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Journal articles on the topic 'Biochemical network'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Wang, Shuqiang, Yanyan Shen, Jinxing Hu, Ning Li, and Dewei Zeng. "Dynamic analysis of biochemical network using complex network method." Thermal Science 19, no. 4 (2015): 1249–53. http://dx.doi.org/10.2298/tsci1504249w.

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In this study, the stochastic biochemical reaction model is proposed based on the law of mass action and complex network theory. The dynamics of biochemical reaction system is presented as a set of non-linear differential equations and analyzed at the molecular-scale. Given the initial state and the evolution rules of the biochemical reaction system, the system can achieve homeostasis. Compared with random graph, the biochemical reaction network has larger information capacity and is more efficient in information transmission. This is consistent with theory of evolution.

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2

Zhou, Yanxiang, Juliane Liepe, Xia Sheng, Michael P. H. Stumpf, and Chris Barnes. "GPU accelerated biochemical network simulation." Bioinformatics 27, no. 6 (2011): 874–76. http://dx.doi.org/10.1093/bioinformatics/btr015.

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3

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 (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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4

Bowsher, Clive G. "Information processing by biochemical networks: a dynamic approach." Journal of The Royal Society Interface 8, no. 55 (2010): 186–200. http://dx.doi.org/10.1098/rsif.2010.0287.

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Understanding how information is encoded and transferred by biochemical networks is of fundamental importance in cellular and systems biology. This requires analysis of the relationships between the stochastic trajectories of the constituent molecular (or submolecular) species that comprise the network. We describe how to identify conditional independences between the trajectories or time courses of groups of species. These are robust network properties that provide important insight into how information is processed. An entire network can then be decomposed exactly into modules on informational grounds. In the context of signalling networks with multiple inputs, the approach identifies the routes and species involved in sequential information processing between input and output modules. An algorithm is developed which allows automated identification of decompositions for large networks and visualization using a tree that encodes the conditional independences. Only stoichiometric information is used and neither simulations nor knowledge of rate parameters are required. A bespoke version of the algorithm for signalling networks identifies the routes of sequential encoding between inputs and outputs, visualized as paths in the tree. Application to the toll-like receptor signalling network reveals that inputs can be informative in ways unanticipated by steady-state analyses, that the information processing structure is not well described as a bow tie, and that encoding for the interferon response is unusually sparse compared with other outputs of this innate immune system.

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5

Kim, J., D. G. Bates, I. Postlethwaite, L. Ma, and P. A. Iglesias. "Robustness analysis of biochemical network models." IEE Proceedings - Systems Biology 153, no. 3 (2006): 96. http://dx.doi.org/10.1049/ip-syb:20050024.

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6

Oates, Chris J., Frank Dondelinger, Nora Bayani, James Korkola, Joe W. Gray, and Sach Mukherjee. "Causal network inference using biochemical kinetics." Bioinformatics 30, no. 17 (2014): i468—i474. http://dx.doi.org/10.1093/bioinformatics/btu452.

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7

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 (2013): 121–37. http://dx.doi.org/10.1080/03610918.2011.633200.

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8

Klipp, Edda, Rebecca C. Wade, and Ursula Kummer. "Biochemical network-based drug-target prediction." Current Opinion in Biotechnology 21, no. 4 (2010): 511–16. http://dx.doi.org/10.1016/j.copbio.2010.05.004.

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9

Köhler, Nikolai, Tim Daniel Rose, Lisa Falk, and Josch Konstantin Pauling. "Investigating Global Lipidome Alterations with the Lipid Network Explorer." Metabolites 11, no. 8 (2021): 488. http://dx.doi.org/10.3390/metabo11080488.

