Gotowe bibliografie tematyczne / Protein binding – Mathematical models

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji

Gotowa bibliografia na temat „Protein binding – Mathematical models”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 12 lutego 2022

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Artykuły w czasopismach na temat "Protein binding – Mathematical models"

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Palacio-Castañeda, Valentina, Simon Dumas, Philipp Albrecht, Thijmen J. Wijgers, Stéphanie Descroix, and Wouter P. R. Verdurmen. "A Hybrid In Silico and Tumor-on-a-Chip Approach to Model Targeted Protein Behavior in 3D Microenvironments." Cancers 13, no. 10 (May 18, 2021): 2461. http://dx.doi.org/10.3390/cancers13102461.

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To rationally improve targeted drug delivery to tumor cells, new methods combining in silico and physiologically relevant in vitro models are needed. This study combines mathematical modeling with 3D in vitro co-culture models to study the delivery of engineered proteins, called designed ankyrin repeat proteins (DARPins), in biomimetic tumor microenvironments containing fibroblasts and tumor cells overexpressing epithelial cell adhesion molecule (EpCAM) or human epithelial growth factor receptor (HER2). In multicellular tumor spheroids, we observed strong binding-site barriers in combination w
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Middendorf, Thomas R., and Richard W. Aldrich. "Structural identifiability of equilibrium ligand-binding parameters." Journal of General Physiology 149, no. 1 (December 19, 2016): 105–19. http://dx.doi.org/10.1085/jgp.201611702.

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Understanding the interactions of proteins with their ligands requires knowledge of molecular properties, such as binding site affinities and the effects that binding at one site exerts on binding at other sites (cooperativity). These properties cannot be measured directly and are usually estimated by fitting binding data with models that contain these quantities as parameters. In this study, we present a general method for answering the critical question of whether these parameters are identifiable (i.e., whether their estimates are accurate and unique). In cases in which parameter estimates
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Premarathna, Galkande Iresha, and 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, no. 4 (April 8, 2021): e0244905. http://dx.doi.org/10.1371/journal.pone.0244905.

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Many researchers have studied the relationship between the biological functions of proteins and the structures of both their overall backbones of amino acids and their binding sites. A large amount of the work has focused on summarizing structural features of binding sites as scalar quantities, which can result in a great deal of information loss since the structures are three-dimensional. Additionally, a common way of comparing binding sites is via aligning their atoms, which is a computationally intensive procedure that substantially limits the types of analysis and modeling that can be done
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Ruan, Shuxiang, and Gary D. Stormo. "Inherent limitations of probabilistic models for protein-DNA binding specificity." PLOS Computational Biology 13, no. 7 (July 7, 2017): e1005638. http://dx.doi.org/10.1371/journal.pcbi.1005638.

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Sedaghat, Ahmad R., Arthur Sherman, and Michael J. Quon. "A mathematical model of metabolic insulin signaling pathways." American Journal of Physiology-Endocrinology and Metabolism 283, no. 5 (November 1, 2002): E1084—E1101. http://dx.doi.org/10.1152/ajpendo.00571.2001.

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We develop a mathematical model that explicitly represents many of the known signaling components mediating translocation of the insulin-responsive glucose transporter GLUT4 to gain insight into the complexities of metabolic insulin signaling pathways. A novel mechanistic model of postreceptor events including phosphorylation of insulin receptor substrate-1, activation of phosphatidylinositol 3-kinase, and subsequent activation of downstream kinases Akt and protein kinase C-ζ is coupled with previously validated subsystem models of insulin receptor binding, receptor recycling, and GLUT4 transl
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Kimchi, Ofer, Carl P. Goodrich, Alexis Courbet, Agnese I. Curatolo, Nicholas B. Woodall, David Baker, and Michael P. Brenner. "Self-assembly–based posttranslational protein oscillators." Science Advances 6, no. 51 (December 2020): eabc1939. http://dx.doi.org/10.1126/sciadv.abc1939.

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Recent advances in synthetic posttranslational protein circuits are substantially impacting the landscape of cellular engineering and offer several advantages compared to traditional gene circuits. However, engineering dynamic phenomena such as oscillations in protein-level circuits remains an outstanding challenge. Few examples of biological posttranslational oscillators are known, necessitating theoretical progress to determine realizable oscillators. We construct mathematical models for two posttranslational oscillators, using few components that interact only through reversible binding and
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Wang, Debby D., Haoran Xie, and Hong Yan. "Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity." Bioinformatics 37, no. 17 (February 27, 2021): 2570–79. http://dx.doi.org/10.1093/bioinformatics/btab132.

