In conclusion, metabolic network reconstruction and simulation can be effectively used to understand how an organism or parasite functions inside of the host cell. For example, if the parasite serves to compromise the immune system by lysing macrophages, then the goal of metabolic reconstruction/simulation would be to determine the metabolites that are essential to the organism's proliferationinside of macrophages. If the proliferation cycle is inhibited, then the parasite would not continue to evade the host's immune system. A reconstruction model serves as a first step to deciphering the complicated mechanisms surrounding disease. The next step would be to use the predictions and postulates generated from a reconstruction model and apply it to drug delivery and drug-engineering techniques.
Currently, many tropical diseases affecting third world nations are very inadequately characterized, and thus poorly understood. Therefore, a metabolic reconstruction and simulation of the parasites that cause the tropical diseases would aid in developing new and innovative cures and treatments.
See also
• Metabolic network
• Computer simulation
• Computational systems biology
• Metabolic pathway
• Metagenomics
• Metabolic control analysis
References
1. Francke, C., Siezen, R. J. and Teusink, B. (2005). Reconstructing the metabolic network of a bacterium from its genome. Trends in Microbiology. 13(11): 550-558.
2. Merriam Webster's Medical Dictionary. (2002). http://dictionary.reference.com/ medical/
3. Papin, J.A., Price, N.D., and Palsson, B.O. (2002). Extreme Pathway Lengths and Reaction Participation in Genome-Scale Metabolic Networks. Genome Research. 12: 1889-1900.
4. Papin, J.A., Stelling, J., Price, N.D., Klamt, S., Schuster, S., and Palsson, B.O. (2004). Comparison of network-based pathway analysis methods. Trends in Biotechnology. 22(8): 400-405.
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5. Price, N.D., Reed, J.L., Papin, J.A., Wiback, S.J., and Palsson, B.O. (2003). Network-based analysis of metabolic regulation in the human red blood cell. Journal of Theoretical
Biology. 225: 185-194.
6. Schuster, S., Fell, D.A. and Dandekar, T. (2000). A general definition of metabolic
pathways useful for systematic organization and analysis of complex metabolic networks. Nature Biotechnology. 18: 326-332.
7. SRI International. (2005). Pathway Tools Information Site. http://bioinformatics.ai.sri. com/ptools/
8. Stelling, J., Klamt, S., Bettenbrock, K., Schuster, S. and Gilles, E.D. (2002). Metabolic network structure determines key aspects of functionality and regulation. Nature. 420: 190-193.
9. Larhlimi, A., Bockmayr, A. (2008) A new constraint-based description of the steady-state flux cone of metabolic networks. Discrete Applied Mathematics.
doi:10.1016/j.dam.2008.06.039[5]
External links
• GeneDB[6]
• KEGG[7]
• PathCase[8]Case Western Reserve University
• BRENDA[9]
• BioCyc[10]and Cyclone[11]- provides an open source Java API to the pathway tool BioCyc to extract Metabolic graphs.
• EcoCyc[12]
• MetaCyc[13]
• ENZYME[14]
• SBRI Bioinformatics Tools and Software[15]
• TIGR[16]
• Pathway Tools[17]
• Stanford Genomic Resources[18]
• Pathway Hunter Tool[19]
• IMG[20]The Integrated Microbial Genomes system, for genome analysis by the DOE-JGI.
• Systems Analysis, Modelling and Prediction Group[21]at the University of Oxford, Biochemical reaction pathway inference techniques.
References
[1] http://www.genome.ad.jp [2] http://www.genedb.org[3] http://bioinformatics.ai.sri.com/ptools/ [4] http://ca.expasy.org/
[5] http://dx.doi.org/10.1016%2Fj.dam.2008.06.039 [6] http://www.genedb.org/
[7] http://www.genome.ad.jp/
[8] http://nashua.case.edu/pathwaysweb [9] http://www.brenda.uni-koeln.de/ [10] http://www.biocyc.org/
[11] http://nemo-cyclone.sourceforge.net [12] http://ecocyc.org/
[13] http://metacyc.org/
Metabolic network modelling 83
[15] http://apps.sbri.org/Genome/Link/Bioinformatics_Tools_Software.aspx/ [16] http://www.jcvi.org
[17] http://bioinformatics.ai.sri.com/ptools/ [18] http://genome-www.stanford.edu/ [19] http://pht.tu-bs.de/
[20] http://img.jgi.doe.gov/
[21] http://www.eng.ox.ac.uk/samp
Protein-protein interaction
Protein-protein interactions involve not only the direct-contact association of protein molecules but also longer range interactions through the electrolyte, aqueous solution medium surrounding neighbor hydrated proteins over distances from less than one nanometer to distances of several tens of nanometers. Furthermore, such protein-protein interactions are thermodynamically linked functions[1]of dynamically bound ions and water that exchange rapidly with the surrounding solution by comparison with the molecular tumbling rate (or correlationtimes) of the interacting proteins. Protein associations are also studied from the perspectives of biochemistry, quantum chemistry, molecular dynamics, signal transduction and other metabolic or genetic/epigenetic networks. Indeed, protein-protein interactions are at the core of the entire Interactomics system of any living cell.
The interactions between proteins are important for very numerous—if not all—biological functions. For example, signals from the exterior of a cellare mediated to the inside of that cell by protein-protein interactions of the signaling molecules. This process, called signal transduction, plays a fundamental role in many biological processes and in many diseases (e.g. cancers). Proteins might interact for a long time to form part of a protein complex, a protein may be carrying another protein (for example, from cytoplasm to nucleus or vice versa in the case of the nuclear pore importins), or a protein may interact briefly with another protein just to modify it (for example, a protein kinase will add a phosphate to a target protein). This modification of proteins can itself change protein-protein interactions. For example, some proteins with SH2 domains only bind to other proteins when they are phosphorylated on the amino acid tyrosine while bromodomains specifically recognise acetylated lysines. In conclusion, protein-protein interactions are of central importance for virtually every process in a living cell. Information about these interactions improves our understanding of diseases and can provide the basis for new therapeutic approaches.