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Lipids play an important role in biological systems and have the potential to serve as biomarkers in medical applications. Advances in lipidomics allow identification of hundreds of lipid species from biological samples. However, a systems biological analysis of the lipidome, by incorporating pathway information remains challenging, leaving lipidomics behind compared to other omics disciplines. An especially uncharted territory is the integration of statistical and network-based approaches for studying global lipidome changes. Here we developed the Lipid Network Explorer (LINEX), a web-tool addressing this gap by providing a way to visualize and analyze functional lipid metabolic networks. It utilizes metabolic rules to match biochemically connected lipids on a species level and combine it with a statistical correlation and testing analysis. Researchers can customize the biochemical rules considered, to their tissue or organism specific analysis and easily share them. We demonstrate the benefits of combining network-based analyses with statistics using publicly available lipidomics data sets. LINEX facilitates a biochemical knowledge-based data analysis for lipidomics. It is availableas a web-application and as a publicly available docker container.

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10

PAPIN, J., J. REED, and B. PALSSON. "Hierarchical thinking in network biology: the unbiased modularization of biochemical networks." Trends in Biochemical Sciences 29, no. 12 (2004): 641–47. http://dx.doi.org/10.1016/j.tibs.2004.10.001.

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11

Gates, Alexander J., Rion Brattig Correia, Xuan Wang, and Luis M. Rocha. "The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling." Proceedings of the National Academy of Sciences 118, no. 12 (2021): e2022598118. http://dx.doi.org/10.1073/pnas.2022598118.

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The ability to map causal interactions underlying genetic control and cellular signaling has led to increasingly accurate models of the complex biochemical networks that regulate cellular function. These network models provide deep insights into the organization, dynamics, and function of biochemical systems: for example, by revealing genetic control pathways involved in disease. However, the traditional representation of biochemical networks as binary interaction graphs fails to accurately represent an important dynamical feature of these multivariate systems: some pathways propagate control signals much more effectively than do others. Such heterogeneity of interactions reflects canalization—the system is robust to dynamical interventions in redundant pathways but responsive to interventions in effective pathways. Here, we introduce the effective graph, a weighted graph that captures the nonlinear logical redundancy present in biochemical network regulation, signaling, and control. Using 78 experimentally validated models derived from systems biology, we demonstrate that 1) redundant pathways are prevalent in biological models of biochemical regulation, 2) the effective graph provides a probabilistic but precise characterization of multivariate dynamics in a causal graph form, and 3) the effective graph provides an accurate explanation of how dynamical perturbation and control signals, such as those induced by cancer drug therapies, propagate in biochemical pathways. Overall, our results indicate that the effective graph provides an enriched description of the structure and dynamics of networked multivariate causal interactions. We demonstrate that it improves explainability, prediction, and control of complex dynamical systems in general and biochemical regulation in particular.

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12

LIU, BING, and P. S. THIAGARAJAN. "MODELING AND ANALYSIS OF BIOPATHWAYS DYNAMICS." Journal of Bioinformatics and Computational Biology 10, no. 04 (2012): 1231001. http://dx.doi.org/10.1142/s0219720012310014.

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Cellular processes are governed and coordinated by a multitude of biopathways. A pathway can be viewed as a complex network of biochemical reactions. The dynamics of this network largely determines the functioning of the pathway. Hence the modeling and analysis of biochemical networks dynamics is an important problem and is an active area of research. Here we review quantitative models of biochemical networks based on ordinary differential equations (ODEs). We mainly focus on the parameter estimation and sensitivity analysis problems and survey the current methods for tackling them. In this context we also highlight a recently developed probabilistic approximation technique using which these two problems can be considerably simplified.

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13

Cinquemani, Eugenio. "Identifiability and Reconstruction of Biochemical Reaction Networks from Population Snapshot Data." Processes 6, no. 9 (2018): 136. http://dx.doi.org/10.3390/pr6090136.