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Abstract Motivation Reliable predictive models of protein–ligand binding affinity are required in many areas of biomedical research. Accurate prediction based on current descriptors or molecular fingerprints (FPs) remains a challenge. We develop novel interaction FPs (IFPs) to encode protein–ligand interactions and use them to improve the prediction. Results Proteo-chemometrics IFPs (PrtCmm IFPs) formed by combining extended connectivity fingerprints (ECFPs) with the proteo-chemometrics concept. Combining PrtCmm IFPs with machine-learning models led to efficient scoring models, which were vali
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Conradi Smith, Gregory Douglas. "Allostery in oligomeric receptor models." Mathematical Medicine and Biology: A Journal of the IMA 37, no. 3 (December 10, 2019): 313–33. http://dx.doi.org/10.1093/imammb/dqz016.

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Abstract We show how equilibrium binding curves of receptor homodimers can be expressed as rational polynomial functions of the equilibrium binding curves of the constituent monomers, without approximation and without assuming independence of receptor monomers. Using a distinguished spanning tree construction for reduced graph powers, the method properly accounts for thermodynamic constraints and allosteric interactions between receptor monomers (i.e. conformational coupling). The method is completely general; it begins with an arbitrary undirected graph representing the topology of a monomer
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Jiang, Yao, Hui-Fang Liu, and Rong Liu. "Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions." PLOS Computational Biology 17, no. 4 (April 19, 2021): e1008951. http://dx.doi.org/10.1371/journal.pcbi.1008951.

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The binding affinities of protein-nucleic acid interactions could be altered due to missense mutations occurring in DNA- or RNA-binding proteins, therefore resulting in various diseases. Unfortunately, a systematic comparison and prediction of the effects of mutations on protein-DNA and protein-RNA interactions (these two mutation classes are termed MPDs and MPRs, respectively) is still lacking. Here, we demonstrated that these two classes of mutations could generate similar or different tendencies for binding free energy changes in terms of the properties of mutated residues. We then develope
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Sohrabi-Jahromi, Salma, and Johannes Söding. "Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins." Bioinformatics 37, Supplement_1 (July 1, 2021): i308—i316. http://dx.doi.org/10.1093/bioinformatics/btab300.

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Abstract Motivation Understanding how proteins recognize their RNA targets is essential to elucidate regulatory processes in the cell. Many RNA-binding proteins (RBPs) form complexes or have multiple domains that allow them to bind to RNA in a multivalent, cooperative manner. They can thereby achieve higher specificity and affinity than proteins with a single RNA-binding domain. However, current approaches to de novo discovery of RNA binding motifs do not take multivalent binding into account. Results We present Bipartite Motif Finder (BMF), which is based on a thermodynamic model of RBPs with
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Rozprawy doktorskie na temat "Protein binding – Mathematical models"

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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.

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[Truncated abstract] Regulation of mRNA stability is an important posttranscriptional mechanism involved in the control of gene expression. The rate of mRNA decay can differ greatly from one mRNA to another and may be regulated by RNA-protein interactions. A key determinant of mRNA decay are sequence instability (cis) elements often located in the 3' untranslated region (UTR) of many mRNAs. For example, the AU rich elements (AREs), are such well characterized elements, and most commonly involved in promoting mRNA degradation, and specific binding of proteins to these elements leading to the st
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Geli, 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.

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Roussel, 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.

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Les neurones sont des cellules excitables capables de coder et transmettre l’information sous forme d’oscillations du potentiel membranaire. Cette activité électrique est produite par une modification des flux ioniques transmembranaires. Les neurones constituent un exemple d’oscillateur cellulaire dont la dynamique non linéaire permet l’apparition d’une activité électrique complexe. Dans ce système, les ions calciques sont des messagers intracellulaires importants. Ils servent de médiateur entre un signal électrique et un signal chimique, par une modulation de l’activité enzymatique de certain
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Hinkle, Adam R. "Tight-binding calculation of electronic properties of oligophenyl and oligoacene nanoribbons." Virtual Press, 2008. http://liblink.bsu.edu/uhtbin/catkey/1398716.

