Identifying frequent patterns in biochemical reaction networks: a workflow

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 VC The Author(s) 2018. Published by Oxford University Press. Page 1 of 14 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. (page number not for citation purposes) Database, 2018, 1–14 doi: 10.1093/database/bay051

[1]  E. Klipp,et al.  Retrieval, alignment, and clustering of computational models based on semantic annotations , 2011, Molecular systems biology.

[2]  Dagmar Waltemath,et al.  Management of simulation studies in computational biology , 2015 .

[3]  Erhard Rahm,et al.  Similarity flooding: a versatile graph matching algorithm and its application to schema matching , 2002, Proceedings 18th International Conference on Data Engineering.

[4]  Nicolas Le Novère,et al.  Systems Biology Graphical Notation: Process Description language Level 1 Version 1.3 , 2011, J. Integr. Bioinform..

[5]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[6]  Edda Klipp,et al.  Propagating semantic information in biochemical network models , 2012, BMC Bioinformatics.

[7]  Wolfgang Nejdl,et al.  Semantically Enhanced Searching and Ranking on the Desktop , 2005, Semantic Desktop Workshop.

[8]  Christian Borgelt,et al.  Mining molecular fragments: finding relevant substructures of molecules , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[9]  Philip S. Yu,et al.  Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..

[10]  Chun-Hsi Huang,et al.  Biological network motif detection: principles and practice , 2012, Briefings Bioinform..

[11]  K. Lakshmi,et al.  FREQUENT SUBGRAPH MINING ALGORITHMS - A SURVEY AND FRAMEWORK FOR CLASSIFICATION , 2012, ICIT 2012.

[12]  John H. Gennari,et al.  Qualitative Causal Analyses of Biosimulation Models , 2016, ICBO/BioCreative.

[13]  Carole A. Goble,et al.  Why Linked Data is Not Enough for Scientists , 2010, 2010 IEEE Sixth International Conference on e-Science.

[14]  Jiawei Han,et al.  gSpan: graph-based substructure pattern mining , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[15]  Hugh D. Spence,et al.  Minimum information requested in the annotation of biochemical models (MIRIAM) , 2005, Nature Biotechnology.

[16]  Nicolas Le Novère,et al.  Structure, function, and behaviour of computational models in systems biology , 2013, BMC Systems Biology.

[17]  M. Elowitz,et al.  A synthetic oscillatory network of transcriptional regulators , 2000, Nature.

[18]  Zhaohui Wu,et al.  An Efficient Recommendation Method for Improving Business Process Modeling , 2014, IEEE Transactions on Industrial Informatics.

[19]  James Hetherington,et al.  Computational challenges of systems biology , 2004, Computer.

[20]  Jianzhi Zhang,et al.  A Big World Inside Small-World Networks , 2009, PloS one.

[21]  Michael Hucka,et al.  The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core , 2010, J. Integr. Bioinform..

[22]  Sarala M. Wimalaratne,et al.  The Systems Biology Graphical Notation , 2009, Nature Biotechnology.

[23]  Jacky L. Snoep,et al.  Web-based kinetic modelling using JWS Online , 2004, Bioinform..

[24]  Yangyang Zhao,et al.  BioModels: ten-year anniversary , 2014, Nucleic Acids Res..

[25]  Olaf Wolkenhauer,et al.  An algorithm to detect and communicate the differences in computational models describing biological systems , 2015, Bioinform..

[26]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[27]  Melanie I. Stefan,et al.  BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models , 2010, BMC Systems Biology.

[28]  D. Broomhead,et al.  A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes , 2013, FEBS letters.

[29]  Gary R. Mirams,et al.  The Cardiac Electrophysiology Web Lab , 2016, Biophysical journal.

[30]  Thorsten Meinl,et al.  A Quantitative Comparison of the Subgraph Miners MoFa, gSpan, FFSM, and Gaston , 2005, PKDD.

[31]  M. Kanehisa,et al.  Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. , 2003, Journal of the American Chemical Society.

[32]  Olaf Wolkenhauer,et al.  Combining computational models, semantic annotations and simulation experiments in a graph database , 2015, Database J. Biol. Databases Curation.

[33]  P. Mendes,et al.  Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks , 2013, PloS one.

[34]  Wojciech Szpankowski,et al.  An efficient algorithm for detecting frequent subgraphs in biological networks , 2004, ISMB/ECCB.

[35]  Mohammad Reza Keyvanpour,et al.  Classification and Analysis of Frequent Subgraphs Mining Algorithms , 2012, J. Softw..

[36]  Olaf Wolkenhauer,et al.  Annotation-based feature extraction from sets of SBML models , 2014, Journal of Biomedical Semantics.

[37]  Amnon Shashua,et al.  Probabilistic graph and hypergraph matching , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Maurice Bruynooghe,et al.  A polynomial time computable metric between point sets , 2001, Acta Informatica.

[39]  Ron Henkel,et al.  Notions of similarity for systems biology models , 2016, Briefings Bioinform..

[40]  Nicolas Le Novère,et al.  Ranked retrieval of Computational Biology models , 2010, BMC Bioinformatics.

[41]  Michel Dumontier,et al.  Controlled vocabularies and semantics in systems biology , 2011, Molecular systems biology.

[42]  Susumu Goto,et al.  The KEGG resource for deciphering the genome , 2004, Nucleic Acids Res..

[43]  J. Tyson.  Modeling the cell division cycle: cdc2 and cyclin interactions. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[44]  Astrid Junker,et al.  Wiring diagrams in biology: towards the standardized representation of biological information. , 2012, Trends in biotechnology.

[45]  George Karypis,et al.  Frequent subgraph discovery , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[46]  Olaf Wolkenhauer,et al.  COMODI: an ontology to characterise differences in versions of computational models in biology , 2016, Journal of Biomedical Semantics.

[47]  John J Tyson,et al.  Functional motifs in biochemical reaction networks. , 2010, Annual review of physical chemistry.

[48]  Dagmar Waltemath,et al.  How Can Semantic Annotations Support the Identification of Network Similarities? , 2014, SWAT4LS.

[49]  Olaf Wolkenhauer,et al.  Improving the reuse of computational models through version control , 2013, Bioinform..

[50]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[51]  Peter J. Hunter,et al.  Bioinformatics Applications Note Databases and Ontologies the Physiome Model Repository 2 , 2022 .

[52]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[53]  Jiawei Han,et al.  Frequent pattern mining: current status and future directions , 2007, Data Mining and Knowledge Discovery.

[54]  Debahuti Mishra,et al.  An approach to graph mining using gSpan algorithm , 2010, 2010 International Conference on Computer and Communication Technology (ICCCT).