Relevant bibliographies by topics / Biochemical model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Biochemical model'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Biochemical model"

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Antoniotti, Marco, Alberto Policriti, Nadia Ugel, and Bud Mishra. "Model Building and Model Checking for Biochemical Processes." Cell Biochemistry and Biophysics 38, no. 3 (2003): 271–86. http://dx.doi.org/10.1385/cbb:38:3:271.

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Sahley, Tony L., and Richard H. Nodar. "A biochemical model of peripheral tinnitus." Hearing Research 152, no. 1-2 (February 2001): 43–54. http://dx.doi.org/10.1016/s0378-5955(00)00235-5.

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Appanna, Vasu D., Shawna L. Anderson, and Tamara Skakoon. "Biogenesis of calcite: A biochemical model." Microbiological Research 152, no. 4 (December 1997): 341–43. http://dx.doi.org/10.1016/s0944-5013(97)80049-3.

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Johnson, Mark A. "Biochemical bone fracture healing process model." IFAC Proceedings Volumes 36, no. 15 (August 2003): 335–40. http://dx.doi.org/10.1016/s1474-6670(17)33525-5.

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Elstner, Erich F., R. Adamczyk, A. Furch, and R. Kröner. "Biochemical Model Reactions for Cataract Research." Ophthalmic Research 17, no. 5 (1985): 302–7. http://dx.doi.org/10.1159/000265390.

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Craciun, Gheorghe, Jaejik Kim, Casian Pantea, and Grzegorz A. Rempala. "Statistical Model for Biochemical Network Inference." Communications in Statistics - Simulation and Computation 42, no. 1 (January 2013): 121–37. http://dx.doi.org/10.1080/03610918.2011.633200.

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Feliu, Elisenda, and Carsten Wiuf. "Simplifying biochemical models with intermediate species." Journal of The Royal Society Interface 10, no. 87 (October 6, 2013): 20130484. http://dx.doi.org/10.1098/rsif.2013.0484.

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Mathematical models are increasingly being used to understand complex biochemical systems, to analyse experimental data and make predictions about unobserved quantities. However, we rarely know how robust our conclusions are with respect to the choice and uncertainties of the model. Using algebraic techniques, we study systematically the effects of intermediate, or transient, species in biochemical systems and provide a simple, yet rigorous mathematical classification of all models obtained from a core model by including intermediates. Main examples include enzymatic and post-translational modification systems, where intermediates often are considered insignificant and neglected in a model, or they are not included because we are unaware of their existence. All possible models obtained from the core model are classified into a finite number of classes. Each class is defined by a mathematically simple canonical model that characterizes crucial dynamical properties, such as mono- and multistationarity and stability of steady states, of all models in the class. We show that if the core model does not have conservation laws, then the introduction of intermediates does not change the steady-state concentrations of the species in the core model, after suitable matching of parameters. Importantly, our results provide guidelines to the modeller in choosing between models and in distinguishing their properties. Further, our work provides a formal way of comparing models that share a common skeleton.
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Miskovic, Ljubisa, Jonas Béal, Michael Moret, and Vassily Hatzimanikatis. "Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties." PLOS Computational Biology 15, no. 8 (August 20, 2019): e1007242. http://dx.doi.org/10.1371/journal.pcbi.1007242.

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Li, Shi, Xi Ju Zong, and Yan Hu. "Modeling and Control of Biochemical Reactor." Advanced Materials Research 791-793 (September 2013): 818–21. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.818.

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This paper is concerns with the study of modeling and control of biochemical reactor. Firstly, a mathematical model is established for a typical biochemical reactor, the mass balance equations are established individually for substrate concentration and biomass concentration. Then, the model is linearized at the steady-state point, two linear models are derived: state space model and transfer function model. The transfer function model is used in internal model control (IMC), where the filter parameter is selected and discussed. The state space model is applied in model predictive control (MPC), where controller parameters of control prediction horizon length and constraint of control variable variation are discussed.
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Iqbal, Muhammad Asad, Syed Tauseef Mohyud-Din, and Bandar Bin-Mohsin. "A study of nonlinear biochemical reaction model." International Journal of Biomathematics 09, no. 05 (June 13, 2016): 1650071. http://dx.doi.org/10.1142/s1793524516500716.

