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Journal articles on the topic 'Biochemical reactions'

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Published: 4 June 2021
Last updated: 7 February 2022

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1

Chisti, Yusuf. "What drives biochemical reactions?" Biotechnology Advances 22, no. 4 (February 2004): 309–10. http://dx.doi.org/10.1016/j.biotechadv.2003.10.001.

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2

Mathews, Christopher K. "Thermodynamics of biochemical reactions." Biochemistry and Molecular Biology Education 32, no. 1 (January 2004): 444. http://dx.doi.org/10.1002/bmb.2004.494032019999.

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3

Bühler, M., and C. Wandrey. "Oleochemicals by Biochemical Reactions?" Fett Wissenschaft Technologie/Fat Science Technology 94, no. 3 (1992): 82–94. http://dx.doi.org/10.1002/lipi.19920940303.

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4

Alberty, Robert A. "Constraints in biochemical reactions." Biophysical Chemistry 49, no. 3 (April 1994): 251–61. http://dx.doi.org/10.1016/0301-4622(93)e0075-g.

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5

Villadsen, John. "Simulation of biochemical reactions." Computers & Chemical Engineering 13, no. 4-5 (April 1989): 385–95. http://dx.doi.org/10.1016/0098-1354(89)85018-5.

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6

HESS, BENNO. "Periodic patterns in biochemical reactions." Quarterly Reviews of Biophysics 30, no. 2 (May 1997): 121–76. http://dx.doi.org/10.1017/s003358359700334x.

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7

Schramm, Vern L. "Chemical Mechanisms in Biochemical Reactions." Journal of the American Chemical Society 133, no. 34 (August 31, 2011): 13207–12. http://dx.doi.org/10.1021/ja2062314.

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8

Edwards, D. A. "Biochemical Reactions on Helical Structures." SIAM Journal on Applied Mathematics 60, no. 4 (January 2000): 1425–46. http://dx.doi.org/10.1137/s0036139998343769.

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9

NAKAMURA, K. "Biochemical reactions in supercritical fluids." Trends in Biotechnology 8 (1990): 288–92. http://dx.doi.org/10.1016/0167-7799(90)90200-h.

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10

Yang, Bin, Chuan Zhu Liao, Ming Yan Jiang, and Dong Feng Yuan. "Delayed Stochastic Biochemical Reactions Reconstruction Based on Additive Reaction Model." Advanced Materials Research 894 (February 2014): 280–83. http://dx.doi.org/10.4028/www.scientific.net/amr.894.280.

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Stochastic dynamics and delayed time of biochemical reactions play an important role in the biological networks such as gene regulatory and metabolic networks. This paper presents a new model, called additive reaction model (ARM), to capture the stochastic dynamical and delayed behavior. The new evolutionary strategy is used to search the optimal biochemical model, in which genetic algorithm (GA) and particle swarm optimization (PSO) are employed to evolve the architecture and parameters of biochemical reactions, respectively. The results reveal that the delayed biochemical reaction modeling problems could be solved effectively and efficiently using our proposed new model and new evolutionary strategy.
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11

Gasteiger, Johann, Martin Reitz, Yongquan Han, and Oliver Sacher. "Analyzing Biochemical Pathways Using Neural Networks and Genetic Algorithms." Australian Journal of Chemistry 59, no. 12 (2006): 854. http://dx.doi.org/10.1071/ch06140.

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The analysis of biochemical pathways has recently gained much interest as these are the processes that keep us alive. A deeper understanding of biochemical reactions must analyze them at atomic resolution. In order to achieve that we have developed a reaction database with the information on the well known Biochemical Pathways wall chart. Based on that, 3D models of the substrates and intermediates of biochemical reactions can be built. It is shown how this information can be used for searching for inhibitors of enzyme catalyzed reactions by superimposition of 3D structures with a genetic algorithm. Physicochemical properties of the bonds directly involved in the reaction event allow a classification of these enzyme catalyzed reactions by self-organizing neural networks. This classification is compared with the enzyme code (EC) classification.
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12

Thanh, Vo Hong, Roberto Zunino, and Corrado Priami. "Efficient finite-difference method for computing sensitivities of biochemical reactions." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474, no. 2218 (October 2018): 20180303. http://dx.doi.org/10.1098/rspa.2018.0303.

