Identifying frequent patterns in biochemical reaction networks: a workflow.

Fabienne Lambusch,Olaf Wolkenhauer,Ron Henkel,Christian Rosenke,Dagmar Waltemath,Kurt Sandkuhl

doi:10.1093/database/bay051

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.Database URL: https://github.com/FabienneL/BioNet-Mining

Highlights

Modelling is an integral part of computational biology [1]
Purely structural and incorporating annotations to the Systems Biology Ontology (SBO) [20], which were detected in curated Systems Biology Markup Language (SBML) models by means of the proposed workflow
An automated retrieval of common patterns enables various types of investigations, such as ‘What are frequently used structures to represent biochemical processes?’; ‘Can we find unique patterns for certain modelling techniques?’; ‘Do frequent patterns reflect well-known motifs in Systems Biology, such as functional motifs proposed by Tyson and Novk [21]?’; ‘Can we cluster a set of models with regard to occurrences of certain patterns?’

Summary

Introduction

Its increasing impact is reflected in the rapidly growing number and complexity of computational models [2, 3]. Such models encode a wide range of biological processes [including cell cycle processes, apoptosis, mitogenactivated protein kinase and many more [4]] and thereby enable computer-based analysis of complex biological systems. We observe that many models reassemble large biochemical reaction networks. They may have been semiautomatically generated using data driven approaches, for example, to construct models from metabolic networks [5, 6]. Models may prove a theory or concept, for example, to mathematically describe interactions between biological entities [7] or generic oscillatory networks of transcriptional regulators [8]

Methods

Results

Discussion

Conclusion