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1

Jackson, Robert C. "Toxicity prediction from metabolic pathway modelling." Toxicology 102, no. 1-2 (1995): 197–205. http://dx.doi.org/10.1016/0300-483x(95)03048-k.

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2

Mustafin, Almaz, and Aliya Kantarbayeva. "SUPPLY CHAIN MODELED AS A METABOLIC PATHWAY." Mathematical Modelling and Analysis 23, no. 3 (2018): 473–91. http://dx.doi.org/10.3846/mma.2018.028.

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A new model of economic production process is proposed (in the form of a set of ODEs) based on an idea that nonconsumable factors of production facilitate the conversion of inputs to output in much the same catalytic way as do enzymes in living cells when transforming substrates into different chemical compounds. The output of a converging, multi-resource, single-product supply chain network is shown to depend on the minimum of its inputs in the form of the Leontief--Liebig production function, providing the validity of the clearing function approximation. In turn use of the clearing function
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3

Berndt, Nikolaus, Antje Egners, Guido Mastrobuoni, et al. "Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer." British Journal of Cancer 122, no. 2 (2019): 233–44. http://dx.doi.org/10.1038/s41416-019-0659-3.

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Abstract Background Metabolic alterations can serve as targets for diagnosis and cancer therapy. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation. Methods We applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer. Results We show that relative differences of protein abundances of metabolic enzymes obtained by mass spectrometry can be used to assess their maximal velocity values. M
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Moulin, Cecile, Laurent Tournier, and Sabine Peres. "Combining Kinetic and Constraint-Based Modelling to Better Understand Metabolism Dynamics." Processes 9, no. 10 (2021): 1701. http://dx.doi.org/10.3390/pr9101701.

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To understand the phenotypic capabilities of organisms, it is useful to characterise cellular metabolism through the analysis of its pathways. Dynamic mathematical modelling of metabolic networks is of high interest as it provides the time evolution of the metabolic components. However, it also has limitations, such as the necessary mechanistic details and kinetic parameters are not always available. On the other hand, large metabolic networks exhibit a complex topological structure which can be studied rather efficiently in their stationary regime by constraint-based methods. These methods pr
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Demko, Martin, Lukáš Chrást, Pavel Dvořák, Jiří Damborský, and David Šafránek. "Computational Modelling of Metabolic Burden and Substrate Toxicity in Escherichia coli Carrying a Synthetic Metabolic Pathway." Microorganisms 7, no. 11 (2019): 553. http://dx.doi.org/10.3390/microorganisms7110553.

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In our previous work, we designed and implemented a synthetic metabolic pathway for 1,2,3-trichloropropane (TCP) biodegradation in Escherichia coli. Significant effects of metabolic burden and toxicity exacerbation were observed on single cell and population levels. Deeper understanding of mechanisms underlying these effects is extremely important for metabolic engineering of efficient microbial cell factories for biotechnological processes. In this paper, we present a novel mathematical model of the pathway. The model addresses for the first time the combined effects of toxicity exacerbation
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Ginger, Michael L., Geoffrey I. McFadden, and Paul A. M. Michels. "Rewiring and regulation of cross-compartmentalized metabolism in protists." Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1541 (2010): 831–45. http://dx.doi.org/10.1098/rstb.2009.0259.

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Plastid acquisition, endosymbiotic associations, lateral gene transfer, organelle degeneracy or even organelle loss influence metabolic capabilities in many different protists. Thus, metabolic diversity is sculpted through the gain of new metabolic functions and moderation or loss of pathways that are often essential in the majority of eukaryotes. What is perhaps less apparent to the casual observer is that the sub-compartmentalization of ubiquitous pathways has been repeatedly remodelled during eukaryotic evolution, and the textbook pictures of intermediary metabolism established for animals,
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Olin-Sandoval, Viridiana, Zabdi González-Chávez, Miriam Berzunza-Cruz, et al. "Drug target validation of the trypanothione pathway enzymes through metabolic modelling." FEBS Journal 279, no. 10 (2012): 1811–33. http://dx.doi.org/10.1111/j.1742-4658.2012.08557.x.

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8

Navas-Delgado, I., A. Real-Chicharro, M. A. Medina, F. Sanchez-Jimenez, and J. F. Aldana-Montes. "Social pathway annotation: extensions of the systems biology metabolic modelling assistant." Briefings in Bioinformatics 12, no. 6 (2010): 576–87. http://dx.doi.org/10.1093/bib/bbq061.

