Accelerating cancer systems biology research through Semantic Web technology

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 not only interact with the DMR 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.

Simulating cancer growth with multiscale agent-based modeling

“…However, given their extensive computational demand ~ a serious shortcoming of ABM ~ agent-based models should be developed as complex as necessary yet as simple as possible with the help of experimental and/or clinical data. There are many challenges researchers may face in pursuing and applying this type of integrated experimental and systems modeling approach in their research, ranging from access to relevant biomedical data to the development of data standards that foster transparency and facilitate exchange of input data and results [85]. In the following, we focus on discussing two of these challenges with the hope of helping researchers to better design and refine their modeling strategy; if interested, the reader can refer to reviews elsewhere (e.g., [4]) to have a more detailed understanding of the challenges involved.…”

Integrated PK-PD and agent-based modeling in oncology

“…We are hopeful that modeling will ultimately provide a valuable tool for clinicians to designing individualized treatment plans in an effort to increase treatment efficacy and improve patient survival rates. However, challenges exist, as with any cancer modeling approach, and they start with data access, which is essential to advance this field [98]. For instance, sharing curated, consented patient data, needed to build more realistic and clinically more useful models, can be technically difficult and time consuming if multiple institutions are involved.…”

Connecting digital cancer model repositories with markup

“…First, the input and output parameters are described to enable the model to be run in an execution environment, like CViT's Computational Model Execution Framework [17], and linked with other TumorML model descriptions as a complex model. In the case of the EGFR-ERK pathway model here, it is a simple model description as it only concerns the one published implementation.…”