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The present study deals with the introduction of an alteration in Legendre wavelets method by availing of the Picard iteration method for system of differential equations and named it Legendre wavelet-Picard method (LWPM). Convergence of the proposed method is also discussed. In order to check the competence of the proposed method, basic enzyme kinetics is considered. Systems of nonlinear ordinary differential equations are formed from the considered enzyme-substrate reaction. The results obtained by the proposed LWPM are compared with the numerical results obtained from Runge–Kutta method of order four (RK-4). Numerical results and those obtained by LWPM are in excellent conformance, which would be explained by the help of table and figures. The proposed method is easy and simple to implement as compared to the other existing analytical methods used for solving systems of differential equations arising in biology, physics and engineering.
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Dissertations / Theses on the topic "Biochemical model"

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Khoshnaw, Sarbaz Hamza Abdullah. "Model reductions in biochemical reaction networks." Thesis, University of Leicester, 2015. http://hdl.handle.net/2381/32442.

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Many complex kinetic models in the field of biochemical reactions contain a large number of species and reactions. These models often require a huge array of computational tools to analyse. Techniques of model reduction, which arise in various theoretical and practical applications in systems biology, represent key critical elements (variables and parameters) and substructures of the original system. This thesis aims to study methods of model reduction for biochemical reaction networks. It has three goals related to techniques of model reduction. The primary goal provides analytical approximate solutions of such models. In order to have this set of solutions, we propose an algorithm based on the Duhamel iterates. This algorithm is an explicit formula that can be studied in detail for wide regions of concentrations for optimization and parameter identification purposes. Another goal is to simplify high dimensional models to smaller sizes in which the dynamics of original models and reduced models should be similar. Therefore, we have developed some techniques of model reduction such as geometric singular perturbation method for slow and fast subsystems, and entropy production analysis for identifying non–important reactions. The suggested techniques can be applied to some models in systems biology including enzymatic reactions, elongation factors EF–Tu and EF–Ts signalling pathways, and nuclear receptor signalling. Calculating the value of deviation at each reduction stage helps to check that the approximation of concentrations is still within the allowable limits. The final goal is to identify critical model parameters and variables for reduced models. We study the methods of local sensitivity in order to find the critical model elements. The results are obtained in numerical simulations based on Systems Biology Toolbox (SBToolbox) and SimBiology Toolbox for Matlab. The simplified models would be accurate, robust, and easily applied by biologists for various purposes such as reproducing biological data and functions for the full models.
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Hughes, Helen Margaret. "Model systems for studying the biochemical effects of hepatotoxins." Thesis, Cardiff University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.254495.

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Wu, Zujian. "A generic approach to behaviour-driven biochemical model construction." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7413.

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Modelling of biochemical systems has received considerable attention over the last decade from bioengineering, biochemistry, computer science, and mathematics. This thesis investigates the applications of computational techniques to computational systems biology, for the construction of biochemical models in terms of topology and kinetic rates. Due to the complexity of biochemical systems, it is natural to construct models representing the biochemical systems incrementally in a piecewise manner. Syntax and semantics of two patterns are defined for the instantiation of components which are extendable, reusable and fundamental building blocks for models composition. We propose and implement a set of genetic operators and composition rules to tackle issues of piecewise composing models from scratch. Quantitative Petri nets are evolved by the genetic operators, and evolutionary process of modelling are guided by the composition rules. Metaheuristic algorithms are widely applied in BioModel Engineering to support intelligent and heuristic analysis of biochemical systems in terms of structure and kinetic rates. We illustrate parameters of biochemical models based on Biochemical Systems Theory, and then the topology and kinetic rates of the models are manipulated by employing evolution strategy and simulated annealing respectively. A new hybrid modelling framework is proposed and implemented for the models construction. Two heuristic algorithms are performed on two embedded layers in the hybrid framework: an outer layer for topology mutation and an inner layer for rates optimization. Moreover, variants of the hybrid piecewise modelling framework are investigated. Regarding flexibility of these variants, various combinations of evolutionary operators, evaluation criteria and design principles can be taken into account. We examine performance of five sets of the variants on specific aspects of modelling. The comparison of variants is not to explicitly show that one variant clearly outperforms the others, but it provides an indication of considering important features for various aspects of the modelling. Because of the very heavy computational demands, the process of modelling is paralleled by employing a grid environment, GridGain. Application of the GridGain and heuristic algorithms to analyze biological processes can support modelling of biochemical systems in a computational manner, which can also benefit mathematical modelling in computer science and bioengineering. We apply our proposed modelling framework to model biochemical systems in a hybrid piecewise manner. Modelling variants of the framework are comparatively studied on specific aims of modelling. Simulation results show that our modelling framework can compose synthetic models exhibiting similar species behaviour, generate models with alternative topologies and obtain general knowledge about key modelling features.
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Wu, Jialiang. "Hybrid modeling and analysis of multiscale biochemical reaction networks." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/47723.