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9

Joly, James H., Brandon T. L. Chew, and Nicholas A. Graham. "The landscape of metabolic pathway dependencies in cancer cell lines." PLOS Computational Biology 17, no. 4 (2021): e1008942. http://dx.doi.org/10.1371/journal.pcbi.1008942.

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The metabolic reprogramming of cancer cells creates metabolic vulnerabilities that can be therapeutically targeted. However, our understanding of metabolic dependencies and the pathway crosstalk that creates these vulnerabilities in cancer cells remains incomplete. Here, by integrating gene expression data with genetic loss-of-function and pharmacological screening data from hundreds of cancer cell lines, we identified metabolic vulnerabilities at the level of pathways rather than individual genes. This approach revealed that metabolic pathway dependencies are highly context-specific such that
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10

Zhu, Yuanyuan, Bin Hu, Lei Chen, and Qi Dai. "iMPTCE-Hnetwork: A Multilabel Classifier for Identifying Metabolic Pathway Types of Chemicals and Enzymes with a Heterogeneous Network." Computational and Mathematical Methods in Medicine 2021 (January 4, 2021): 1–12. http://dx.doi.org/10.1155/2021/6683051.

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Metabolic pathway is an important type of biological pathways. It produces essential molecules and energies to maintain the life of living organisms. Each metabolic pathway consists of a chain of chemical reactions, which always need enzymes to participate in. Thus, chemicals and enzymes are two major components for each metabolic pathway. Although several metabolic pathways have been uncovered, the metabolic pathway system is still far from complete. Some hidden chemicals or enzymes are not discovered in a certain metabolic pathway. Besides the traditional experiments to detect hidden chemica
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11

Heckmann, David. "Modelling metabolic evolution on phenotypic fitness landscapes: a case study on C4 photosynthesis." Biochemical Society Transactions 43, no. 6 (2015): 1172–76. http://dx.doi.org/10.1042/bst20150148.

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How did the complex metabolic systems we observe today evolve through adaptive evolution? The fitness landscape is the theoretical framework to answer this question. Since experimental data on natural fitness landscapes is scarce, computational models are a valuable tool to predict landscape topologies and evolutionary trajectories. Careful assumptions about the genetic and phenotypic features of the system under study can simplify the design of such models significantly. The analysis of C4 photosynthesis evolution provides an example for accurate predictions based on the phenotypic fitness la
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12

Yasemi, Mohammadreza, and Mario Jolicoeur. "Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches." Processes 9, no. 2 (2021): 322. http://dx.doi.org/10.3390/pr9020322.

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Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions.
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13

Cherdal, Safae, and Salma Mouline. "Modelling and Simulation of Biochemical Processes Using Petri Nets." Processes 6, no. 8 (2018): 97. http://dx.doi.org/10.3390/pr6080097.

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Systems composed of many components which interact with each other and lead to unpredictable global behaviour, are considered as complex systems. In a biological context, complex systems represent living systems composed of a large number of interacting elements. In order to study these systems, a precise mathematical modelling was typically used in this context. However, this modelling has limitations in the structural understanding and the behavioural study. In this sense, formal computational modelling is an approach that allows to model and to simulate dynamical properties of these particu
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14

Voit, Eberhard O. "Modelling metabolic networks using power-laws and S-systems." Essays in Biochemistry 45 (September 30, 2008): 29–40. http://dx.doi.org/10.1042/bse0450029.

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Mathematical modelling has great potential in biochemical network analysis because, in contrast with the unaided human mind, mathematics has no problems keeping track of hundreds of interacting variables that affect each other in intricate ways. The scalability of mathematical models, together with their ability to capture all imaginable non-linear responses, allows us to explore the dynamics of complicated pathway systems, to study what happens if a metabolite, gene or enzyme is altered, and to optimize biochemical systems, for instance toward the goal of increased yield of some desired organ
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15

Tang, Joseph Kuo-Hsiang, Le You, Robert E. Blankenship, and Yinjie J. Tang. "Recent advances in mapping environmental microbial metabolisms through 13 C isotopic fingerprints." Journal of The Royal Society Interface 9, no. 76 (2012): 2767–80. http://dx.doi.org/10.1098/rsif.2012.0396.