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Inference of biochemical network models from experimental data is a crucial problem in systems and synthetic biology that includes parameter calibration but also identification of unknown interactions. Stochastic modelling from single-cell data is known to improve identifiability of reaction network parameters for specific systems. However, general results are lacking, and the advantage over deterministic, population-average approaches has not been explored for network reconstruction. In this work, we study identifiability and propose new reconstruction methods for biochemical interaction networks. Focusing on population-snapshot data and networks with reaction rates affine in the state, for parameter estimation, we derive general methods to test structural identifiability and demonstrate them in connection with practical identifiability for a reporter gene in silico case study. In the same framework, we next develop a two-step approach to the reconstruction of unknown networks of interactions. We apply it to compare the achievable network reconstruction performance in a deterministic and a stochastic setting, showing the advantage of the latter, and demonstrate it on population-snapshot data from a simulated example.

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14

HEIDEL, JACK, JOHN MALONEY, CHRISTOPHER FARROW, and J. A. ROGERS. "FINDING CYCLES IN SYNCHRONOUS BOOLEAN NETWORKS WITH APPLICATIONS TO BIOCHEMICAL SYSTEMS." International Journal of Bifurcation and Chaos 13, no. 03 (2003): 535–52. http://dx.doi.org/10.1142/s0218127403006765.

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This paper is an analytical study of Boolean networks. The motivation is our desire to understand the large, complicated and interconnected pathways which comprise intracellular biochemical signal transduction networks. The simplest possible conceptual model that mimics signal transduction with sigmoidal kinetics is the n-node Boolean network each of whose elements or nodes has the value 0 (off) or 1 (on) at any given time T = 0, 1, 2, …. A Boolean network has 2nstates all of which are either on periodic cycles (including fixed points) or transients leading to cycles. Thus one understands a Boolean network by determining the number and length of its cycles. The problem one must circumvent is the large number of states (2n) since the networks we are interested in have 100 or more elements. Thus we concentrate on developing size n methods rather than the impossible task of enumerating all 2nstates. This is done as follows: the dynamics of the network can be described by n polynomial equations which describe the logical function which determines the interaction at each node. Iterating the equations one step at a time finds all fixed points, period two cycles, period three cycles, etc. This is a general method that can be used to determine the fixed points and moderately large periodic cycles of any size network, but it is not useful in finding the largest cycles in a large network. However, we also show that the network equations can often be reduced to scalar form, which makes the cycle structure much more transparent. The scalar equations method is a true "size n" method and several examples (including nontrivial biochemical systems) are examined.

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15

Liebermeister, Wolfram, Ulrike Baur, and Edda Klipp. "Biochemical network models simplified by balanced truncation." FEBS Journal 272, no. 16 (2005): 4034–43. http://dx.doi.org/10.1111/j.1742-4658.2005.04780.x.

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16

Rost, U., and U. Kummer. "Visualisation of biochemical network simulations with SimWiz." Systems Biology 1, no. 1 (2004): 184–89. http://dx.doi.org/10.1049/sb:20045018.

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17

Smith, Joshua I., Mike Steel, and Wim Hordijk. "Autocatalytic sets in a partitioned biochemical network." Journal of Systems Chemistry 5, no. 1 (2014): 2. http://dx.doi.org/10.1186/1759-2208-5-2.

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18

Qian, Hong, Daniel A. Beard, and Shou-dan Liang. "Stoichiometric network theory for nonequilibrium biochemical systems." European Journal of Biochemistry 270, no. 3 (2003): 415–21. http://dx.doi.org/10.1046/j.1432-1033.2003.03357.x.

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19

Rubina, Tatjana. "Tools for analysis of biochemical network topology." Biosystems and Information technology 1, no. 1 (2012): 25–31. http://dx.doi.org/10.11592/bit.121101.

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20

Okamoto, Masahiro, Yukihiro Maki, Tatsuya Sekiguchi, and Satoshi Yoshida. "Self-organization in a biochemical-neuron network." Physica D: Nonlinear Phenomena 84, no. 1-2 (1995): 194–203. http://dx.doi.org/10.1016/0167-2789(95)00015-v.