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Sensitivity analysis of biochemical reactions aims at quantifying the dependence of the reaction dynamics on the reaction rates. The computation of the parameter sensitivities, however, poses many computational challenges when taking stochastic noise into account. This paper proposes a new finite-difference method for efficiently computing sensitivities of biochemical reactions. We employ propensity bounds of reactions to couple the simulation of the nominal and perturbed processes. The exactness of the simulation is preserved by applying the rejection-based mechanism. For each simulation step, the nominal and perturbed processes under our coupling strategy are synchronized and often jump together, increasing their positive correlation and hence reducing the variance of the estimator. The distinctive feature of our approach in comparison with existing coupling approaches is that it only needs to maintain a single data structure storing propensity bounds of reactions during the simulation of the nominal and perturbed processes. Our approach allows to compute sensitivities of many reaction rates simultaneously. Moreover, the data structure does not require to be updated frequently, hence improving the computational cost. This feature is especially useful when applied to large reaction networks. We benchmark our method on biological reaction models to prove its applicability and efficiency.
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13

Ross, J., and M. Schell. "Thermodynamic Efficiency in Nonlinear Biochemical Reactions." Annual Review of Biophysics and Biophysical Chemistry 16, no. 1 (June 1987): 401–22. http://dx.doi.org/10.1146/annurev.bb.16.060187.002153.

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14

Reinker, S., R. M. Altman, and J. Timmer. "Parameter estimation in stochastic biochemical reactions." IEE Proceedings - Systems Biology 153, no. 4 (2006): 168. http://dx.doi.org/10.1049/ip-syb:20050105.

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15

Lawley, Sean D., and James P. Keener. "Rebinding in biochemical reactions on membranes." Physical Biology 14, no. 5 (July 28, 2017): 056002. http://dx.doi.org/10.1088/1478-3975/aa6f93.

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16

Firman, Taylor, and Kingshuk Ghosh. "Competition enhances stochasticity in biochemical reactions." Journal of Chemical Physics 139, no. 12 (September 28, 2013): 121915. http://dx.doi.org/10.1063/1.4816527.

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17

Mišković, Ljubiša, and Vassily Hatzimanikatis. "Modeling of uncertainties in biochemical reactions." Biotechnology and Bioengineering 108, no. 2 (October 11, 2010): 413–23. http://dx.doi.org/10.1002/bit.22932.

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18

ALBERTY, R. A. "Thermodynamics of Systems of Biochemical Reactions." Journal of Theoretical Biology 215, no. 4 (April 2002): 491–501. http://dx.doi.org/10.1006/jtbi.2001.2516.

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19

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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20

Cerrito, Serenella, Marta Cialdea Mayer, and Robert Demolombe. "Temporal abductive reasoning about biochemical reactions." Journal of Applied Non-Classical Logics 27, no. 3-4 (October 2, 2017): 269–91. http://dx.doi.org/10.1080/11663081.2018.1427986.

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21

Wang, Wen, Dinesh Singh Tekcham, Min Yan, Zhichao Wang, Huan Qi, Xiaolong Liu, and Hai-Long Piao. "Biochemical reactions in metabolite-protein interaction." Chinese Chemical Letters 29, no. 5 (May 2018): 645–47. http://dx.doi.org/10.1016/j.cclet.2017.10.002.

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22

Angulo-Brown, F., M. Santillán, and E. Calleja-Quevedo. "Thermodynamic optimality in some biochemical reactions." Il Nuovo Cimento D 17, no. 1 (January 1995): 87–90. http://dx.doi.org/10.1007/bf02451604.

