Abstract
Integrative Bioinformatics deals with the development of methods and tools to solve biological problems as well as providing a better understanding or new knowledge about biochemical phenomena by means of data integration and computational experiments [7]. Current high-throughput technologies such as NMR, mass spectrometry, protein/DNA chips, gel electrophoresis data, Yeast Two-Hybrid, QTL mapping, and NGS generate large quantities of high-throughput data. The challenge of Integrative Bioinformatics is to capture, model, simulate, integrate, and analyze this huge amount of data in addition to the data represented by hundreds of biological databases and thousands of scientific journals. The data needs to be integrated and made available in a consistent way to provide new and deeper insights into complex biological systems. Molecular biology produces this volume of data based on high-throughput technologies. One characteristic of this data is exponential growing. Therefore, storing and analysis of this molecular and cellular data essentially uses methods and concepts of Bioinformatics. Currently, there are more than 2,000 database and information systems available via the Internet, which represent this molecular data. Every year new molecular databases and information systems which can be used via the Internet crop up. The classical definition of an information system is based on a database system which represents the data and tools for the user-specific analysis of this data. Today an information system is or can be embedded into the Internet as shown in Fig. 1.1.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alla H, David R (1998) Continuous and hybrid Petri nets. J Circuit Syst Comput 8(1):159–188
Allen HD (2001) Reconstruction of metabolic pathways by the exploration of gene expression data with factor analysis. Dissertation, Virginia Polytechnic Institute and State University, Blacksburg, VA
Arita M (2000) Metabolic reconstruction using shortest paths. Simulat Pract Theory 8:109–125
Bansal AK (2000) A framework of automated reconstruction of microbial metabolic pathways. In: Proceedings of the IEEE international symposium on bio-informatics and biomedical engineering, Arlington, VA, 8–11 November, pp 184–190
Boyer F, Viari A (2003) An initio reconstruction of metabolic pathways. In: ECCB’2003 (European conference on computational biology), 27–30 September, Paris
Chen M, Hofestädt R (2003) Quantitative Petri net model of gene regulated metabolic networks in the cell. In Silico Biol 3(3):347–365
Collado-Vides J, Hofestädt R (2002) Gene regulation and metabolism – post genomic computational approaches. MIT Press, Cambridge, MA
Dandekar T, Schuster S, Snel B (1999) Pathway alignment: application to the comparative analysis of glycolytic enzymes. Biochem J 1:115–124
Forst CV, Schulten K (1999) Evolution of metabolisms: a new method for the comparison of metabolic pathways using genomics information. J Comput Biol 6:343–360
Haas LM, Schwarz PM, Kodali P, Kotlar E, Rice JE, Swope WC (2001) DiscoveryLink: a system for integrated access to life sciences data sources. IBM Syst J 40:489–511
Hofestädt R (ed) (2005) Yearbook bioinformatics 2004. IMBio, Informations management in der Biotechnologie e.V, Magdeburg
McShan DC, Rao S, Shah I (2003) PathMiner: predicting metabolic pathways by heuristic search. Bioinformatics 19(13):1692–1698
Paley S, Karp PD (2002) Evaluation of computational metabolic pathway predictions for Helicobacter pylori. Bioinformatics 18(5):715–724
Pinter RY, Rokhlenko O, Yeger-Lotem E et al (2005) Alignment of metabolic pathways. Bioinformatics 21(16):3401–3408
Schaftenaar G, Cuelenaere K, Noordik JH, Etzold T (1996) A Tcl-based SRS v. 4 interface. Comput Appl Biosci 12(2):151–155
Siepel A, Farmer A, Tolopko A, Zhuang M, Mendes P, Beavis W, Sobral B (2001) ISYS: a decentralized, component-based approach to the integration of heterogeneous bioinformatics resources. Bioinformatics 17:83–94
Sommer B, Ivanisenko V, Arrigo P, Hofestädt R (2012) Prediction and 3D visualization of biological networks using cytological disease mapping. EMBnet J 18(Suppl B):115–116
Stevens R, Baker P, Bechhofer S, Ng G, Jacoby A, Paton NW, Goble CA, Brass A (2000) TAMBIS: transparent access to multiple bioinformatics information sources. Bioinformatics 16:184–185
Tatusova TA, Karsch-Mizrachi L, Ostell JA (1999) Complete genomes in WWW Entrez: data representation and analysis. Bioinformatics 15:536–543
Davidson SB, Overton C, Tannen V, Wong L (1997) BioKleisli: a digital library for biomedical researchers. Int J Digit Libr 1:36–53
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
WWW-List of Selected Molecular Information Systems
WWW-List of Selected Molecular Information Systems
Genes
-
EMBL – http://www.ebi.ac.uk/
-
The “EMBL Nucleotide Sequence Database” represents all known DNA and RNA sequences.
-
GenBank – http://www.ncbi.nih.gov/Genbank/
-
NIH genetic sequence database.
-
HGMD represents the Human Gene Mutation Database.
Proteins and Enzymes
-
ENZYME – http://www.expasy.org/enzyme/
-
ENZYME is a repository of information relative to the nomenclature of enzymes.
-
LIGAND – http://www.genome.ad.jp/ligand/
-
The Ligand Chemical Database for Enzyme Reactions is linking chemical and biological aspects of life in the light of enzymatic reactions.
-
PDB – http://www.rcsb.org/pdb/
-
PDB presents 3D macromolecular structure data primarily determined experimentally by X-ray crystallography and NMR.
-
This database is a comprehensive, annotated, and nonredundant set of protein sequence databases in which entries are classified into family groups and where alignments of each group are available.
-
PROSITE – http://www.expasy.ch/prosite/
-
PROSITE is a database of protein families and domains.
-
REBASE – http://rebase.neb.com/
-
Restriction Enzyme data BASE is a collection of information about restriction enzymes.
-
SWISSPROT – http://www.expasy.org/sprot/
-
Protein sequence database.
Pathways
-
CSNDB – http://geo.nihs.go.jp/
-
The Cell Signaling Networks DataBase (CSNDB) is a data and knowledge base for signaling pathways of human cells.
-
KEGG – http://www.genome.ad.jp/
-
The Kyoto Encyclopedia of Genes and Genomes represents information of pathways that consist of interacting molecules or genes.
Gene Regulation
-
TRANSFAC – http://www.biobase.de
-
This database presents data about gene regulatory DNA sequences.
-
TRRD – http://www.bionet.nsc.ru/
-
Transcription Regulatory Regions Database.
Metabolic Diseases
-
The Online Mendelian Inheritance in Man database is a catalogue of human genes and genetic disorders authored and edited by Dr. Victor A. McKusick and his colleagues.
-
PATHWAY – http://oxmedinfo.jr2.ox.ac.uk/
-
PATHWAY is a database of inherited metabolic diseases. The database is divided into two sections: substances and diseases.
-
PEDBASE – http://www.icondata.com/health/pedbase/
-
PEDBASE is a database of pediatric disorders. Entries are listed alphabetically by disease or condition name.
-
The Rare Disease Database is a delivery system for understandable medical information to the public, including patients, families, physicians, medical institutions, and support organizations.
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Chen, M., Hofestädt, R. (2014). Integrative Bioinformatics. In: Chen, M., Hofestädt, R. (eds) Approaches in Integrative Bioinformatics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41281-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-41281-3_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41280-6
Online ISBN: 978-3-642-41281-3
eBook Packages: Computer ScienceComputer Science (R0)