Semantically Linking in Silico Cancer Models

Abstract

Multiscale models are commonplace in cancer modeling, where individual models acting on different biological scales are combined within a single, cohesive modeling framework. However, model composition gives rise to challenges in understanding interfaces and interactions between them. Based on specific domain expertise, typically these computational models are developed by separate research groups using different methodologies, programming languages, and parameters. This paper introduces a graph-based model for semantically linking computational cancer models via domain graphs that can help us better understand and explore combinations of models spanning multiple biological scales. We take the data model encoded by TumorML, an XML-based markup language for storing cancer models in online repositories, and transpose its model description elements into a graph-based representation. By taking such an approach, we can link domain models, such as controlled vocabularies, taxonomic schemes, and ontologies, with cancer model descriptions to better understand and explore relationships between models. The union of these graphs creates a connected property graph that links cancer models by categorizations, by computational compatibility, and by semantic interoperability, yielding a framework in which opportunities for exploration and discovery of combinations of models become possible.

Recent advances in computational epigenetics

“…Examples of interactive query tools for integration and management of different medical and biological data types are given, together with Neo4j linkages for data management and analysis (Figure 1). 38 Neo4j-based frameworks have also been used to assess performance of in silico models of biological systems, notably computational and mathematical models of cancer 92,93 (and the BioModels database). 94 Moreover, FlockDB supports application to reset rather than traversal searches (based on adjacency graph storage), providing a platform, similar to that of, for example, GraphLab, MapReduce and Scope (amongst others), for scalable execution.…”

EpiGeNet: A Graph Database of Interdependencies Between Genetic and Epigenetic Events in Colorectal Cancer

“…Neo4j-based models have been developed also to capture and explore semantic relationships among computational and mathematical models related to cancer and to other biological systems [e.g., Johnson et al (2014) and Henkel et al (2015)]. In Henkel et al (2015), authors describe a Neo4j-based framework that facilitates identification, comparison, and ranking of in-silico models (encoded in SBML and CELL-ML standard formats and stored in major specialized resources such as BioModels Database) that correspond to specific categories.…”

Peer Review #2 of "GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis (v0.1)"

“…Both, nodes and edges can hold properties, which add information about the objects or the relationships. In recent years, this database model has been used in many bioinformatics applications and are particularly promising for biological datasets (Preusse et al, 2016;Johnson et al, 2014;Balaur et al, 2016;Henkel et al, 2015;Muth et al, 2015;Lysenko et al, 2016). Have and Jensen (Have et al, 2013) observed that for path and neighborhood queries, Neo4j, a graph database, can be orders of magnitude faster than PostgreSQL, a widely used relational database, while allowing for queries to be expressed more intuitively.…”