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This dissertation addresses the development of integrative modeling strategies capable of combining deterministic and stochastic, discrete and continuous, as well as multi-scale features. The first set of studies combines the purely deterministic modeling methodology of Biochemical Systems Theory (BST) with a hybrid approach, using Functional Petri Nets, which permits the account of discrete features or events, stochasticity, and different types of delays. The efficiency and significance of this combination is demonstrated with several examples, including generic biochemical networks with feedback controls, gene regulatory modules, and dopamine based neuronal signal transduction. A study expanding the use of stochasticity toward systems with small numbers of molecules proposes a rather general strategy for converting a deterministic process model into a corresponding stochastic model. The strategy characterizes the mathematical connection between a stochastic framework and the deterministic analog. The deterministic framework is assumed to be a generalized mass action system and the stochastic analogue is in the format of the chemical master equation. The analysis identifies situations where internal noise affecting the system needs to be taken into account for a valid conversion from a deterministic to a stochastic model. The conversion procedure is illustrated with several representative examples, including elemental reactions, Michaelis-Menten enzyme kinetics, a genetic regulatory motif, and stochastic focusing. The last study establishes two novel, particle-based methods to simulate biochemical diffusion-reaction systems within crowded environments. These simulation methods effectively simulate and quantify crowding effects, including reduced reaction volumes, reduced diffusion rates, and reduced accessibility between potentially reacting particles. The proposed methods account for fractal-like kinetics, where the reaction rate depends on the local concentrations of the molecules undergoing the reaction. Rooted in an agent based modeling framework, this aspect of the methods offers the capacity to address sophisticated intracellular spatial effects, such as macromolecular crowding, active transport along cytoskeleton structures, and reactions on heterogeneous surfaces, as well as in porous media. Taken together, the work in this dissertation successfully developed theories and simulation methods which extend the deterministic, continuous framework of Biochemical Systems Theory to allow the account of delays, stochasticity, discrete features or events, and spatial effects for the modeling of biological systems, which are hybrid and multiscale by nature.
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Trumble, Troy Neal. "Early osteoarthritic changes in a canine cranial cruciate deficient model." Access citation, abstract and download form; downloadable file 15.41 Mb, 2004. http://wwwlib.umi.com/dissertations/fullcit/3131703.

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Quaiser, Tom [Verfasser]. "Data- and model-based identification of biochemical processes / Tom Quaiser." Aachen : Shaker, 2012. http://d-nb.info/1069045845/34.

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Gibb, Jason Ocean Telford. "NMR studies on the effects of model pollutants in selected invertebrate species." Thesis, Birkbeck (University of London), 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268053.

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Banks, Christopher Jon. "Spatio-temporal logic for the analysis of biochemical models." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10512.

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Process algebra, formal specification, and model checking are all well studied techniques in the analysis of concurrent computer systems. More recently these techniques have been applied to the analysis of biochemical systems which, at an abstract level, have similar patterns of behaviour to concurrent processes. Process algebraic models and temporal logic specifications, along with their associated model-checking techniques, have been used to analyse biochemical systems. In this thesis we develop a spatio-temporal logic, the Logic of Behaviour in Context (LBC), for the analysis of biochemical models. That is, we define and study the application of a formal specification language which not only expresses temporal properties of biochemical models, but expresses spatial or contextual properties as well. The logic can be used to express, or specify, the behaviour of a model when it is placed into the context of another model. We also explore the types of properties which can be expressed in LBC, various algorithms for model checking LBC - each an improvement on the last, the implementation of the computational tools to support model checking LBC, and a case study on the analysis of models of post-translational biochemical oscillators using LBC. We show that a number of interesting and useful properties can be expressed in LBC and that it is possible to express highly useful properties of real models in the biochemistry domain, with practical application. Statements in LBC can be thought of as expressing computational experiments which can be performed automatically by means of the model checker. Indeed, many of these computational experiments can be higher-order meaning that one succinct and precise specification in LBC can represent a number of experiments which can be automatically executed by the model checker.
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Reid, Cherith Norma. "Biochemical basis of interspecific cell signalling : an invertebrate and vertebrate model." Thesis, Queen's University Belfast, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326448.

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Mintz, Christina. "Predicting Chemical and Biochemical Properties Using the Abraham General Solvation Model." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc28373/.

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Several studies were done to illustrate the versatillity of the Abraham model in mathematically describing the various solute-solvent interactions found in a wide range of different chemical and biological systems. The first study focused on using the solvation model to construct mathematical correlations describing the minimum inhibitory concentration of organic compounds for growth inhibition towards the three bacterial strains Porphyromonas gingivalis, Selenomonas artemidis, and Streptococcus sobrinus. The next several studies expand the practicallity of the Abraham model by predicting free energies of partition in chemical systems. The free energy studies expand the use of the Abraham model to other temperatures and properties by developing correlations for the enthalpies of solvation of gaseous solutes of various compounds dissolved in water, 1-octanol, hexane, heptane, hexadecane, cyclohexane, benzene, toluene, carbon tetrachloride, chloroform, methanol, ethanol, 1-butanol, propylene carbonate, dimethyl sulfoxide, 1,2-dichloroethane, N,N-dimethylformamide, tert-butanol, dibutyl ether, ethyl acetate, acetonitrile, and acetone. Also, a generic equation for linear alkanes is created for use when individual datasets are small. The prediction of enthalpies of solvation is furthered by modifying the Abraham model so that experimental data measured at different temperatures can be included into a single correlation expression. The temperature dependence is directly included in the model by separating each coefficient into an enthalpic and entropic component. Specifically, the final study describes the effects of temperature on the sorption coefficients of organic gases onto humic acid. The derived predicted values for each research study show a good correlation with experimental values.
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Books on the topic "Biochemical model"

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DeCampos, Kleber N. Functional and biochemical assessment of post preservation lung viability in a rat model. Ottawa: National Library of Canada, 1992.

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Fieldhouse, John. Biochemical structural models. 2nd ed. Wymondham, Melton Mowbray, Leicestershire: Witmehá Productions, 1993.

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Fieldhouse, John. Biochemical structural models. [Wymondham, Melton Mowbray, Leicestershire: Witmehá Productions, 1989.

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Ruwe, Patrick Alan. The treatment of peripheral nerve injuries with cyopreserved autografts: A functional, histological, and biochemical evaluation of the role of cryopreservation in the rat model. [New Haven: s.n.], 1987.

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Caemmerer, S. Von. Biochemical models of leaf photosynthesis. Collingwood, VIC: CSIRO Pub., 2000.

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Caemmerer, S. von. Biochemical models of photosynthetic CO2 assimilation. Collingwood, Aus: CSIRO, 1999.

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Computer games and simulation for biochemical engineering. New York: Wiley, 1985.

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Parag, Garhyan, ed. Conservation equations and modeling of chemical and biochemical processes. New York: Marcel Dekker, 2003.

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Kafarov, V. V. Modelirovanie i sistemnyĭ analiz biokhimicheskikh proizvodstv. Moskva: Lesnai͡a︡ Promyshl., 1985.

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Skinner, F. Interpretation of ultimate biochemical oxygen demand data via kinetic curve extrapolation models. Vegreville, Alta: Environmental Technology Division, Alberta Environmental Centre, 1990.

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Book chapters on the topic "Biochemical model"

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Pop, Paul, Mirela Alistar, Elena Stuart, and Jan Madsen. "Biochemical Application Model." In Fault-Tolerant Digital Microfluidic Biochips, 51–66. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23072-6_4.

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Ostell, James M., and Jonathan A. Kans. "The NCBI Data Model." In Methods of Biochemical Analysis, 121–44. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470110607.ch6.

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Ostell, James M., Sarah J. Wheelan, and Jonathan A. Kans. "The NCBI Data Model." In Methods of Biochemical Analysis, 19–43. New York, USA: John Wiley & Sons, Inc., 2002. http://dx.doi.org/10.1002/0471223921.ch2.

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von Caemmerer, Susanne, Graham Farquhar, and Joseph Berry. "Biochemical Model of C3 Photosynthesis." In Photosynthesis in silico, 209–30. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-9237-4_9.

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Fränzle, Stefan. "A Causal Model of Biochemical Essentiality." In Chemical Elements in Plant and Soil: Parameters Controlling Essentiality, 131–51. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2752-8_3.

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Chabrier, Nathalie, and François Fages. "Symbolic Model Checking of Biochemical Networks." In Computational Methods in Systems Biology, 149–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36481-1_13.

