In Silico Analysis of the Feasibility of Mutation of Biological Systems
Keywords:
SYSTEMS BIOLOGY, SOFTWARE, MUTATION.Abstract
Introduction: in Bioinformatics the goal of the researchers involves the achievement of computerize models for biological processes that approach the real behavior, all this inserted in a discipline known as Biology Systems. In silico analysis in this area of the biological sciences relies on the use of mathematical-computerize methods. Within the analysis in biological systems it is of interest to be able to compare the structure of different organisms. In this comparison, the metabolism of different organisms will be taken into account, as well as the topology of their associated metabolic network. This comparison serves to select the species or to strain the best that suits a particular application.
Objective: to analyze the viability of the mutation of a biological system to assume the functions of another.
Method: BioOpt computer systems were used for this analysis, which focuses on the analysis of the flow balance, using linear programming as a mathematical support, and CompNet, which compares metabolic networks according to several metrics of similarity and the necessary transformations to take from one metabolic network to another.
Results: a comparison was performed between two biological systems, being able to determine the essential reactions within the metabolism of these organisms and from them which ones had to be modified and which ones eliminated to achieve the mutation from one organism to another.
Conclusions: this work shows the in silico analysis that helps to determine whether it is practicable or not to perform the genetic manipulation of an organism to assume functions that are defined in another.
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1. Raies AB, Bajic VB. In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdisciplinary Reviews: Computational Molecular Science [Internet]. 2016 [citado 2017 Dic 24]; 6(2): 147-72. Disponible en: http://onlinelibrary.wiley.com/doi/10.1002/wcms.1240/full.
2. Wolstencroft K, Owen S, Krebs O, Nguyen Q, Stanford NJ, Golebiewski M, et al. SEEK: a systems biology data and model management platform. BMC systems biology [Internet]. 2015[citado 2017 Dic 24]; 9(1): 33. Disponible en: https://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-015-0174-y.
3. Baltes NJ, Voytas DF. Enabling plant synthetic biology through genome engineering. Trends in biotechnology [Internet]. 2015 [citado 2017 Dic 24]; 33(2): 120-31. Disponible en: http://www.cell.com/trends/biotechnology/fulltext/S0167-7799(14)00237-6?_returnURL=http%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0167779914002376%3Fshowall%3Dtrue.
4. Castilla Guerra J. La autorización de los organismos genéticamente modificados [Tesis]. España: Universidad de Sevilla; 2016. Disponible en : https://idus.us.es/xmlui/bitstream/handle/11441/39186/TESIS.%20Autorizaci%c3%b3nOMG..pdf?sequence=1&isAllowed=y.
5. Infante RAJ. Empleo de las TIC´s en el aprovechamiento de la biomasa forestal en Pinar del Río. II Congreso Internacinal de Marketimg Desarrllo Local y Turismo MARDELTUR 2017; Pinar Del Río. 2017. Disponible en: https://www.responsibletravel.org/docs/MARDELTUR2017.pdf
6. Baláž V, Koča J, Kvasnička V, Sekanina M. A metric for graphs. Časopis pro pěstování matematiky [Internet]. 1986 [citado 2017 Dic 24]; 114(2): 255-9. Disponible en: https://dml.cz/bitstream/handle/10338.dmlcz/108715/CasPestMat_114-1989-2_5.pdf
7. Bunke H, Shearer K. A graph distance metric based on the maximal common subgraph. Pattern recognition letters [Internet]. 1998 [citado 2017 Dic 24]; 19(3): 255-9. Disponible en: http://biomet-toolbox.chalmers.se/index.php?page=downtools-bioOpt.
8. BioOpt; 2017. Disponible en: http://biomet-toolbox.chalmers.se/index.php?page=downtools-bioOpt
9. Edwards J, Ramakrishna R, Schilling C, Palsson B. Metabolic flux balance analysis. BIOPROCESS TECHNOLOGY. 1997; 24:13-58.
10. Komi N, Xingxing J, Xinkai Z, Liujing W, Jiajia L, Catherine M, Qiang H. Flux Balance Analysis Inspired Bioprocess Upgrading for Lycopene Production by a Metabolically Engineered Strain of Yarrowia lipolytica Metabolites [Internet]. 2015 Dec [citado 2017 Dic 24]; 5(4): 794–813. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4693195/pdf/metabolites-05-00794.pdf
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