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21

Gasparyan, Manvel, Arnout Van Messem, and Shodhan Rao. "An Automated Model Reduction Method for Biochemical Reaction Networks." Symmetry 12, no. 8 (2020): 1321. http://dx.doi.org/10.3390/sym12081321.

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We propose a new approach to the model reduction of biochemical reaction networks governed by various types of enzyme kinetics rate laws with non-autocatalytic reactions, each of which can be reversible or irreversible. This method extends the approach for model reduction previously proposed by Rao et al. which proceeds by the step-wise reduction in the number of complexes by Kron reduction of the weighted Laplacian corresponding to the complex graph of the network. The main idea in the current manuscript is based on rewriting the mathematical model of a reaction network as a model of a network consisting of linkage classes that contain more than one reaction. It is done by joining certain distinct linkage classes into a single linkage class by using the conservation laws of the network. We show that this adjustment improves the extent of applicability of the method proposed by Rao et al. We automate the entire reduction procedure using Matlab. We test our automated model reduction to two real-life reaction networks, namely, a model of neural stem cell regulation and a model of hedgehog signaling pathway. We apply our reduction approach to meaningfully reduce the number of complexes in the complex graph corresponding to these networks. When the number of species’ concentrations in the model of neural stem cell regulation is reduced by 33.33%, the difference between the dynamics of the original model and the reduced model, quantified by an error integral, is only 4.85%. Likewise, when the number of species’ concentrations is reduced by 33.33% in the model of hedgehog signaling pathway, the difference between the dynamics of the original model and the reduced model is only 6.59%.

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22

ENCISO, GERMAN, RADEK ERBAN, and JINSU KIM. "Identifiability of stochastically modelled reaction networks." European Journal of Applied Mathematics 32, no. 5 (2021): 865–87. http://dx.doi.org/10.1017/s0956792520000492.

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Chemical reaction networks describe interactions between biochemical species. Once an underlying reaction network is given for a biochemical system, the system dynamics can be modelled with various mathematical frameworks such as continuous-time Markov processes. In this manuscript, the identifiability of the underlying network structure with a given stochastic system dynamics is studied. It is shown that some data types related to the associated stochastic dynamics can uniquely identify the underlying network structure as well as the system parameters. The accuracy of the presented network inference is investigated when given dynamical data are obtained via stochastic simulations.

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23

Sun, Yonghui, Zhinong Wei, and Guoqiang Sun. "Positive Stability Analysis and Bio-Circuit Design for Nonlinear Biochemical Networks." Abstract and Applied Analysis 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/717489.

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This paper is concerned with positive stability analysis and bio-circuits design for nonlinear biochemical networks. A fuzzy interpolation approach is employed to approximate nonlinear biochemical networks. Based on the Lyapunov stability theory, sufficient conditions are developed to guarantee the equilibrium points of nonlinear biochemical networks to be positive and asymptotically stable. In addition, a constrained bio-circuits design with positive control input is also considered. It is shown that the conditions can be formulated as a solution to a convex optimization problem, which can be easily facilitated by using the Matlab LMI control toolbox. Finally, a real biochemical network model is provided to illustrate the effectiveness and validity of the obtained results.

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24

Davis, Jacob D., and Eberhard O. Voit. "Metrics for regulated biochemical pathway systems." Bioinformatics 35, no. 12 (2018): 2118–24. http://dx.doi.org/10.1093/bioinformatics/bty942.

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Abstract Motivation The assessment of graphs through crisp numerical metrics has long been a hallmark of biological network analysis. However, typical graph metrics ignore regulatory signals that are crucially important for optimal pathway operation, for instance, in biochemical or metabolic studies. Here we introduce adjusted metrics that are applicable to both static networks and dynamic systems. Results The metrics permit quantitative characterizations of the importance of regulation in biochemical pathway systems, including systems designed for applications in synthetic biology or metabolic engineering. They may also become criteria for effective model reduction. Availability and implementation The source code is available at https://gitlab.com/tienbien44/metrics-bsa

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25

Hadlich, Frieder, Stephan Noack, and Wolfgang Wiechert. "Translating biochemical network models between different kinetic formats." Metabolic Engineering 11, no. 2 (2009): 87–100. http://dx.doi.org/10.1016/j.ymben.2008.10.002.