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23

Wandrey, Christian. "Biochemical reaction engineering for redox reactions." Chemical Record 4, no. 4 (2004): 254–65. http://dx.doi.org/10.1002/tcr.20016.

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24

Krebs, Olga, Martin Golebiewski, Renate Kania, Saqib Mir, Jasmin Saric, Andreas Weidemann, Ulrike Wittig, and Isabel Rojas. "SABIO-RK: A data warehouse for biochemical reactions and their kinetics." Journal of Integrative Bioinformatics 4, no. 1 (March 1, 2007): 22–30. http://dx.doi.org/10.1515/jib-2007-49.

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Abstract Systems biology is an emerging field that aims at obtaining a system-level understanding of biological processes. The modelling and simulation of networks of biochemical reactions have great and promising application potential but require reliable kinetic data. In order to support the systems biology community with such data we have developed SABIO-RK (System for the Analysis of Biochemical Pathways - Reaction Kinetics), a curated database with information about biochemical reactions and their kinetic properties, which allows researchers to obtain and compare kinetic data and to integrate them into models of biochemical networks. SABIO-RK is freely available for academic use at http://sabio.villa-bosch.de/SABIORK/.
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25

GUO, XIANPING, QIULI LIU, and TIANSHOU ZHOU. "OPTIMAL CONTROL OF STOCHASTIC FLUCTUATIONS IN BIOCHEMICAL REACTIONS." Journal of Biological Systems 17, no. 02 (June 2009): 283–301. http://dx.doi.org/10.1142/s0218339009002806.

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Different experimental conditions can give rise to changes in rate constants of biochemical reactions, thus resulting in different stochastic fluctuations in the numbers of chemical species molecules. A naturally arising question is how to choose a set of reaction rate constants such that experiment-depending stochastic fluctuations can be optimally controlled. In this paper, we determine the optimal rate constants by optimally controlling stochastic fluctuations in the numbers of chemical species molecules based on the theory of continuous-time Markov decision processes. Specifically, we first propose a stochastic model for a coupled set of biochemical reactions, then solve an optimality problem for rate constants with the mean-maximal numbers of chemical species molecules, and finally find, using a policy iteration algorithm of the continuous-time Markov decision processes, optimal rate constants with the variance-minimal molecule numbers over all possible sets of the rate constants with the maximal-mean molecule numbers obtained.
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26

Kim, Hyunju, Harrison B. Smith, Cole Mathis, Jason Raymond, and Sara I. Walker. "Universal scaling across biochemical networks on Earth." Science Advances 5, no. 1 (January 2019): eaau0149. http://dx.doi.org/10.1126/sciadv.aau0149.

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The application of network science to biology has advanced our understanding of the metabolism of individual organisms and the organization of ecosystems but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, we constructed biochemical networks using a global database of 28,146 annotated genomes and metagenomes and 8658 cataloged biochemical reactions. We uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical reaction networks to random reaction networks reveals that the observed biological scaling is not a product of chemistry alone but instead emerges due to the particular structure of selected reactions commonly participating in living processes. We show that the topology of biochemical networks for the three domains of life is quantitatively distinguishable, with >80% accuracy in predicting evolutionary domain based on biochemical network size and average topology. Together, our results point to a deeper level of organization in biochemical networks than what has been understood so far.
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27

Warshel, Arieh, and William W. Parson. "Dynamics of biochemical and biophysical reactions: insight from computer simulations." Quarterly Reviews of Biophysics 34, no. 4 (November 2001): 563–679. http://dx.doi.org/10.1017/s0033583501003730.

