Abstract
Understanding disease mechanisms often requires complex and accurate integration of cellular pathways and molecular networks. Systems biology offers the possibility to provide a comprehensive map of the cell’s intricate wiring network, which can ultimately lead to decipher the disease phenotype. Here, we describe what biological pathways are, how they function in normal and abnormal cellular systems, limitations faced by databases for integrating data, and highlight how network models are emerging as a powerful integrative framework to understand and interpret the roles of proteins and peptides in diseases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Schadt EE (2009) Molecular networks as sensors and drivers of common human diseases. Nature 461:218–223
Krishnamurthy L, Nadeau J, Ozsoyoglu G et al (2003) Pathways database system: an integrated system for biological pathways. Bioinformatics 19:930–937
Kreeger PK, Lauffenburger DA (2010) Cancer systems biology: a network modeling perspective. Carcinogenesis 31:2–8
Cho DY, Kim YA, Przytycka TM (2012) Chapter 5: network biology approach to complex diseases. PLoS Comput Biol 8:e1002820
Zhang F, Drabier R (2012) IPAD: the integrated pathway analysis database for systematic enrichment analysis. BMC Bioinformatics 13(Suppl 15):S7
Huang H, Wu X, Sonachalam M et al (2012) PAGED: a pathway and gene-set enrichment database to enable molecular phenotype discoveries. BMC Bioinformatics 13(Suppl 15):S2
Losko S, Heumann K (2009) Semantic data integration and knowledge management to represent biological network associations. In: Nikolsky Y, Bryant J (eds) Protein networks and pathway analysis. Humana. Methods Mol Biol. 563: 241–258
Guo X, Shriver CD, Hu H et al (2005) Analysis of metabolic and regulatory pathways through gene ontology-derived semantic similarity measures. AMIA Annu Symp Proc 2005:972
Sahoo SS, Bodenreider O, Rutter JL et al (2008) An ontology-driven semantic mashup of gene and biological pathway information: application to the domain of nicotine dependence. J Biomed Inform 41:752–765
Wixon J, Kell D (2000) The Kyoto encyclopedia of genes and genomes: KEGG. Yeast 17:48–55
Kanehisa M, Goto S, Kawashima S et al (2004) The KEGG resource for deciphering the genome. Nucleic Acids Res 32:D277–D280
Croft D, O’Kelly G, Wu G et al (2011) Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res 39:D691–D697
Van Iersel MP, Kelder T, Pico AR et al (2008) Presenting and exploring biological pathways with PathVisio. BMC Bioinformatics 9:399
Bindea G, Mlecnik B, Hackl H et al (2009) ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25:1091–1093
Maere S, Heymans K, Kuiper M (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21:3448–3449
Eden E, Navon R, Steinfeld I et al (2009) GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10:48
Berriz GF, King OD, Bryant B et al (2003) Characterizing gene sets with FuncAssociate. Bioinformatics 19:2502–2504
Warde-Farley D, Donaldson SL, Comes O et al (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38:W214–W220
Bauer-Mehren A, Rautschka M, Sanz F et al (2010) DisGeNET: a Cytoscape plugin to visualize, integrate, search and analyze gene-disease networks. Bioinformatics 26:2924–2926
Merico D, Isserlin R, Stueker O et al (2010) Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 5:e13984
Yang L, Walker JR, Hogenesch JB et al (2008) NetAtlas: a Cytoscape plugin to examine signaling networks based on tissue gene expression. In Silico Biol 8:47–52
Doncheva NT, Assenov Y, Domingues FS et al (2012) Topological analysis and interactive visualization of biological networks and protein structures. Nat Protoc 7:670–685
Razick S, Mora A, Michalickova K et al (2011) iRefScape. A Cytoscape plug-in for visualization and data mining of protein interaction data from iRefIndex. BMC Bioinformatics 12:388
Gao J, Ade AS, Tarcea VG et al (2009) Integrating and annotating the interactome using the MiMI plugin for cytoscape. Bioinformatics 25:137–138
Srivas R, Hannum G, Ruscheinski J et al (2011) Assembling global maps of cellular function through integrative analysis of physical and genetic networks. Nat Protoc 6:1308–1323
Avila-Campillo I, Drew K, Lin J et al (2007) BioNetBuilder: automatic integration of biological networks. Bioinformatics 23:392–393
Martin A, Ochagavia ME, Rabasa LC et al (2010) BisoGenet: a new tool for gene network building, visualization and analysis. BMC Bioinformatics 11:91
Prifti E, Zucker J-D, Clement K et al (2008) FunNet: an integrative tool for exploring transcriptional interactions. Bioinformatics 24:2636–2638
Creek DJ, Jankevics A, Burgess KE et al (2012) IDEOM: an Excel interface for analysis of LC-MS-based metabolomics data. Bioinformatics 28:1048–1049