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Within recent years, allotropic structures of carbon have been produced in the forms of tubes and ribbons which offer the promise of extraordinary electronic and thermal properties. Here we present analyses of oligophenyl and oligoacene systems–infinite, one-dimensional chains of benzene rings linked along the armchair and zigzag directions. These one-dimensional structures, which are amenable to calculation by analytical means, exhibit features very similar to carbon nanotubes and nanoribbons. Using a tight-binding Hamiltonian we analytically determine the density of states, local density of
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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.

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Protein interactions, both amongst themselves and with other molecules, are responsible for much of the work within the cellular machine. As the number of protein interaction data sets grow in number and in size, from experiments such as Yeast 2-Hybrid or Affinity Purification followed by Mass Spectrometry, there is a need to analyse the data both quantitatively and qualitatively. One area of research is determining how reliable a report of a protein interaction is – whether it could be reproduced if the experiment were repeated, or if it were tested using an independent assay. One might aim t
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Gregor, Craig Robert. "Epitopes, aggregation and membrane binding : investigating the protein structure-function relationship." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/5833.

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The three-dimensional structure of a protein, formed as a result of amino-acid sequences folding into compact domains, is regarded as a key factor in its biological function. How and why proteins fold into specific topologies, remain the key focus of scientific research in the field of biophysics. By stripping down complex reactions down to the most basic elements, biophysicists aim to develop simplified models for biological phenomena such as antibody discrimination, viral fusion or self-assembly. Focusing on small model peptide systems, rather than the full proteins from which they were deri
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Allison, 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.

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Nordling, Erik. "Biocomputational studies on protein structures /." Stockholm, 2002.

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Smoler, 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.

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Chu, Vano. "Molecular recognition in the streptavidin-biotin system /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/8106.

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Książki na temat "Protein binding – Mathematical models"

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Protein interaction networks: Computational analysis. Cambridge: Cambridge University Press, 2009.

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Rice, Stuart Alan, I. Prigogine, and Richard A. Friesner. Computational methods for protein folding. Chichester: Wiley, 2002.

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Zimmermann, Karl-Heinz. An introduction to protein informatics. Dordrecht: Springer-Science+Business Media, B.V., 2003.

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Koliński, Andrzej. Lattice models of protein folding, dynamics, and thermodynamics. Austin, Tex: R.G. Landes, 1996.

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Koliński, Andrzej. Multiscale approaches to protein modeling. New York: Springer Science + Business Media, 2011.

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Zimmermann, Karl-Heinz. An introduction to protein informatics. Boston: Kluwer Academic Publishers, 2003.

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Zimmermann, Karl-Heinz. An introduction to protein informatics. Boston: Kluwer Academic Publishers, 2003.

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Rangwala, Huzefa, G. Karypis, and G. Karypis. Introduction to protein structure prediction: Methods and algorithms. Hoboken, N.J: Wiley, 2010.

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Chandru, Vijay. Protein folding on lattices: An integer programming approach. Bangalore: Indian Institute of Management, 2002.

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Calcium signalling in cancer. Boca Raton: CRC, 2001.

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Części książek na temat "Protein binding – Mathematical models"

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Sharp, Kim A. "Statistical Thermodynamics of Binding and Molecular Recognition Models." In Protein-Ligand Interactions, 1–22. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527645947.ch1.

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Vacca, Marcella, Floriana Della Ragione, Kumar Parijat Tripathi, Francesco Scalabrì, and Maurizio D’Esposito. "MECP2: A Multifunctional Protein Supporting Brain Complexity." In Mathematical Models in Biology, 109–17. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23497-7_8.

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D’Agostino, Daniele, Andrea Clematis, Emanuele Danovaro, and Ivan Merelli. "Modelling of Protein Surface Using Parallel Heterogeneous Architectures." In Mathematical Models in Biology, 189–99. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23497-7_14.

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Zhao, Hongyu, Baolin Wu, and Ning Sun. "DNA-protein binding and gene expression patterns." In 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.

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Tapia-Rojo, Rafael, Juan José Mazo, Andrés González, M. Luisa Peleato, Maria F. Fillat, and Fernando Falo. "Free Energy Landscape Analysis of Mesoscopic Model for Finding DNA-Protein Binding Sites." In Trends in Mathematics, 81–85. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08138-0_15.

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Xia, Xuhua. "Protein and Codon Substitution Models and Their Evolutionary Distances." In 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.

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Sun, Hongzhe, Mark C. Cox, Hongyan Li, and Peter J. Sadler. "Rationalisation of metal binding to transferrin: Prediction of metal-protein stability constants." In 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.