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1. Introduction 5632. Obtaining rate constants from molecular-dynamics simulations 5642.1 General relationships between quantum electronic structures and reaction rates 5642.2 The transition-state theory (TST) 5692.3 The transmission coefficient 5723. Simulating biological electron-transfer reactions 5753.1 Semi-classical surface-hopping and the Marcus equation 5753.2 Treating quantum mechanical nuclear tunneling by the dispersed-polaron/spin-boson method 5803.3 Density-matrix treatments 5833.4 Charge separation in photosynthetic bacterial reaction centers 5844. Light-induced photoisomerizations in rhodopsin and bacteriorhodopsin 5965. Energetics and dynamics of enzyme reactions 6145.1 The empirical-valence-bond treatment and free-energy perturbation methods 6145.2 Activation energies are decreased in enzymes relative to solution, often by electrostatic effects that stabilize the transition state 6205.3 Entropic effects in enzyme catalysis 6275.4 What is meant by dynamical contributions to catalysis? 6345.5 Transmission coefficients are similar for corresponding reactions in enzymes and water 6365.6 Non-equilibrium solvation effects contribute to catalysis mainly through Δg[Dagger], not the transmission coefficient 6415.7 Vibrationally assisted nuclear tunneling in enzyme catalysis 6485.8 Diffusive processes in enzyme reactions and transmembrane channels 6516. Concluding remarks 6587. Acknowledgements 6588. References 658Obtaining a detailed understanding of the dynamics of a biochemical reaction is a formidable challenge. Indeed, it might appear at first sight that reactions in proteins are too complex to analyze microscopically. At room temperature, even a relatively small protein can have as many as 1034 accessible conformational states (Dill, 1985). In many cases, however, we have detailed structural information about the active site of an enzyme, whereas such information is missing for corresponding chemical systems in solution. The atomic coordinates of the chromophore in bacteriorhodopsin, for example, are known to a resolution of 1–2 Å. In addition, experimental studies of biological processes such as photoisomerization and electron transfer have provided a wealth of detailed information that eventually may make some of these processes classical problems in chemical physics as well as biology.
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28

Poulain, Stéphane, Ophélie Arnaud, Sachi Kato, Iris Chen, Hiro Ishida, Piero Carninci, and Charles Plessy. "Machine-driven parameter screen of biochemical reactions." Nucleic Acids Research 48, no. 7 (February 6, 2020): e37-e37. http://dx.doi.org/10.1093/nar/gkaa079.

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Abstract The development of complex methods in molecular biology is a laborious, costly, iterative and often intuition-bound process where optima are sought in a multidimensional parameter space through step-by-step optimizations. The difficulty of miniaturizing reactions under the microliter volumes usually handled in multiwell plates by robots, plus the cost of the experiments, limit the number of parameters and the dynamic ranges that can be explored. Nevertheless, because of non-linearities of the response of biochemical systems to their reagent concentrations, broad dynamic ranges are necessary. Here we use a high-performance nanoliter handling platform and computer generation of liquid transfer programs to explore in quadruplicates 648 combinations of 4 parameters of a biochemical reaction, the reverse-transcription, which lead us to uncover non-linear responses, parameter interactions and novel mechanistic insights. With the increased availability of computer-driven laboratory platforms for biotechnology, our results demonstrate the feasibility and advantage of methods development based on reproducible, computer-aided exhaustive characterization of biochemical systems.
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29

NAKAKUKI, Takashi. "A Multifunctional Controller Realized by Biochemical Reactions." SICE Journal of Control, Measurement, and System Integration 8, no. 2 (2015): 99–107. http://dx.doi.org/10.9746/jcmsi.8.99.

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30

Bodnar, Marek, Urszula Foryś, and Jan Poleszczuk. "Analysis of biochemical reactions models with delays." Journal of Mathematical Analysis and Applications 376, no. 1 (April 2011): 74–83. http://dx.doi.org/10.1016/j.jmaa.2010.10.038.

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31

Latendresse, Mario, Jeremiah P. Malerich, Mike Travers, and Peter D. Karp. "Accurate Atom-Mapping Computation for Biochemical Reactions." Journal of Chemical Information and Modeling 52, no. 11 (October 15, 2012): 2970–82. http://dx.doi.org/10.1021/ci3002217.

