| 1. |
- Henkel, Ron, et al.
(författare)
-
Finding patterns in biochemical reaction networks
- 2016
-
Ingår i: PeerJ Preprints. - : PeerJ. - 2167-9843. ; 4
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Computational models in biology encode molecular and cell biological processes. Many of them can be represented as biochemical reaction networks. Studying such networks, one is often interested in systems that share similar reactions and mechanisms. Typical goals are to understand the parts of a model, to identify reoccurring patterns, and to find biologically relevant motifs. The large number of models are available for such a search, but also the large size of models require automated methods. Specifically the generic problem of finding patterns in large networks is computationally hard. As a consequence, only partial solutions for a structural analysis of models exist. Here we introduce a tool chain that identifies reoccurring patterns in biochemical reaction networks. We started this work with an evaluation of algorithms for the identification of frequent subgraphs. Then, we created graph representations of existing SBML models and ran the most suitable algorithm on the data. The result was a list of reaction patterns together with statistics about the occurrence of each pattern in the data set. The approach was validated with 575 SBML models from the curated branch of BioModels. We analysed how the resulting patterns confirm with expectations from the literature and from previous model statistics. In the future, the identified patterns can serve as a tool to measure the similarity of models
|
|
| 2. |
- Lambusch, Fabienne, et al.
(författare)
-
Identifying frequent patterns in biochemical reaction networks : a workflow
- 2018
-
Ingår i: Database. - : Oxford University Press (OUP). - 1758-0463. ; 2018
-
Tidskriftsartikel (refereegranskat)abstract
- Computational models in biology encode molecular and cell biological processes. Many of these models can be represented as biochemical reaction networks. Studying such networks, one is mostly interested in systems that share similar reactions and mechanisms. Typical goals of an investigation thus include understanding of model parts, identification of reoccurring patterns and recognition of biologically relevant motifs. The large number and size of available models, however, require automated methods to support researchers in achieving their goals. Specifically for the problem of finding patterns in large networks only partial solutions exist. We propose a workflow that identifies frequent structural patterns in biochemical reaction networks encoded in the Systems Biology Markup Language. The workflow utilizes a subgraph mining algorithm to detect the network patterns. Once patterns are identified, the textual pattern description can automatically be converted into a graphical representation. Furthermore, information about the distribution of patterns among a selected set of models can be retrieved. The workflow was validated with 575 models from the curated branch of BioModels. In this paper, we highlight interesting and frequent structural patterns. Furthermore, we provide exemplary patterns that incorporate terms from the Systems Biology Ontology. Our workflow can be applied to a custom set of models or to models already existing in our graph database MaSyMoS. The occurrences of frequent patterns may give insight into the encoding of central biological processes, evaluate postulated biological motifs or serve as a similarity measure for models that share common structures.
|
|
