Systematic Analysis of Signaling Pathways Using an Integrative Environment

Summary

Introduction: Understanding the biological processes of signaling pathways as a whole system requires an integrative software environment that has comprehensive capabilities. The environment should include tools for pathway design, visualization, simulation and a knowledge base concerning signaling pathways as one. In this paper we introduce a new integrative environment for the systematic analysis of signaling pathways.

Methods: This system includes environments for pathway design, visualization, simulation and a knowledge base that combines biological and modeling information concerning signaling pathways that provides the basic understanding of the biological system, its structure and functioning. The system is designed with a client-server architecture. It contains a pathway designing environment and a simulation environment as upper layers with a relational knowledge base as the underlying layer.

Results: The TNFα-mediated NF-κB signal transduction pathway model was designed and tested using our integrative framework. It was also useful to define the structure of the knowledge base. Sensitivity analysis of this specific pathway was performed providing simulation data. Then the model was extended showing promising initial results.

Conclusion: The proposed system offers a holistic view of pathways containing biological and modeling data. It will help us to perform biological interpretation of the simulation results and thus contribute to a better understanding of the biological system for drug identification.

Keywords

Databases - modeling - signaling - pathways - simulation and analysis

• References

• 1 Westerhoff HV, Palsson BO. The evolution of molecular biology into systems biology. Nat Biotech J 2004; 22: 1249-52.
• 2 Sax U, Schmidt S. Integration of Genomic Data in Electronic Health Records – Opportunities and Dilemmas. Methods Inf Med 2005; 44: 546-50.
• 3 Baumgartner C, Mátyás G, Steinmann B, Baumgartner D. Marfan syndrome: A diagnostic challenge caused by phenotypic and genetic heterogeneity. Methods Inf Med 2005; 44: 487-97.
• 4 Cannataro M, Cuda G, Veltri P. Modeling and Designing a Proteomics Application on PROTEUS. Methods Inf Med 2005; 44: 221-6.
• 5 Hucka M, Finney A, Sauro HM, Bolouri H, Doyle J, Kitano H. The Erato Systems Biology Workbench: Enabling integration and exchanging between software tools for computational biology. Proc of the Pacific Bio Computing Symposium. 2002
• 6 Bhalla US, Iyengar R. Emergent properties of networks of biological signaling pathways. Sciences 1999; 283: 381-7.
• 7 Sivakumaran S, Hariharaputran S, Mishra J, Bhalla US. The database of quantative cellular signaling. Bioinformatics 2003; 19: 405-15.
• 8 Medes P. Biochemistry by numbers: Simulation of biochemical pathways with Gepasi 3. Trends in Biochemical Sciences 1997; 22: 361-3.
• 9 Sauro HM. Jarnac: A system for integrative metabolic analysis. Proc of the 9th Int Meeting on Bio Thermo Kinetics.. Stellenbosch University Press; 2000
• 10 Bray D, Firth C, Levere N, Shimizu T. Stoch Sim. www.zoo.cam.ac.uk/compcell/StochSim.html 2001
• 11 Schaff J, Slepchenko B, Loew LM. Physiological modeling with Virtual Cell framework. Methods in Enzymology 2000; 321: 1-23.
• 12 Demir E, Babur O, Dogrusoz U, Gurosy A, Nisanci G, Cetin-Atalay R, Oztruk M. Patika: an integrated visual environment for collaborative construction and analysis of cellular pathways. Bioinformatics 2002; 18: 966-1003.
• 13 Gray DB, Christopher WVH. BIND – a data specification for storing and describing bimolecular interactions, molecular complexes and pathways. Bioinformatics 2000; 16: 465-77.
• 14 Han DSH, Seo KJ, Jang W. A domain combination based probabilistic framework for protein-protein interaction prediction. Genome Informatics 2003; 14: 250-9.
• 15 Reshetnikov V, Karpov A, Pot D, Tereshchenko F. Vector Path Blazer: A New Pathway Analysis and Visualization Tool. http://www.iscb.org/ismb2003/posters/feodorATinformaxinc.com_468.html.
• 16 Xenarios I, Rice DW, Salwinski L, Baron MK, Marcotte EM, Eisenberg D. DIP: The Database of interacting proteins. A search tool for studying cellular networks of protein interactions. Nucleic Acid Res 2002; 30: 303-5.
• 17 Hoebeke M, Chiapello H, Noirot P, Bessières P. SPiD: a subtilis protein interaction database. Bioinformatics 2001; 17: 1209-12.
• 18 Kuroda S. et al. Exploration of signal transduction in cerebellar long-term depression by kinetic simulation. J Neurosci 2001; 21: 5693-702.
• 19 Cho KH, Wolkenhauer O. Experimental Design in Systems Biology Based on Parameter Analysis with Monte Carlo Simulation: A Case Study for TNFα Mediated NF-κB Signal Transduction Pathway. Simulation 2003; 79: 726-39.
• 20 Cho KH, Wolkenhauer O. Analysis and modeling of signal transduction pathways in systems biology. J Biochemical society Transactions 2003; 31: 1503-9.
• 21 Achard F. et al. XML-bioinformatics and data integration. Bioinformatics 2000; 17: 115-25.
• 22 Kolpakov FA. BIOUML – Framework for visual modeling and simulation biological systems. Proc Int Conf Bioinformatics of Genome Regulations and structures. 2002
• 23 Shannon P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13: 2498-2504.
• 24 Hucka M. et al. Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project. IEE 2004; 01: 41-53.
• 25 BioPAX Group. BioPAX data exchange ontology for biological pathway databases. http://www.iscb.org/ismb2003/posters/jlucianoATbiopathways.org_396.html.