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26

Arceo, Carlene Perpetua P., Editha C. Jose, Alberto Marin-Sanguino, and Eduardo R. Mendoza. "Chemical reaction network approaches to Biochemical Systems Theory." Mathematical Biosciences 269 (November 2015): 135–52. http://dx.doi.org/10.1016/j.mbs.2015.08.022.

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27

Dasgupta, Anjan Kr. "Finite time thermodynamic coupling in a biochemical network." Systems and Synthetic Biology 8, no. 1 (2014): 41–45. http://dx.doi.org/10.1007/s11693-014-9130-1.

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28

Linder, Daniel F., and Grzegorz A. Rempala. "Algebraic statistical model for biochemical network dynamics inference." Journal of Coupled Systems and Multiscale Dynamics 1, no. 4 (2013): 468–75. http://dx.doi.org/10.1166/jcsmd.2013.1032.

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29

Iordache, O., J. P. Corriou, L. Garrido-Sanchez, C. Fonteix, and D. Tondeur. "Neural network frames. Application to biochemical kinetic diagnosis." Computers & Chemical Engineering 17, no. 11 (1993): 1101–13. http://dx.doi.org/10.1016/0098-1354(93)80091-z.

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30

Inoue, Kentaro, Sayaka Tomeda, Shinpei Tonami, Yuki Shimokawa, Masayo Ono, and Hiroyuki Kurata. "CADLIVE Converter for constructing a biochemical network map." Biochemical Engineering Journal 54, no. 3 (2011): 200–206. http://dx.doi.org/10.1016/j.bej.2011.02.022.

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31

Ma, Wenzhe, Ala Trusina, Hana El-Samad, Wendell A. Lim, and Chao Tang. "Defining Network Topologies that Can Achieve Biochemical Adaptation." Cell 138, no. 4 (2009): 760–73. http://dx.doi.org/10.1016/j.cell.2009.06.013.

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32

Michnick, Stephen W. "Protein fragment complementation strategies for biochemical network mapping." Current Opinion in Biotechnology 14, no. 6 (2003): 610–17. http://dx.doi.org/10.1016/j.copbio.2003.10.014.

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33

Price, Nathan D., and Ilya Shmulevich. "Biochemical and statistical network models for systems biology." Current Opinion in Biotechnology 18, no. 4 (2007): 365–70. http://dx.doi.org/10.1016/j.copbio.2007.07.009.

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34

Warne, David J., Ruth E. Baker, and Matthew J. Simpson. "Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art." Journal of The Royal Society Interface 16, no. 151 (2019): 20180943. http://dx.doi.org/10.1098/rsif.2018.0943.

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Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is essential to understand the complex dynamics of living things. Mathematical idealizations of biochemically reacting systems must be able to capture stochastic phenomena. While robust theory exists to describe such stochastic models, the computational challenges in exploring these models can be a significant burden in practice since realistic models are analytically intractable. Determining the expected behaviour and variability of a stochastic biochemical reaction network requires many probabilistic simulations of its evolution. Using a biochemical reaction network model to assist in the interpretation of time-course data from a biological experiment is an even greater challenge due to the intractability of the likelihood function for determining observation probabilities. These computational challenges have been subjects of active research for over four decades. In this review, we present an accessible discussion of the major historical developments and state-of-the-art computational techniques relevant to simulation and inference problems for stochastic biochemical reaction network models. Detailed algorithms for particularly important methods are described and complemented with Matlab ® implementations. As a result, this review provides a practical and accessible introduction to computational methods for stochastic models within the life sciences community.