Clasquin MF, Melamud E, Rabinowitz JD (2012) LC-MS data processing with MAVEN: a metabolomic analysis and visualization engine. Curr Protoc Bioinformatics 37:14.11.1–14.11.23
Scheltema RA, Jankevics A, Jansen RC et al (2011) PeakML/mzMatch: a file format, Java library, R library, and tool-chain for mass spectrometry data analysis. Anal Chem 83:2786–2793
Chokkathukalam A, Jankevics A, Creek DJ et al (2013) mzMatch-ISO: an R tool for the annotation and relative quantification of isotope-labelled mass spectrometry data. Bioinformatics 29:281–283
Przytycka TM, Singh M, Slonim DK (2010) Toward the dynamic interactome: it’s about time. Brief Bioinform 11:15–29
Vidal M (2009) A unifying view of 21st century systems biology. FEBS Lett 583:3891–3894
Cusick ME, Klitgord N, Vidal M et al (2005) Interactome: gateway into systems biology. Hum Mol Genet 14:R171–R181
De Las Rivas J, Fontanillo C (2010) Protein–protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput Biol 6:e1000807
Ideker T, Ozier O, Schwikowski B et al (2002) Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18(1):S233–S240
Go GO (2011) The gene ontology. Genome 2009:1–13
Dominiczak AF, Herget-Rosenthal S, Delles C et al (2010) Systems biology to battle vascular disease. Nephrol Dial Transplant 25:1019–1022
Barabási A-L, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12:56–68
Müller F-J, Laurent LC, Kostka D et al (2008) Regulatory networks define phenotypic classes of human stem cell lines. Nature 455:401–405
Chowdhury SA, Koyutürk M (2010) Identification of coordinately dysregulated subnetworks in complex phenotypes. Pac Symp Biocomput. 133–144
Chowdhury SA, Nibbe RK, Chance MR et al (2011) Subnetwork state functions define dysregulated subnetworks in cancer. J Comput Biol 18:263–281
Dao P, Colak R, Salari R et al (2010) Inferring cancer subnetwork markers using density-constrained biclustering. Bioinformatics 26:i625–i631
Dao P, Wang K, Collins C et al (2011) Optimally discriminative subnetwork markers predict response to chemotherapy. Bioinformatics 27:i205–i213
Lee E, Chuang H-Y, Kim J-W et al (2008) Inferring pathway activity toward precise disease classification. PLoS Comput Biol 4:e1000217
Fechete R, Heinzel A, Perco P et al (2011) Mapping of molecular pathways, biomarkers and drug targets for diabetic nephropathy. Proteomics Clin Appl 5:354–366
Vidal M, Cusick ME, Barabási A-L (2011) Interactome networks and human disease. Cell 144:986–998
Suthram S, Dudley JT, Chiang AP et al (2010) Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. PLoS Comput Biol 6:e1000662
Chu L-H, Chen B-S (2008) Construction of a cancer-perturbed protein–protein interaction network for discovery of apoptosis drug targets. BMC Syst Biol 2:56
Magrane M, Consortium U (2011) UniProt Knowledgebase: a hub of integrated protein data. Database (Oxford) 2011:bar009
Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57
Smedley D, Haider S, Ballester B et al (2009) BioMart: biological queries made easy. BMC Genomics 10:22
Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–24504
Kerrien S, Aranda B, Breuza L et al (2012) The IntAct molecular interaction database in 2012. Nucleic Acids Res 40:D841–D846
Stark C, Breitkreutz B-J, Chatr-Aryamontri A et al (2011) The BioGRID Interaction Database: 2011 update. Nucleic Acids Res 39:D698–D704
Isserlin R, El-Badrawi RA, Bader GD (2011) The biomolecular interaction network database in PSI-MI 2.5. Database (Oxford) 2011:baq037
Szklarczyk D, Franceschini A, Kuhn M et al (2011) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39:D561–D568
Pruitt KD, Tatusova T, Brown GR et al (2012) NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy. Nucleic Acids Res 40:D130–D135. doi:10.1093/nar/gkr1079
Xenarios I, Salwínski L, Duan XJ et al (2002) DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res 30:303–305
Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29. doi:10.1038/75556
Huang DW, Sherman BT, Tan Q et al (2007) The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol 8:R183
Acknowledgments
This work was supported in part by the Marie Curie Actions—BCMolMed (FP7-PEOPLE-2012-ITN-EID).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media New York
About this protocol
Cite this protocol
Bhat, A., Dakna, M., Mischak, H. (2015). Integrating Proteomics Profiling Data Sets: A Network Perspective. In: Vlahou, A., Makridakis, M. (eds) Clinical Proteomics. Methods in Molecular Biology, vol 1243. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1872-0_14
Download citation
DOI: https://doi.org/10.1007/978-1-4939-1872-0_14
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-1871-3
Online ISBN: 978-1-4939-1872-0
eBook Packages: Springer Protocols