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Lapedes, Alan S., Bertrand Giraud, LonChang Liu, and Gary D. Stormo. "Correlated mutations in models of protein sequences: phylogenetic and structural effects." In 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.

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Schuster, Stefan, Marko Marhl, Milan Brumen, and Reinhart Heinrich. "Influence of Calcium Binding to Proteins on Calcium Oscillations and ER Membrane Potential Oscillations. A Mathematical Model." In 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.

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Krepets, Vladimir V., and Natalya V. Belkina. "Prediction of Binding Affinities for Protein-Ligand Complexes with Neural Network Models." In Discovery Science, 240–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44418-1_19.

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Streszczenia konferencji na temat "Protein binding – Mathematical models"

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Kauffman, Chris, Huzefa Rangwala, and George Karypis. "IMPROVING HOMOLOGY MODELS FOR PROTEIN-LIGAND BINDING SITES." In 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.

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Rui Gao, Juanyi Yu, Mingjun Zhang, and Tzyh-Jong Tarn. "Mathematical models of protein secondary structures and gene mutation." In 2009 International Conference on Mechatronics and Automation (ICMA). IEEE, 2009. http://dx.doi.org/10.1109/icma.2009.5246578.

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RAIMONDO, DOMENICO, ALEJANDRO GIORGETTI, DOMENICO COZZETTO, and ANNA TRAMONTANO. "QUALITY AND EFFECTIVENESS OF PROTEIN STRUCTURE COMPARATIVE MODELS." In Proceedings of the International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812773685_0017.

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Yang, Wenyi, and Lei Deng. "PNAB: Prediction of protein-nucleic acid binding affinity using heterogeneous ensemble models." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8982930.

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Koh, Sung, G. K. Ananthasuresh, and Christopher Croke. "Design of Reduced Protein Models by Energy Minimization Using Mathematical Programming." In 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.

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Zhang, Linda Yu, Emilio Gallicchio, and Ronald M. Levy. "Implicit solvent models for protein-ligand binding: Insights based on explicit solvent simulations." In SIMULATION AND THEORY OF ELECTROSTATIC INTERACTIONS IN SOLUTION. ASCE, 1999. http://dx.doi.org/10.1063/1.1301542.

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Pap, Gergely, Krisztian Adam, Zoltan Gyorgypal, Laszlo Toth, and Zoltan Hegedus. "Training models employing physico-chemical properties of DNA for protein binding site detection." In 2021 International Conference on Applied Artificial Intelligence (ICAPAI). IEEE, 2021. http://dx.doi.org/10.1109/icapai49758.2021.9462057.

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WÜST, T., D. P. LANDAU, C. GERVAIS, and YING XU. "MONTE CARLO SIMULATIONS OF PROTEIN MODELS: AT THE INTERFACE BETWEEN STATISTICAL PHYSICS AND BIOLOGY." In International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814304900_0006.

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Matveev, Konstantin I., Thomas T. Goodman, Jingyang Chen, and Suzie H. Pun. "Parametric Modeling Study of Nanoparticle Penetration Into Spherical Cell Clusters." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41153.

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Nanoparticle-based drug delivery is a promising cancer treatment method due to the ability to target tumor sites by preferential extravasation and to deliver higher loads of therapeutics. Although nanoparticle penetration in tumor tissue is limited due to diffusional restrictions, delivery can be improved by enzymatic degradation of extracellular matrix proteins at the tumor site. Here, a mathematical model describing transport of nanoparticles in non-uniformly porous spheroids is developed, accounting for binding of particles with cells and endocytosis. Results of parametric simulations for n
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Gaivoronskaya, Irina, and Valenitna Kolpakova. "MATHEMATICAL MODELS FOR THE SYNTHESIS OF PLANT-BASED COMPOSITIONS WITH IMPROVED AMINO ACID COMPOSITION." In GEOLINKS Conference Proceedings. Saima Consult Ltd, 2021. http://dx.doi.org/10.32008/geolinks2021/b1/v3/12.

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The aim of the work was to optimize the process of obtaining multicomponent protein compositions with high biological value and higher functional properties than the original vegetable protein products. Was realized studies to obtain biocomposites on the base of pea protein-oat protein and pea protein-rice protein. Developed composites were enriched with all limited amino acids. For each of the essential amino acids, the amino acid score was 100% and higher. Protein products used in these compositions are not in major allergen list, which allows to use these compositions in allergen-free produ
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