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32

Holliday, Gemma L., Claudia Andreini, Julia D. Fischer, Syed Asad Rahman, Daniel E. Almonacid, Sophie T. Williams, and William R. Pearson. "MACiE: exploring the diversity of biochemical reactions." Nucleic Acids Research 40, no. D1 (November 3, 2011): D783—D789. http://dx.doi.org/10.1093/nar/gkr799.

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33

REITER, W. "Biochemical genetics of nucleotide sugar interconversion reactions." Current Opinion in Plant Biology 11, no. 3 (June 2008): 236–43. http://dx.doi.org/10.1016/j.pbi.2008.03.009.

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34

Dobrzyński, Maciej, Jordi Vidal Rodríguez, Jaap A. Kaandorp, and Joke G. Blom. "Computational methods for diffusion-influenced biochemical reactions." Bioinformatics 23, no. 15 (May 30, 2007): 1969–77. http://dx.doi.org/10.1093/bioinformatics/btm278.

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35

Gillies, G. T., and D. W. Kupke. "Improved magnetic suspension densimeter for biochemical reactions." Review of Scientific Instruments 59, no. 2 (February 1988): 307–13. http://dx.doi.org/10.1063/1.1140246.

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36

D'Andre, Susan C., and Alexander Y. Fadeev. "Wettability Changes Induced by Biochemical Surface Reactions." Langmuir 22, no. 9 (April 2006): 3962–63. http://dx.doi.org/10.1021/la053324f.

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37

Bialek, William, William J. Bruno, Julian Joseph, and Jos� Nelson Onuchic. "Quantum and classical dynamics in biochemical reactions." Photosynthesis Research 22, no. 1 (1989): 15–27. http://dx.doi.org/10.1007/bf00114763.

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38

Zimmerman, James. "Thermodynamics of biochemical reactions: Alberty, Robert A." Biochemistry and Molecular Biology Education 31, no. 4 (July 2003): 275–76. http://dx.doi.org/10.1002/bmb.2003.494031040245.

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39

Gasteiger, Johann. "Explorations into Chemical Reactions and Biochemical Pathways." Molecular Informatics 35, no. 11-12 (May 11, 2016): 588–92. http://dx.doi.org/10.1002/minf.201600038.

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40

Schempp, Harald, Dieter Weiser, and Erich Elstner. "Biochemical Model Reactions Indicative of Inflammatory Processes." Arzneimittelforschung 50, no. 04 (December 27, 2011): 362–72. http://dx.doi.org/10.1055/s-0031-1300215.

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41

Chiarugi, Davide, Moreno Falaschi, Diana Hermith, Carlos Olarte, and Luca Torella. "Modelling non-Markovian dynamics in biochemical reactions." BMC Systems Biology 9, Suppl 3 (2015): S8. http://dx.doi.org/10.1186/1752-0509-9-s3-s8.

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42

Egri-Nagy, Attila, Chrystopher L. Nehaniv, John L. Rhodes, and Maria J. Schilstra. "Automatic analysis of computation in biochemical reactions." Biosystems 94, no. 1-2 (October 2008): 126–34. http://dx.doi.org/10.1016/j.biosystems.2008.05.018.

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43

Alberty, R. A. "Equilibrium compositions of solutions of biochemical species and heats of biochemical reactions." Proceedings of the National Academy of Sciences 88, no. 8 (April 15, 1991): 3268–71. http://dx.doi.org/10.1073/pnas.88.8.3268.

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44

Sabatini, Antonio, Alberto Vacca, and Stefano Iotti. "Balanced Biochemical Reactions: A New Approach to Unify Chemical and Biochemical Thermodynamics." PLoS ONE 7, no. 1 (January 11, 2012): e29529. http://dx.doi.org/10.1371/journal.pone.0029529.

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45

Lawley, Sean D., and James P. Keener. "Including Rebinding Reactions in Well-Mixed Models of Distributive Biochemical Reactions." Biophysical Journal 111, no. 10 (November 2016): 2317–26. http://dx.doi.org/10.1016/j.bpj.2016.10.008.