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After feeding microbes with a defined 13 C substrate, unique isotopic patterns (isotopic fingerprints) can be formed in their metabolic products. Such labelling information not only can provide novel insights into functional pathways but also can determine absolute carbon fluxes through the metabolic network via metabolic modelling approaches. This technique has been used for finding pathways that may have been mis-annotated in the past, elucidating new enzyme functions, and investigating cell metabolisms in microbial communities. In this review paper, we summarize the applications of 13 C app
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Kromer, Jens O. "Quantification of microbial phenotypes using 13C-Fluxomics." Microbiology Australia 32, no. 4 (2011): 163. http://dx.doi.org/10.1071/ma11163.

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Systems biology is an emerging tool in microbiology that helps us to understand cellular processes and to optimise microbes for production purposes1. It strongly relies on the use of large datasets created using omics tools followed by data mining and modelling in order to gain new insights into biology. The creation of the datasets is usually comprised of genomics defining the overall capacity of a microbe, transcriptomics and proteomics as a measure of the active set of reactions within the overall capacity and more recently metabolomics as a measure of the available building blocks and (if
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17

Ip, Kuhn, Caroline Colijn, and Desmond S. Lun. "Analysis of complex metabolic behavior through pathway decomposition." BMC Systems Biology 5, no. 1 (2011): 91. http://dx.doi.org/10.1186/1752-0509-5-91.

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18

Stobbe, Miranda D., Morris A. Swertz, Ines Thiele, Trebor Rengaw, Antoine HC van Kampen, and Perry D. Moerland. "Consensus and conflict cards for metabolic pathway databases." BMC Systems Biology 7, no. 1 (2013): 50. http://dx.doi.org/10.1186/1752-0509-7-50.

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19

M. A. Basher, Abdur Rahman, Ryan J. McLaughlin, and Steven J. Hallam. "Metabolic pathway inference using multi-label classification with rich pathway features." PLOS Computational Biology 16, no. 10 (2020): e1008174. http://dx.doi.org/10.1371/journal.pcbi.1008174.

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20

Voit, E. O., I. C. Chou, L. L. Fonseca, and G. Goel. "Estimation of metabolic pathway systems from different data sources." IET Systems Biology 3, no. 6 (2009): 513–22. http://dx.doi.org/10.1049/iet-syb.2008.0180.

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21

Wu, Wu-Hsiung, Fan-Yu Li, Yi-Chen Shu, et al. "Oncogene inference optimization using constraint-based modelling incorporated with protein expression in normal and tumour tissues." Royal Society Open Science 7, no. 3 (2020): 191241. http://dx.doi.org/10.1098/rsos.191241.

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Cancer cells are known to exhibit unusual metabolic activity, and yet few metabolic cancer driver genes are known. Genetic alterations and epigenetic modifications of cancer cells result in the abnormal regulation of cellular metabolic pathways that are different when compared with normal cells. Such a metabolic reprogramming can be simulated using constraint-based modelling approaches towards predicting oncogenes. We introduced the tri-level optimization problem to use the metabolic reprogramming towards inferring oncogenes. The algorithm incorporated Recon 2.2 network with the Human Protein
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22

Min, Yong, Xiaogang Jin, Ming Chen, Zhengzheng Pan, Ying Ge, and Jie Chang. "Pathway knockout and redundancy in metabolic networks." Journal of Theoretical Biology 270, no. 1 (2011): 63–69. http://dx.doi.org/10.1016/j.jtbi.2010.11.012.

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23

WRIGHT, T. C., J. P. CANT, and B. W. MCBRIDE. "Use of metabolic control analysis in lactation biology." Journal of Agricultural Science 146, no. 3 (2008): 267–73. http://dx.doi.org/10.1017/s002185960800782x.

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SUMMARYSensitivity analysis is routinely carried out in the evaluation of simulation models to identify the degree to which parameters influence model outputs. This type of sensitivity analysis is much less frequently applied to real systems, but a technique called metabolic control analysis (MCA) was developed in the 1970s for the purpose of experimentally identifying the degree to which individual enzymes in a metabolic pathway influence flux through the pathway. MCA is applied to the results of inhibition, activation or genetic manipulation of enzymatic steps in a biochemical pathway. Flux
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24

Hsu, Kai-Cheng, Wen-Chi Cheng, Yen-Fu Chen, Wen-Ching Wang, and Jinn-Moon Yang. "Pathway-based Screening Strategy for Multitarget Inhibitors of Diverse Proteins in Metabolic Pathways." PLoS Computational Biology 9, no. 7 (2013): e1003127. http://dx.doi.org/10.1371/journal.pcbi.1003127.

