Considerations of graph-based concepts to manage of computational biology models and associated simulations.

Abstract

Abstract: Over the past years various databases in Life Sciences have been de-veloped, among them databases to handle computational models of biological systems. Exchange formats that represent these models are typically XML-based; they encode models as networks. Models are published together with supplementary materials such as annotations, simulation experiment descrip-tions, or result sets. In consequence, not only model files need to be managed, but also the associated simulation setups, and highly linked meta-information. We discuss here the use of graph databases for model storage as they well re-flect this interrelated nature. They can also enhance the integrated management of computational models and associated meta-information, as they handle con-nections between models, simulation descriptions and result data files, as well as external knowledge. This property enhances version control, retrieval and ranking, thereby resulting in improved model reuse and result reproducibility.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 10 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.