Advancing cancer systems biology: introducing the Center for the Development of a Virtual Tumor, CViT

Abstract

Integrative cancer biology research relies on a variety of data-driven computational modeling and simulation methods and techniques geared towards gaining new insights into the complexity of biological processes that are of critical importance for cancer research. These include the dynamics of gene-protein interaction networks, the percolation of sub-cellular perturbations across scales and the impact they may have on tumorigenesis in both experiments and clinics. Such innovative 'systems' research will greatly benefit from enabling Information Technology that is currently under development, including an online collaborative environment, a Semantic Web based computing platform that hosts data and model repositories as well as high-performance computing access. Here, we present one of the National Cancer Institute's recently established Integrative Cancer Biology Programs, i.e. the Center for the Development of a Virtual Tumor, CViT, which is charged with building a cancer modeling community, developing the aforementioned enabling technologies and fostering multi-scale cancer modeling and simulation.

Keywords: Cancer; complexity; digital model repository; multi-scale computational tumor modeling; semantic layered research platform; systems biology.

Figure 1.

Multi-Scale Modeling. 3D snapshots of a virtual brain tumor at three consecutive time points (left to right), from Zhang et al. (2007). Blue color represents proliferating tumor cells, while red depicts migratory, green quiescent and grey dead tumor cells. At an early stage the proliferative tumor core appears to be completely surrounded by a cloud of migratory cells, at a later time point, however, a more heterogeneous picture emerges where ultimately a ‘tip’-population of migratory cells can be found adjacent to the location of a nutrient source (top right quadrant, not shown).

Figure 2.

CViT Home Page (left) and Investigator Profiles (right).

Figure 3.

CViT Blog (left) and RSS Feeds (right).

Figure 4.

CViT Annotation System.

Figure 5.

Knowledge Integrated Modeling. Employing SLRP annotated data (e.g. from PubMed listed manuscripts; top left) are captured and used as model input parameters (top right). Deploying the code (language editing perspective, bottom right) to a high-performance compute environment yields in silico results that can then be compared with experimental data such as the microscopy images of tumors cultured in vitro (bottom left), or back to data published in the literature. This feedback allows continuous refinement of the (DMR-archived) algorithm(s) and spurs design and development of experiments.

Figure 6.

Digital Model Repository (DMR). Shown are three ‘Biologist Workbench’ views of data in the DMR. The first image (top left) depicts an actual model instance, with links to the model artifacts, experimental data, source code, abstract representation (equations), related literature, annotation and, of course, the results, which may include biomedical images, video-microscopy and computer simulation movie. The second image (top right) shows how a user may choose to view the key module to which the model belongs whereas the third image (bottom) visually describes the semantic inter-relationship links between data elements in series of models.