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25

Hu, Xin, Fangyu Jing, Qingjun Wang, Linyang Shi, Yunfeng Cao, and Zhitu Zhu. "Alteration of Ornithine Metabolic Pathway in Colon Cancer and Multivariate Data Modelling for Cancer Diagnosis." Oncologie 23, no. 2 (2021): 203–17. http://dx.doi.org/10.32604/oncologie.2021.016155.

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26

Kuchel, Philip W., and David J. Philp. "Isotopomer subspaces as indicators of metabolic-pathway structure." Journal of Theoretical Biology 252, no. 3 (2008): 391–401. http://dx.doi.org/10.1016/j.jtbi.2007.05.039.

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27

Olavarria, K., J. De Ingeniis, D. C. Zielinski, et al. "Metabolic impact of an NADH-producing glucose-6-phosphate dehydrogenase in Escherichia coli." Microbiology 160, no. 12 (2014): 2780–93. http://dx.doi.org/10.1099/mic.0.082180-0.

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In Escherichia coli, the oxidative branch of the pentose phosphate pathway (oxPPP) is one of the major sources of NADPH when glucose is the sole carbon nutrient. However, unbalanced NADPH production causes growth impairment as observed in a strain lacking phosphoglucoisomerase (Δpgi). In this work, we studied the metabolic response of this bacterium to the replacement of its glucose-6-phosphate dehydrogenase (G6PDH) by an NADH-producing variant. The homologous enzyme from Leuconostoc mesenteroides was studied by molecular dynamics and site-directed mutagenesis to obtain the NAD-preferring LmG6
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28

Yuan, Jinlong, Xu Zhang, Xi Zhu, Enmin Feng, Hongchao Yin, and Zhilong Xiu. "Modelling and pathway identification involving the transport mechanism of a complex metabolic system in batch culture." Communications in Nonlinear Science and Numerical Simulation 19, no. 6 (2014): 2088–103. http://dx.doi.org/10.1016/j.cnsns.2013.10.021.

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29

Chong, Chuii, Mohd Mohamad, Safaai Deris, Mohd Shamsir, Yee Choon, and Lian Chai. "A Review on Modelling Methods, Pathway Simulation Software and Recent Development on Differential Evolution Algorithms for Metabolic Pathways in Systems Biology." Current Bioinformatics 9, no. 5 (2014): 509–21. http://dx.doi.org/10.2174/157489360905141014154242.

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30

Soons, Zita I. T. A., Eugénio C. Ferreira, and Isabel Rocha. "Identification of minimal metabolic pathway models consistent with phenotypic data." Journal of Process Control 21, no. 10 (2011): 1483–92. http://dx.doi.org/10.1016/j.jprocont.2011.05.012.

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31

Auslander, Noam, Allon Wagner, Matthew Oberhardt, and Eytan Ruppin. "Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction." PLOS Computational Biology 12, no. 9 (2016): e1005125. http://dx.doi.org/10.1371/journal.pcbi.1005125.

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32

Sylvester, Karl G., Shiying Hao, Jin You, et al. "Maternal metabolic profiling to assess fetal gestational age and predict preterm delivery: a two-centre retrospective cohort study in the US." BMJ Open 10, no. 12 (2020): e040647. http://dx.doi.org/10.1136/bmjopen-2020-040647.

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ObjectivesThe aim of this study was to develop a single blood test that could determine gestational age and estimate the risk of preterm birth by measuring serum metabolites. We hypothesised that serial metabolic modelling of serum analytes throughout pregnancy could be used to describe fetal gestational age and project preterm birth with a high degree of precision.Study designA retrospective cohort study.SettingTwo medical centres from the USA.ParticipantsThirty-six patients (20 full-term, 16 preterm) enrolled at Stanford University were used to develop gestational age and preterm birth risk
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33

SCHILLING, CHRISTOPHE H., DAVID LETSCHER, and BERNHARD Ø. PALSSON. "Theory for the Systemic Definition of Metabolic Pathways and their use in Interpreting Metabolic Function from a Pathway-Oriented Perspective." Journal of Theoretical Biology 203, no. 3 (2000): 229–48. http://dx.doi.org/10.1006/jtbi.2000.1073.

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34

Çakır, Tunahan, Cemil Serhan Tacer, and Kutlu Özergin Ülgen. "Metabolic pathway analysis of enzyme-deficient human red blood cells." Biosystems 78, no. 1-3 (2004): 49–67. http://dx.doi.org/10.1016/j.biosystems.2004.06.004.

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35

Kaleta, Christoph, Luís F. de Figueiredo, Ines Heiland, Steffen Klamt, and Stefan Schuster. "Special issue: Integration of OMICs datasets into Metabolic Pathway Analysis." Biosystems 105, no. 2 (2011): 107–8. http://dx.doi.org/10.1016/j.biosystems.2011.05.008.

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36

Carbonell, Pablo, Anne-Gaëlle Planson, Davide Fichera, and Jean-Loup Faulon. "A retrosynthetic biology approach to metabolic pathway design for therapeutic production." BMC Systems Biology 5, no. 1 (2011): 122. http://dx.doi.org/10.1186/1752-0509-5-122.

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37

van Riel, Natal A. W., Christian A. Tiemann, Joep Vanlier, and Peter A. J. Hilbers. "Applications of analysis of dynamic adaptations in parameter trajectories." Interface Focus 3, no. 2 (2013): 20120084. http://dx.doi.org/10.1098/rsfs.2012.0084.

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Metabolic profiling in combination with pathway-based analyses and computational modelling are becoming increasingly important in clinical and preclinical research. Modelling multi-factorial, progressive diseases requires the integration of molecular data at the metabolome, proteome and transcriptome levels. Also the dynamic interaction of organs and tissues needs to be considered. The processes involved cover time scales that are several orders of magnitude different. We report applications of a computational approach to bridge the scales and different levels of biological detail. Analysis of
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38

Voit, Eberhard O. "Characterizability of metabolic pathway systems from time series data." Mathematical Biosciences 246, no. 2 (2013): 315–25. http://dx.doi.org/10.1016/j.mbs.2013.01.008.

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39

Bakker, Barbara M., Karen van Eunen, Jeroen A. L. Jeneson, Natal A. W. van Riel, Frank J. Bruggeman, and Bas Teusink. "Systems biology from micro-organisms to human metabolic diseases: the role of detailed kinetic models." Biochemical Society Transactions 38, no. 5 (2010): 1294–301. http://dx.doi.org/10.1042/bst0381294.

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Human metabolic diseases are typically network diseases. This holds not only for multifactorial diseases, such as metabolic syndrome or Type 2 diabetes, but even when a single gene defect is the primary cause, where the adaptive response of the entire network determines the severity of disease. The latter may differ between individuals carrying the same mutation. Understanding the adaptive responses of human metabolism naturally requires a systems biology approach. Modelling of metabolic pathways in micro-organisms and some mammalian tissues has yielded many insights, qualitative as well as qu
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Sharma, Suraj, and Ralf Steuer. "Modelling microbial communities using biochemical resource allocation analysis." Journal of The Royal Society Interface 16, no. 160 (2019): 20190474. http://dx.doi.org/10.1098/rsif.2019.0474.

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To understand the functioning and dynamics of microbial communities is a fundamental challenge in current biology. To tackle this challenge, the construction of computational models of interacting microbes is an indispensable tool. There is, however, a large chasm between ecologically motivated descriptions of microbial growth used in many current ecosystems simulations, and detailed metabolic pathway and genome-based descriptions developed in the context of systems and synthetic biology. Here, we seek to demonstrate how resource allocation models of microbial growth offer the potential to adv
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Lesnik, Elena A., Gary B. Fogel, Dana Weekes, et al. "Identification of conserved regulatory RNA structures in prokaryotic metabolic pathway genes." Biosystems 80, no. 2 (2005): 145–54. http://dx.doi.org/10.1016/j.biosystems.2004.11.002.

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Boghigian, Brett A., Hai Shi, Kyongbum Lee, and Blaine A. Pfeifer. "Utilizing elementary mode analysis, pathway thermodynamics, and a genetic algorithm for metabolic flux determination and optimal metabolic network design." BMC Systems Biology 4, no. 1 (2010): 49. http://dx.doi.org/10.1186/1752-0509-4-49.

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Wieder, Cecilia, Clément Frainay, Nathalie Poupin, et al. "Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis." PLOS Computational Biology 17, no. 9 (2021): e1009105. http://dx.doi.org/10.1371/journal.pcbi.1009105.

