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Warianty tytułu
Metabolic modelling in the era of biological engineering
Języki publikacji
Abstrakty
Współczesna inżynieria metaboliczna oparta jest na narzędziach biologii systemów i biologii syntetycznej. Modelowanie matematyczne odgrywa istotną rolę w badaniach związanych z modyfikacją sieci metabolicznej, ponieważ pozwala w sposób ilościowy analizować system biologiczny. Analiza bilansu strumieni pozwala na wyznaczenie wartości strumieni w sieci poprzez maksymalizację biologicznej funkcji celu. Algorytmy inżynierii metabolicznej in silico oparte są na procedurach optymalizacji.
Modern metabolic engineering is based on the tools of systems biology and synthetic biology. Mathematical modelling plays an important role in the research related to metabolic network modification, because it enables one to analyze the biological system in a quantitative manner. The flux balance analysis enables one to determine flux values in the network by maximizing the biological objective function. Algorithms of in silico metabolic engineering are based on optimization procedures.
Czasopismo
Rocznik
Tom
Strony
89--91
Opis fizyczny
Bibliogr. 26 poz.
Twórcy
autor
autor
autor
- Katedra Inżynierii Bioprocesowej, Wydział Inżynierii Procesowej i Ochrony Środowiska, Politechnika Łódzka, Łódź, tomaszboruta85@gmail.com
Bibliografia
- Bailey J.E., 1991. Toward a science of metabolic engineering. Science, 252, nr 5013, 1668-1675. DOI:10.1126/science.2047876
- Becker J., Wittmann C., 2012. Systems and synthetic metabolic engineering for amino acid production – the heartbeat of industrial strain development. Current Opinion in Biotechnology, 23, 1-9. DOI: 10.1016/j.copbio.2011.12.025
- Burgard A.P., Pharkya P., Maranas C.D., 2003. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and Bioengineering, 84, nr 6, 647-657. DOI: 10.1002/bit.10803
- Chen J., Densmore D., Ham T.S., Keasling J.D., Hillson N.J., 2012. Device Editor visual biological CAD canvas. Journal of Biological Engineering, 6, nr 1. DOI: 10.1186/PREACCEPT-1967068768635731
- Edwards J.S., Palsson B.O., 2000. The Escherichia coli MG1655 in silico metabolic genotype: its defi nition, characteristics, and capabilities. Proceedings of the National Academy of Sciences of the United States of America, 97, nr 10, 5528–5533. DOI: 10.1073/pnas.97.10.5528
- Galdzicki M., Rodriguez C., Chandran D., Sauro H.M., Gennari J.H., 2011. Standard Biological Parts Knowledgebase. PLoS ONE, 6, nr 2, e17005. DOI: 10.1371/journal.pone.0017005
- Gibson D.G., Glass J.I., Lartigue C., Noskov V.N., Chuang R.Y., Algire M.A., Benders G.A., Montague M.G., Ma L., Moodie M.M., Merryman C., Vashee S., Krishnakumar R., Assad-Garcia N., Andrews-Pfannkoch C., Denisova E.A., Young L., Qi Z.Q., Segall-Shapiro T.H., Calvey C.H., Parmar P.P., Hutchison C.A. 3rd, Smith H.O., Venter J.C., 2010. Creation of a bacterial cell controlled by a chemically synthesized genome. Science, 329, nr 5987, 52-56. DOI: 10.1126/science.1190719
- Hoppe A., Hoffmann S., Holzhutter H.G., 2007. Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks. BMC Systems Biology, 1, nr 23. DOI:10.1186/1752-0509-1-23
- Jensen P.A., Papin J.A., 2011. Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics, 27, nr 4, 541-547. DOI: 10.1093/bioinformatics/btq702
- Keasling J.D., 2010. Manufacturing molecules through metabolic engineering. Science, 330, nr 6009, 1355-1358. DOI: 10.1126/science.1193990
- Kim I.K., Roldao A., Siewers V., Nielsen J., 2012. A systems-level approach for metabolic engineering of yeast cell factories. FEMS Yeast Research, 12, nr 2, 228-248. DOI: 10.1111/j.1567-1364.2011.00779.x
- Marchisio M.A., Stelling J., 2011. Automatic design of digital synthetic gene circuits. PLoS Computational Biology, 7, nr 2, e1001083. DOI: 10.1371/journal. pcbi.1001083
- Marchisio M.A., 2012. In silico implementation of synthetic gene networks. Methods in Molecular Biology, 813, nr 1, 3-21. DOI: 10.1007/978-1-61779- 412-4_1
- Metzker M.L., 2010. Sequencing technologies – the next generation. Nature Reviews Genetics, 11, nr 1, 31-46. DOI:10.1038/nrg2626
- Nandagopal N., Elowitz M.B., 2011. Synthetic biology: integrated gene circuits. Science, 333, nr 6047, 1244-1248. DOI: 10.1126/science.1207084
- Nielsen J., 2012. Translational and systems medicine. Journal of Internal Medicine, 271, nr, 108-110. DOI: 10.1111/j.1365-2796.2011.02490.x
- Orth J.D., Conrad T.M., Na J., Lerman J.A., Nam H., Feist A.M., Palsson B.O., 2011. A comprehensive genome-scale reconstruction of Escherichia coli metabolism--2011. Molecular Systems Biology, 7, nr 535. DOI:10.1038/ msb.2011.65
- Orth J.D., Thiele I., Palsson B.O., 2010. What is fl ux balance analysis? Nature Biotechnology, 28, nr 3, 245-248. DOI:10.1038/nbt.1614
- Park J.M., Kim T.Y., Lee S.Y., 2009. Constraints-based genome-scale metabolic simulation for systems metabolic engineering. Biotechnology Advances, 27, nr 6, 979–988. DOI: 10.1016/j.biotechadv.2009.05.019
- Patil K.R., Rocha I., Forster J, Nielsen J., 2005. Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics, 6, nr 308. DOI:10.1186/1471-2105-6-308
- Pharkya P., Burgard A.P., Maranas C.D., 2004. OptStrain: a computational framework for redesign of microbial production systems. Genome Research, 14, nr 11, 2367-2376. DOI: 10.1101/gr.2872004
- Pharkya P., Maranas C.D., 2006. An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metabolic Engineering, 8, nr 1, 1-13. DOI: 10.1016/ j.ymben.2005.08.003
- Segre D., Vitkup D., Church G.M., 2002. Analysis of optimality in natural and perturbed metabolic networks. Proceedings of the National Academy of Sciences of the United States of America, 99, nr 23, 15112-15117. DOI: 10.1073/pnas.232349399
- Shlomi T., Eisenberg Y., Sharan R., Ruppin E., 2007. A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Molecular Systems Biology, 3, nr 101. DOI: 10.1038/msb410014
- Thiele I., Palsson B.O., 2010. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature Protocols, 5, nr 1, 93–121. DOI:10.1038/nprot.2009.203
- Zhang W., Li F., Nie L., 2010. Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies. Microbiology, 156, nr 2, 287-301. DOI: 10.1099/mic.0.034793-0
Typ dokumentu
Bibliografia
Identyfikator YADDA
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