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35

Cardelli, Luca, Mirco Tribastone, and Max Tschaikowski. "From electric circuits to chemical networks." Natural Computing 19, no. 1 (2019): 237–48. http://dx.doi.org/10.1007/s11047-019-09761-7.

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Abstract Electric circuits manipulate electric charge and magnetic flux via a small set of discrete components to implement useful functionality over continuous time-varying signals represented by currents and voltages. Much of the same functionality is useful to biological organisms, where it is implemented by a completely different set of discrete components (typically proteins) and signal representations (typically via concentrations). We describe how to take a linear electric circuit and systematically convert it to a chemical reaction network of the same functionality, as a dynamical system. Both the structure and the components of the electric circuit are dissolved in the process, but the resulting chemical network is intelligible. This approach provides access to a large library of well-studied devices, from analog electronics, whose chemical network realization can be compared to natural biochemical networks, or used to engineer synthetic biochemical networks.

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36

Waldherr, Steffen, Jan Hasenauer, and Frank Allgöwer. "Estimation of biochemical network parameter distributions in cell populations." IFAC Proceedings Volumes 42, no. 10 (2009): 1265–70. http://dx.doi.org/10.3182/20090706-3-fr-2004.00210.

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37

Kim, Jinkyung, Younghee Lee, and Il Moon. "Automatic Verification of Biochemical Network Using Model Checking Method." Chinese Journal of Chemical Engineering 16, no. 1 (2008): 90–94. http://dx.doi.org/10.1016/s1004-9541(08)60043-9.

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38

Wang, Shu-Qiang, and Han-Xiong Li. "Random Network Based Dynamic Analysis for Biochemical Reaction System." Advanced Science Letters 10, no. 1 (2012): 554–58. http://dx.doi.org/10.1166/asl.2012.3381.

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39

Pfaffelhuber, Peter, and Lea Popovic. "How spatial heterogeneity shapes multiscale biochemical reaction network dynamics." Journal of The Royal Society Interface 12, no. 104 (2015): 20141106. http://dx.doi.org/10.1098/rsif.2014.1106.

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Spatial heterogeneity in cells can be modelled using distinct compartments connected by molecular movement between them. In addition to movement, changes in the amount of molecules are due to biochemical reactions within compartments, often such that some molecular types fluctuate on a slower timescale than others. It is natural to ask the following questions: how sensitive is the dynamics of molecular types to their own spatial distribution, and how sensitive are they to the distribution of others? What conditions lead to effective homogeneity in biochemical dynamics despite heterogeneity in molecular distribution? What kind of spatial distribution is optimal from the point of view of some downstream product? Within a spatially heterogeneous multiscale model, we consider two notions of dynamical homogeneity (full homogeneity and homogeneity for the fast subsystem), and consider their implications under different timescales for the motility of molecules between compartments. We derive rigorous results for their dynamics and long-term behaviour, and illustrate them with examples of a shared pathway, Michaelis–Menten enzymatic kinetics and autoregulating feedbacks. Using stochastic averaging of fast fluctuations to their quasi-steady-state distribution, we obtain simple analytic results that significantly reduce the complexity and expedite simulation of stochastic compartment models of chemical reactions.

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40

Celik, Emrah, Midhat H. Abdulreda, Dony Maiguel, Jie Li, and Vincent T. Moy. "Rearrangement of microtubule network under biochemical and mechanical stimulations." Methods 60, no. 2 (2013): 195–201. http://dx.doi.org/10.1016/j.ymeth.2013.02.014.

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41

Golightly, Andrew, and Darren J. Wilkinson. "Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models." Journal of Computational Biology 13, no. 3 (2006): 838–51. http://dx.doi.org/10.1089/cmb.2006.13.838.

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42

Plesa, Tomislav, Konstantinos C. Zygalakis, David F. Anderson, and Radek Erban. "Noise control for molecular computing." Journal of The Royal Society Interface 15, no. 144 (2018): 20180199. http://dx.doi.org/10.1098/rsif.2018.0199.

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Synthetic biology is a growing interdisciplinary field, with far-reaching applications, which aims to design biochemical systems that behave in a desired manner. With the advancement in nucleic-acid-based technology in general, and strand-displacement DNA computing in particular, a large class of abstract biochemical networks may be physically realized using nucleic acids. Methods for systematic design of the abstract systems with prescribed behaviours have been predominantly developed at the (less-detailed) deterministic level. However, stochastic effects, neglected at the deterministic level, are increasingly found to play an important role in biochemistry. In such circumstances, methods for controlling the intrinsic noise in the system are necessary for a successful network design at the (more-detailed) stochastic level. To bridge the gap, the noise-control algorithm for designing biochemical networks is developed in this paper. The algorithm structurally modifies any given reaction network under mass-action kinetics, in such a way that (i) controllable state-dependent noise is introduced into the stochastic dynamics, while (ii) the deterministic dynamics are preserved. The capabilities of the algorithm are demonstrated on a production–decay reaction system, and on an exotic system displaying bistability. For the production–decay system, it is shown that the algorithm may be used to redesign the network to achieve noise-induced multistability. For the exotic system, the algorithm is used to redesign the network to control the stochastic switching, and achieve noise-induced oscillations.

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McGeary, Sean E., Kathy S. Lin, Charlie Y. Shi, et al. "The biochemical basis of microRNA targeting efficacy." Science 366, no. 6472 (2019): eaav1741. http://dx.doi.org/10.1126/science.aav1741.

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MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of messenger RNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA-target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute-miRNA complexes and all sequences ≤12 nucleotides in length. This approach revealed noncanonical target sites specific to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.

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44

Gormley, Michael, Viswanadha U. Akella, Judy N. Quong, and Andrew A. Quong. "An Integrated Framework to Model Cellular Phenotype as a Component of Biochemical Networks." Advances in Bioinformatics 2011 (November 29, 2011): 1–14. http://dx.doi.org/10.1155/2011/608295.

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Identification of regulatory molecules in signaling pathways is critical for understanding cellular behavior. Given the complexity of the transcriptional gene network, the relationship between molecular expression and phenotype is difficult to determine using reductionist experimental methods. Computational models provide the means to characterize regulatory mechanisms and predict phenotype in the context of gene networks. Integrating gene expression data with phenotypic data in transcriptional network models enables systematic identification of critical molecules in a biological network. We developed an approach based on fuzzy logic to model cell budding in Saccharomyces cerevisiae using time series expression microarray data of the cell cycle. Cell budding is a phenotype of viable cells undergoing division. Predicted interactions between gene expression and phenotype reflected known biological relationships. Dynamic simulation analysis reproduced the behavior of the yeast cell cycle and accurately identified genes and interactions which are essential for cell viability.

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Murabito, Ettore, Kieran Smallbone, Jonathan Swinton, Hans V. Westerhoff, and Ralf Steuer. "A probabilistic approach to identify putative drug targets in biochemical networks." Journal of The Royal Society Interface 8, no. 59 (2010): 880–95. http://dx.doi.org/10.1098/rsif.2010.0540.

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Network-based drug design holds great promise in clinical research as a way to overcome the limitations of traditional approaches in the development of drugs with high efficacy and low toxicity. This novel strategy aims to study how a biochemical network as a whole, rather than its individual components, responds to specific perturbations in different physiological conditions. Proteins exerting little control over normal cells and larger control over altered cells may be considered as good candidates for drug targets. The application of network-based drug design would greatly benefit from using an explicit computational model describing the dynamics of the system under investigation. However, creating a fully characterized kinetic model is not an easy task, even for relatively small networks, as it is still significantly hampered by the lack of data about kinetic mechanisms and parameters values. Here, we propose a Monte Carlo approach to identify the differences between flux control profiles of a metabolic network in different physiological states, when information about the kinetics of the system is partially or totally missing. Based on experimentally accessible information on metabolic phenotypes, we develop a novel method to determine probabilistic differences in the flux control coefficients between the two observable phenotypes. Knowledge of how differences in flux control are distributed among the different enzymatic steps is exploited to identify points of fragility in one of the phenotypes. Using a prototypical cancerous phenotype as an example, we demonstrate how our approach can assist researchers in developing compounds with high efficacy and low toxicity.

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de la Fuente, Alberto. "Condensing Biochemistry into Gene Regulatory Networks." International Journal of Natural Computing Research 4, no. 3 (2014): 1–25. http://dx.doi.org/10.4018/ijncr.2014070101.

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Gene Regulatory Networks are models of gene regulation. Inferring such model from genome-wide gene-expression measurements is one of the key challenges in modern biology, and a large number of algorithms have been proposed for this task. As there is still much confusion in the current literature as to what precisely Gene Regulatory Networks are, it is important to provide a definition that is as unambiguous as possible. In this paper the author provides such a definition and explain what Gene Regulatory Networks are in terms of the underlying biochemical processes. The author will use a linear approximation to the in general non-linear kinetics underlying interactions in biochemical systems and show how a biochemical system can be ‘condensed' into a more compact description, i.e. Gene Regulatory Networks. Important differences between the defined Gene Regulatory Networks and other network models for gene regulation, i.e. Transcriptional Regulatory Networks and Co-Expression Networks, are also discussed.

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Droste, Peter, Wolfgang Wiechert, and Katharina Nöh. "Semi-automatic drawing of metabolic networks." Information Visualization 11, no. 3 (2011): 171–87. http://dx.doi.org/10.1177/1473871611413565.

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In the living cell, biochemical reactions catalyzed by enzymes are the drivers for metabolic processes like growth, energy production, and replication. Metabolic networks are the representation of these processes describing the complex interactions of biochemical compounds. The large amount of manifold data concerning metabolic networks continually arising from current research activities in biotechnology leads to the great challenge of information visualization. Visualizing information in networks first of all requires appropriate network diagrams. In the context of metabolic networks, historical conventions regarding the network layout have been established. These layouts are not realizable by prevailing algorithms for automatic graph drawing. Hence, manual graph drawing is the predominating way to set up metabolic network diagrams. This is very time-consuming without software support, especially considering large networks with more than 500 nodes. We present a semi-automatic approach to drawing networks which relies on manual editing supported by two concepts of the interactive and automatic arrangement of nodes and edges. The first concept, called the layout pattern, uses an arbitrarily shaped skeleton as a backbone for the arrangement of nodes and edges. The second concept allows us to wrap a set of repeating drawing steps onto a so-called motif stamp, which can be appended to other parts of a diagram during the drawing process. Finally, a case study demonstrates that both semi-automatic drawing techniques diminish the time to be devoted for the manual network drawing process.

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Chen, Lei, and Fei Liu. "Hierarchical Process Neural Network Model within Variable-Sampling Time." Applied Mechanics and Materials 20-23 (January 2010): 920–25. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.920.

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The biochemical processes are usually described as seriously time varying and nonlinear dynamic systems. It is very costly and difficult to build their first-principle models due to the absence of inherent mechanism and efficient on-line sensors. In this paper, a hierarchical process neural network (HPNN) model within variable-sampling time has been proposed. Simulation is based on penicillin fed-batch fermentation process, shows that the model established is more accurately and efficient, and suffice for the requirements of control and optimization for biochemical processes.

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Kubota, Yoshihisa, and James M. Bower. "Decoding time-varying calcium signals by the postsynaptic biochemical network." Neurocomputing 26-27 (June 1999): 29–38. http://dx.doi.org/10.1016/s0925-2312(99)00085-5.

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Noack, S., A. Wahl, M. