Literatura académica sobre el tema "Protein binding – Mathematical models"
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Artículos de revistas sobre el tema "Protein binding – Mathematical models"
Palacio-Castañeda, Valentina, Simon Dumas, Philipp Albrecht, Thijmen J. Wijgers, Stéphanie Descroix y Wouter P. R. Verdurmen. "A Hybrid In Silico and Tumor-on-a-Chip Approach to Model Targeted Protein Behavior in 3D Microenvironments". Cancers 13, n.º 10 (18 de mayo de 2021): 2461. http://dx.doi.org/10.3390/cancers13102461.
Texto completoMiddendorf, Thomas R. y Richard W. Aldrich. "Structural identifiability of equilibrium ligand-binding parameters". Journal of General Physiology 149, n.º 1 (19 de diciembre de 2016): 105–19. http://dx.doi.org/10.1085/jgp.201611702.
Texto completoPremarathna, Galkande Iresha y Leif Ellingson. "A mathematical representation of protein binding sites using structural dispersion of atoms from principal axes for classification of binding ligands". PLOS ONE 16, n.º 4 (8 de abril de 2021): e0244905. http://dx.doi.org/10.1371/journal.pone.0244905.
Texto completoRuan, Shuxiang y Gary D. Stormo. "Inherent limitations of probabilistic models for protein-DNA binding specificity". PLOS Computational Biology 13, n.º 7 (7 de julio de 2017): e1005638. http://dx.doi.org/10.1371/journal.pcbi.1005638.
Texto completoSedaghat, Ahmad R., Arthur Sherman y Michael J. Quon. "A mathematical model of metabolic insulin signaling pathways". American Journal of Physiology-Endocrinology and Metabolism 283, n.º 5 (1 de noviembre de 2002): E1084—E1101. http://dx.doi.org/10.1152/ajpendo.00571.2001.
Texto completoKimchi, Ofer, Carl P. Goodrich, Alexis Courbet, Agnese I. Curatolo, Nicholas B. Woodall, David Baker y Michael P. Brenner. "Self-assembly–based posttranslational protein oscillators". Science Advances 6, n.º 51 (diciembre de 2020): eabc1939. http://dx.doi.org/10.1126/sciadv.abc1939.
Texto completoWang, Debby D., Haoran Xie y Hong Yan. "Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity". Bioinformatics 37, n.º 17 (27 de febrero de 2021): 2570–79. http://dx.doi.org/10.1093/bioinformatics/btab132.
Texto completoConradi Smith, Gregory Douglas. "Allostery in oligomeric receptor models". Mathematical Medicine and Biology: A Journal of the IMA 37, n.º 3 (10 de diciembre de 2019): 313–33. http://dx.doi.org/10.1093/imammb/dqz016.
Texto completoJiang, Yao, Hui-Fang Liu y Rong Liu. "Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions". PLOS Computational Biology 17, n.º 4 (19 de abril de 2021): e1008951. http://dx.doi.org/10.1371/journal.pcbi.1008951.
Texto completoSohrabi-Jahromi, Salma y Johannes Söding. "Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins". Bioinformatics 37, Supplement_1 (1 de julio de 2021): i308—i316. http://dx.doi.org/10.1093/bioinformatics/btab300.
Texto completoTesis sobre el tema "Protein binding – Mathematical models"
Sidiqi, Mahjooba. "The structure and RNA-binding of poly (C) protein 1". University of Western Australia. School of Biomedical, Biomolecular and Chemical Sciences, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0077.
Texto completoGeli, Rolfhamre Patricia. "From penicillin binding proteins to community interventions : mathematical and statistical models related to antibiotic resistance /". Stockholm : Department of Mathematics, Stockholm University, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-8477.
Texto completoRoussel, Céline. "Etude du rôle des chélateurs calciques sur les oscillations du potentiel membranaire neuronal: approche expérimentale et théorique". Doctoral thesis, Universite Libre de Bruxelles, 2006. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210854.
Texto completoAu niveau théorique, nous avons élaboré un modèle mathématique de l’activité électrique du grain cérébelleux, prenant en compte la chélation du calcium intracellulaire. Il permet de clarifier le rôle de la chélation du calcium intracellulaire sur les oscillations du potentiel membranaire. La modélisation de l’activité électrique du grain cérébelleux repose sur le formalisme développé par Hodgkin et Huxley pour l’axone géant de calmar. Dans ce contexte, l’application de la conservation de la charge au circuit équivalent de la membrane cellulaire fournit un système d’équations différentielles ordinaires, non linéaires. Dès lors, notre modèle nous a permis d’étudier l’impact des variations de la concentration de chélateur calcique sur les oscillations du potentiel membranaire. Nous avons ainsi pu constater qu’une diminution de la concentration en chélateur calcique induisait une augmentation de l’excitabilité électrique du grain cérébelleux, sans altérer le régime d’oscillations. Par contre, en augmentant fortement la concentration en chélateur calcique, nous avons montré que le grain cérébelleux changeait de dynamique oscillatoire, montrant des transitions d’un mode de décharge périodique régulier vers des oscillations en salve du potentiel membranaire.
Au niveau expérimental, nous avons vérifié les résultats prévus par le modèle théorique. Nous avons ainsi montré que des grains de souris transgéniques déficientes en calrétinine présentaient une excitabilité électrique accrue par rapport aux grains contrôles.
Puis, en restaurant un niveau de chélation calcique normal dans ces grains, par perfusion intracellulaire de chélateur calcique, nous montrons qu’ils retrouvent un niveau d’excitabilité normal. Ensuite, nous avons introduit dans des grains cérébelleux de souris sauvages, une forte concentration en chélateur calcique exogène. Conformément aux résultats théoriques, nous avons pu observer des transitions vers des oscillations en salve du potentiel membranaire. Enfin, nous avons montré que l’absence de calrétinine affecte les paramètres morphologiques du grain cérébelleux des souris transgéniques déficientes en calrétinine.
En conclusion, ces résultats suggèrent que le mode de décharge des cellules excitables peut être modulé d’une façon importante par les protéines liant le calcium. De ce fait, des changements dans le niveau d’expression et/ou dans la localisation subcellulaire des protéines liant le calcium pourraient aussi jouer un rôle critique dans la régulation de processus physiologiques contrôlés par l’excitabilité membranaire. De plus, les mécanismes que nous avons mis en évidence pourraient être à l’origine d’un nouveau principe de régulation de la signalisation dans les circuits neuronaux et pourraient jouer un rôle fonctionnel dans le contrôle du codage de l’information et de son stockage dans le système nerveux central.
Doctorat en sciences, Spécialisation physique
info:eu-repo/semantics/nonPublished
Hinkle, Adam R. "Tight-binding calculation of electronic properties of oligophenyl and oligoacene nanoribbons". Virtual Press, 2008. http://liblink.bsu.edu/uhtbin/catkey/1398716.
Texto completoDepartment of Physics and Astronomy
Chiang, T. "Mathematical and statistical models for the analysis of protein". Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597600.
Texto completoGregor, Craig Robert. "Epitopes, aggregation and membrane binding : investigating the protein structure-function relationship". Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/5833.
Texto completoAllison, Jerry Dewell. "An implementation of the competitive Gaussian model for metal-humic binding in a general speciation model". Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/25965.
Texto completoNordling, Erik. "Biocomputational studies on protein structures /". Stockholm, 2002.
Buscar texto completoSmoler, Eliezer. "Mathematical models to predict milk protein concentration from dietary components fed to dairy cows". Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308060.
Texto completoChu, Vano. "Molecular recognition in the streptavidin-biotin system /". Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/8106.
Texto completoLibros sobre el tema "Protein binding – Mathematical models"
Protein interaction networks: Computational analysis. Cambridge: Cambridge University Press, 2009.
Buscar texto completoRice, Stuart Alan, I. Prigogine y Richard A. Friesner. Computational methods for protein folding. Chichester: Wiley, 2002.
Buscar texto completoZimmermann, Karl-Heinz. An introduction to protein informatics. Dordrecht: Springer-Science+Business Media, B.V., 2003.
Buscar texto completoKoliński, Andrzej. Lattice models of protein folding, dynamics, and thermodynamics. Austin, Tex: R.G. Landes, 1996.
Buscar texto completoKoliński, Andrzej. Multiscale approaches to protein modeling. New York: Springer Science + Business Media, 2011.
Buscar texto completoZimmermann, Karl-Heinz. An introduction to protein informatics. Boston: Kluwer Academic Publishers, 2003.
Buscar texto completoZimmermann, Karl-Heinz. An introduction to protein informatics. Boston: Kluwer Academic Publishers, 2003.
Buscar texto completoRangwala, Huzefa, G. Karypis y G. Karypis. Introduction to protein structure prediction: Methods and algorithms. Hoboken, N.J: Wiley, 2010.
Buscar texto completoChandru, Vijay. Protein folding on lattices: An integer programming approach. Bangalore: Indian Institute of Management, 2002.
Buscar texto completoCalcium signalling in cancer. Boca Raton: CRC, 2001.
Buscar texto completoCapítulos de libros sobre el tema "Protein binding – Mathematical models"
Sharp, Kim A. "Statistical Thermodynamics of Binding and Molecular Recognition Models". En Protein-Ligand Interactions, 1–22. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527645947.ch1.
Texto completoVacca, Marcella, Floriana Della Ragione, Kumar Parijat Tripathi, Francesco Scalabrì y Maurizio D’Esposito. "MECP2: A Multifunctional Protein Supporting Brain Complexity". En Mathematical Models in Biology, 109–17. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23497-7_8.
Texto completoD’Agostino, Daniele, Andrea Clematis, Emanuele Danovaro y Ivan Merelli. "Modelling of Protein Surface Using Parallel Heterogeneous Architectures". En Mathematical Models in Biology, 189–99. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23497-7_14.
Texto completoZhao, Hongyu, Baolin Wu y Ning Sun. "DNA-protein binding and gene expression patterns". En Institute of Mathematical Statistics Lecture Notes - Monograph Series, 259–74. Beachwood, OH: Institute of Mathematical Statistics, 2003. http://dx.doi.org/10.1214/lnms/1215091147.
Texto completoTapia-Rojo, Rafael, Juan José Mazo, Andrés González, M. Luisa Peleato, Maria F. Fillat y Fernando Falo. "Free Energy Landscape Analysis of Mesoscopic Model for Finding DNA-Protein Binding Sites". En Trends in Mathematics, 81–85. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08138-0_15.
Texto completoXia, Xuhua. "Protein and Codon Substitution Models and Their Evolutionary Distances". En A Mathematical Primer of Molecular Phylogenetics, 141–70. Includes bibliographical references and index.: Apple Academic Press, 2020. http://dx.doi.org/10.1201/9780429425875-4.
Texto completoSun, Hongzhe, Mark C. Cox, Hongyan Li y Peter J. Sadler. "Rationalisation of metal binding to transferrin: Prediction of metal-protein stability constants". En Metal Sites in Proteins and Models, 71–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62870-3_3.
Texto completoLapedes, Alan S., Bertrand Giraud, LonChang Liu y Gary D. Stormo. "Correlated mutations in models of protein sequences: phylogenetic and structural effects". En Institute of Mathematical Statistics Lecture Notes - Monograph Series, 236–56. Hayward, CA: Institute of Mathematical Statistics, 1999. http://dx.doi.org/10.1214/lnms/1215455556.
Texto completoSchuster, Stefan, Marko Marhl, Milan Brumen y Reinhart Heinrich. "Influence of Calcium Binding to Proteins on Calcium Oscillations and ER Membrane Potential Oscillations. A Mathematical Model". En Information Processing in Cells and Tissues, 137–50. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5345-8_15.
Texto completoKrepets, Vladimir V. y Natalya V. Belkina. "Prediction of Binding Affinities for Protein-Ligand Complexes with Neural Network Models". En Discovery Science, 240–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44418-1_19.
Texto completoActas de conferencias sobre el tema "Protein binding – Mathematical models"
Kauffman, Chris, Huzefa Rangwala y George Karypis. "IMPROVING HOMOLOGY MODELS FOR PROTEIN-LIGAND BINDING SITES". En Proceedings of the CSB 2008 Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2008. http://dx.doi.org/10.1142/9781848162648_0019.
Texto completoRui Gao, Juanyi Yu, Mingjun Zhang y Tzyh-Jong Tarn. "Mathematical models of protein secondary structures and gene mutation". En 2009 International Conference on Mechatronics and Automation (ICMA). IEEE, 2009. http://dx.doi.org/10.1109/icma.2009.5246578.
Texto completoRAIMONDO, DOMENICO, ALEJANDRO GIORGETTI, DOMENICO COZZETTO y ANNA TRAMONTANO. "QUALITY AND EFFECTIVENESS OF PROTEIN STRUCTURE COMPARATIVE MODELS". En Proceedings of the International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812773685_0017.
Texto completoYang, Wenyi y Lei Deng. "PNAB: Prediction of protein-nucleic acid binding affinity using heterogeneous ensemble models". En 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8982930.
Texto completoKoh, Sung, G. K. Ananthasuresh y Christopher Croke. "Design of Reduced Protein Models by Energy Minimization Using Mathematical Programming". En 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4382.
Texto completoZhang, Linda Yu, Emilio Gallicchio y Ronald M. Levy. "Implicit solvent models for protein-ligand binding: Insights based on explicit solvent simulations". En SIMULATION AND THEORY OF ELECTROSTATIC INTERACTIONS IN SOLUTION. ASCE, 1999. http://dx.doi.org/10.1063/1.1301542.
Texto completoPap, Gergely, Krisztian Adam, Zoltan Gyorgypal, Laszlo Toth y Zoltan Hegedus. "Training models employing physico-chemical properties of DNA for protein binding site detection". En 2021 International Conference on Applied Artificial Intelligence (ICAPAI). IEEE, 2021. http://dx.doi.org/10.1109/icapai49758.2021.9462057.
Texto completoWÜST, T., D. P. LANDAU, C. GERVAIS y YING XU. "MONTE CARLO SIMULATIONS OF PROTEIN MODELS: AT THE INTERFACE BETWEEN STATISTICAL PHYSICS AND BIOLOGY". En International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814304900_0006.
Texto completoMatveev, Konstantin I., Thomas T. Goodman, Jingyang Chen y Suzie H. Pun. "Parametric Modeling Study of Nanoparticle Penetration Into Spherical Cell Clusters". En ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41153.
Texto completoGaivoronskaya, Irina y Valenitna Kolpakova. "MATHEMATICAL MODELS FOR THE SYNTHESIS OF PLANT-BASED COMPOSITIONS WITH IMPROVED AMINO ACID COMPOSITION". En GEOLINKS Conference Proceedings. Saima Consult Ltd, 2021. http://dx.doi.org/10.32008/geolinks2021/b1/v3/12.
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