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Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention. Using five publicly available datasets, we demonstrated that changes in parameters such as the background set, differential metabolite selection methods, and pathway database used can result in profoundly diffe
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Dubey, Swati, Sheela Joshi, Goshali Dwivedi, and Rajendra Prasad. "Application of Petri Net Theory for Modelling and Validation of Menthol Biosynthesis." Asian Journal of Organic & Medicinal Chemistry 5, no. 4 (2020): 312–18. http://dx.doi.org/10.14233/ajomc.2020.ajomc-p297.

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An essential step in network modelling is to validate the network model. Petri net theory provides algorithms and methods, which can be applied directly to metabolic network modelling and analysis in order to validate the model. This paper describes the thriving application of Petri net theory for model validation of biosynthesis of menthol using the well-established Petri net analysis technique of place and transition invariants. Because of the complexity of metabolic networks and their regulation, formal modelling is a useful method to improve the understanding of these systems. A petri net
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45

Kommedal, R., R. Bakke, J. Dockery, and P. Stoodley. "Modelling production of extracellular polymeric substances in a pseudomonas aeruginosa chemostat culture." Water Science and Technology 43, no. 6 (2001): 129–34. http://dx.doi.org/10.2166/wst.2001.0357.

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Formation of extracellular polymeric substances (EPSs) by mucoid pseudomonas aeruginosa was investigated using literature data from chemostat cultures. The data were used to calibrate a product formation regime allowing substrate sufficient and endogenous EPS formation. Yield coefficients for both formation conditions were elucidated based on metabolic pathway analysis. Growth and non-growth related specific formation rates of 0.18 g CEPS/g Ccell/h and 0.03 1/h were estimated, respectively. The exogenous and endogenous EPS yield was found to be 0.77 g CEPS/g Cglu and 0.79 g CEPS/g Ccell, respe
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46

Quo, Chang F., Richard A. Moffitt, Alfred H. Merrill, and May D. Wang. "Adaptive Control Model Reveals Systematic Feedback and Key Molecules in Metabolic Pathway Regulation." Journal of Computational Biology 18, no. 2 (2011): 169–82. http://dx.doi.org/10.1089/cmb.2010.0215.

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47

Sakamoto, Naoto. "A transfer-function representation for regulatory responses of a controlled metabolic pathway." Biosystems 20, no. 4 (1987): 317–27. http://dx.doi.org/10.1016/0303-2647(87)90050-5.

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48

Vinding, Rebecca Kofod, Daniela Rago, Rachel S. Kelly, et al. "Delayed Motor Milestones Achievement in Infancy Associates with Perturbations of Amino Acids and Lipid Metabolic Pathways." Metabolites 10, no. 9 (2020): 337. http://dx.doi.org/10.3390/metabo10090337.

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The relationship between developmental milestone achievement in infancy and later cognitive function and mental health is well established, but underlying biochemical mechanisms are poorly described. Our study aims to discover pathways connected to motor milestone achievement during infancy by using untargeted plasma metabolomic profiles from 571 six-month-old children in connection with age of motor milestones achievement (Denver Developmental Index) in the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC2010) mother–child cohort. We used univariate regression models and mul
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49

Ye, Jianxiong, Honglei Xu, Xueying Huang, Chongrong Ke, and Enmin Feng. "Dynamics Analysis and Prediction of Genetic Regulation in Glycerol Metabolic Network via Structural Kinetic Modelling." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/673120.

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Glycerol can be biologically converted to 1,3-propanediol (1,3-PD) byKlebsiella pneumoniae. In the synthesis pathway of 1,3-PD, the accumulation of an intermediary metabolite 3-hydroxypropionaldehyde (3-HPA) would cause an irreversible cessation of the dynamic system. Genetic manipulation on the key enzymes which control the formation rate and consumption rate of 3-HPA would decrease the accumulation of 3-HPA, resulting in nonlinear regulation on the dynamic system. The interest of this work is to focus on analyzing the influence of 3-HPA inhibition on the stability of the dynamic system. Due
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Bartl, Martin, Pu Li, and Stefan Schuster. "Modelling the optimal timing in metabolic pathway activation—Use of Pontryagin's Maximum Principle and role of the Golden section." Biosystems 101, no. 1 (2010): 67–77. http://dx.doi.org/10.1016/j.biosystems.2010.04.007.

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