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46

Минкевич, И. Г., and I. G. Minkevich. "Mathematical Problems of Metabolic Pathway Organization from Biochemical Reactions." Mathematical Biology and Bioinformatics 11, no. 2 (December 21, 2016): 406–25. http://dx.doi.org/10.17537/2016.11.406.

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The problem of metabolic pathway building from biochemical reactions is considered. For the given pair “main substrate – main product” it is necessary to select the reactions performing this conversion and find all the attendant substrates and products. The approach is based on a system of stoichiometric equations for reaction rates. The mathematical problems are as follows: 1) a number of right-hand sides are initially unknown, 2) a part of the system with the known right-hand sides has less number of equations than variables, and 3) presence of irreversible reactions imposes restrictions on signs of the corresponding rates in the form of inequalities. The method is described enabling to solve these problems. It is found that many restrictions are represented as series of parallel hyperplanes in the space of variables. This fact makes it possible to eliminate many restrictions and to find out a big number of fixed variables the values of which, thus, are found before the complete solving of the whole problem. The search of unnecessary restrictions and fixed variables is of the form of specific iterations due to which the problem is substantially simplified. Examples are given which display effectiveness of the approach described.
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47

EHRENFEUCHT, ANDRZEJ, MICHAEL MAIN, and GRZEGORZ ROZENBERG. "FUNCTIONS DEFINED BY REACTION SYSTEMS." International Journal of Foundations of Computer Science 22, no. 01 (January 2011): 167–78. http://dx.doi.org/10.1142/s0129054111007927.

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Reaction systems are a formal model of interactions between biochemical reactions. They consist of sets of reactions, where each reaction is classified by its set of reactants (needed for the reaction to take place), its set of inhibitors (each of which prevents the reaction from taking place), and its set of products (produced when the reaction takes place) – the set of reactants and inhibitors form the resources of the reaction. Each reaction system defines a (transition) function on its set of states. (States here are subsets of an a priori given set of biochemical entities.) In this paper we investigate properties of functions defined by reaction systems. In particular, we investigate how the power of defining functions depends on available resources, and we demonstrate that with small resources one can define functions exhibiting complex behavior.
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48

Thanh, Vo Hong. "RSSALib: a library for stochastic simulation of complex biochemical reactions." Bioinformatics 36, no. 18 (July 2, 2020): 4825–26. http://dx.doi.org/10.1093/bioinformatics/btaa602.

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Abstract Motivation Stochastic chemical kinetics is an essential mathematical framework for investigating the dynamics of biological processes, especially when stochasticity plays a vital role in their development. Simulation is often the only option for the analysis of many practical models due to their analytical intractability. Results We present in this article, the simulation library RSSALib, implementing our recently developed rejection-based stochastic simulation algorithm (RSSA) and a wide range of its improvements, to accelerate the simulation and analysis of biochemical reactions. RSSALib supports reactions with complex kinetics and time delays, necessary to model complexities of reaction mechanisms. Our library provides both an application program interface and a graphic user interface to ease the set-up and visualization of the simulation results. Availability and implementation RSSALib is freely available at: https://github.com/vo-hong-thanh/rssalib. Supplementary information Supplementary data are available at Bioinformatics online.
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49

Saito, Natsumi, Yoshiaki Ohashi, Tomoyoshi Soga, and Masaru Tomita. "Unveiling cellular biochemical reactions via metabolomics-driven approaches." Current Opinion in Microbiology 13, no. 3 (June 2010): 358–62. http://dx.doi.org/10.1016/j.mib.2010.04.006.

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50

Alcántara, Rafael, Kristian B. Axelsen, Anne Morgat, Eugeni Belda, Elisabeth Coudert, Alan Bridge, Hong Cao, et al. "Rhea—a manually curated resource of biochemical reactions." Nucleic Acids Research 40, no. D1 (November 30, 2011): D754—D760. http://dx.doi.org/10.1093/nar/gkr1126.

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