]

Computational Model Execution Framework (CMEF) components of the DMR architecture. The CMEF extends the functionality of the DMR by enabling any model stored within the repository to be executed within a grid‐based execution environment. It consists of the following services. (1) The Model Administration Service lets the principal investigator add semantic metadata necessary to describe the parameters for model execution. (2) The Model Execution Service handles the requests from users to execute models and is in charge of linking the input data specified with the model, and of submitting the execution to the Job Scheduler. (3) The Job Scheduler interacts with external Grid Execution Engines in order to send a model for actual execution. (4) The Execution Monitoring Service receives events from the models under execution and from the Grid Execution Engine, and provides information to the user through the CViT website. Upon completion of execution of a model, it stores the simulation results within the DMR.

[ Normal View | Magnified View ]

Snapshots of key Computational Model Execution Framework (CMEF) workflow features. (a) New model wizard. (b) User submits a job—simulation task. (c) Job results page. The model added as an example is a two‐dimensional simulation model for investigating lung cancer growth across molecular and multicellular scales.55

[ Normal View | Magnified View ]

Snapshots of key Digital Model Repository (DMR) workflow features. (a) An example of a complete entry in the DMR with title, description, references, etc. (b) User publishes his entry to other users in the DMR. (c) New contributors are added to a DMR entry.

[ Normal View | Magnified View ]

The initial Digital Model Repository domain model was constructed to expose the content of the CViT repository through a caBIG silver‐level compliant data service. It took several months of interaction with caBIG's work groups to refine the model and annotate it with NCI Thesaurus concepts.

[ Normal View | Magnified View ]

Related Articles

Multiscale modeling for biologists
Systems approaches and algorithms for discovery of combinatorial therapies
In silico models of cancer
Cancer: A Systems Approach

Browse by Topic

Models of Systems Properties and Processes > Mechanistic Models
Analytical and Computational Methods > Computational Methods

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts