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Identifying frequent patterns in biochemical reaction networks: a workflow.
Fabienne Lambusch1, Dagmar Waltemath2, Olaf Wolkenhauer2,3
1Business Information Systems, University of Rostock, Rostock, Mecklenburg-Vorpommern, Germany.
Summary
This study introduces a computational workflow to automatically identify frequent structural patterns in biological reaction networks. This aids in understanding complex biological processes and discovering recurring motifs across many models.
Area of Science:
- Computational Biology
- Systems Biology
- Bioinformatics
Background:
- Biological models often represent complex molecular and cell biological processes as biochemical reaction networks.
- Identifying recurring patterns and motifs in these networks is crucial for understanding biological mechanisms.
- Existing methods struggle with the scale and complexity of large biological model databases.
Purpose of the Study:
- To develop an automated workflow for identifying frequent structural patterns in biochemical reaction networks.
- To facilitate the analysis and comparison of biological models.
- To support researchers in understanding complex biological systems.
Main Methods:
- Utilized a subgraph mining algorithm to detect patterns in networks encoded in Systems Biology Markup Language.
- Developed a workflow for pattern identification, graphical representation, and distribution analysis.
- Validated the workflow using 575 curated models from BioModels.
Main Results:
- Successfully identified frequent structural patterns in biochemical reaction networks.
- Demonstrated the ability to convert textual pattern descriptions into graphical representations.
- Provided exemplary patterns incorporating Systems Biology Ontology terms.
Conclusions:
- The proposed workflow effectively automates the discovery of structural patterns in biological networks.
- This approach can offer insights into central biological processes and serve as a model similarity measure.
- The workflow is applicable to custom model sets and existing graph databases like MaSyMoS.
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