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Fages, François. "Symbolic Model-Checking for Biochemical Systems." In Logic Programming, 102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24599-5_7.

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Jeong-Woo, Choi, Cho Sang-Won, Huh Yun-Jun, and Lee Won-Hong. "Bioproduct Adsorption on Encapsulated Adsorbent: Local Thermodynamic Equilibrium Model." In Biochemical Engineering for 2001, 575–77. Tokyo: Springer Japan, 1992. http://dx.doi.org/10.1007/978-4-431-68180-9_154.

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Werdan, Karl, and Ursula Müller-Werdan. "Elucidating molecular mechanisms of septic cardiomyopathy — the cardiomyocyte model." In Biochemical Regulation of Myocardium, 291–303. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1289-5_36.

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Anderson, David F., and Thomas G. Kurtz. "Analytic approaches to model simplification and approximation." In Stochastic Analysis of Biochemical Systems, 43–53. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16895-1_4.

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Conference papers on the topic "Biochemical model"

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Bashkirtseva, Irina, and Svetlana Zaitseva. "Analysis of dynamic modes in 3D biochemical model." In THE VII INTERNATIONAL YOUNG RESEARCHERS’ CONFERENCE – PHYSICS, TECHNOLOGY, INNOVATIONS (PTI-2020). AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0036606.

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Prud'homme, M., and S. Jasmin. "INVERSE DETERMINATION OF BIOCHEMICAL MODEL PARAMETERS IN POROUS MEDIUM CONVECTION WITH BIOCHEMICAL HEATING." In Annals of the Assembly for International Heat Transfer Conference 13. Begell House Inc., 2006. http://dx.doi.org/10.1615/ihtc13.p5.140.

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Rao, Shodhan, Arjan van der Schaft, Karen van Eunen, Barbara M. Bakker, and Bayu Jayawardhana. "Model-order reduction of biochemical reaction networks." In 2013 European Control Conference (ECC). IEEE, 2013. http://dx.doi.org/10.23919/ecc.2013.6669587.

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Bashkirtseva, Irina A., and Svetlana S. Zaitseva. "Stochastic multimodal oscillations in nonlinear biochemical model." In PHYSICS, TECHNOLOGIES AND INNOVATION (PTI-2019): Proceedings of the VI International Young Researchers’ Conference. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5134341.

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Ghaemi, Reza, and Domitilla Del Vecchio. "Evaluating the robustness of a biochemical network model." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434348.

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Zhao, Yihe, Libo Zhao, Zhikang Li, Jie Li, Tingzhong Xu, Shuaishuai Guo, Zichen Liu, et al. "Lumped Element Model for CMUTs­Based Biochemical Resonant Sensor." In 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII). IEEE, 2019. http://dx.doi.org/10.1109/transducers.2019.8808692.

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Javed, Anum, and Mian Ilyas Ahmad. "Projection-Based Model Order Reduction for Biochemical Systems." In 2019 International Conference on Applied and Engineering Mathematics (ICAEM). IEEE, 2019. http://dx.doi.org/10.1109/icaem.2019.8853743.

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Khalid, Arfeen, and Sumit Kumar Jha. "Calibration of Rule-Based Stochastic Biochemical Models using Statistical Model Checking." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621292.

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Lapid, Hadas, Assaf Marron, Smadar Szekely, and David Harel. "Using Reactive-System Modeling Techniques to Create Executable Models of Biochemical Pathways." In 7th International Conference on Model-Driven Engineering and Software Development. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007572504540464.

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Lapid, Hadas, Assaf Marron, Smadar Szekely, and David Harel. "Using Reactive-System Modeling Techniques to Create Executable Models of Biochemical Pathways." In 7th International Conference on Model-Driven Engineering and Software Development. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007572504560466.

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Reports on the topic "Biochemical model"

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Aden, Andy. Biochemical Production of Ethanol from Corn Stover: 2007 State of Technology Model. Office of Scientific and Technical Information (OSTI), May 2008. http://dx.doi.org/10.2172/1218322.

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Humbird, D., and A. Aden. Biochemical Production of Ethanol from Corn Stover: 2008 State of Technology Model. Office of Scientific and Technical Information (OSTI), August 2009. http://dx.doi.org/10.2172/962490.

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Aden, A. Biochemical Production of Ethanol from Corn Stover: 2007 State of Technology Model. Office of Scientific and Technical Information (OSTI), May 2008. http://dx.doi.org/10.2172/928256.

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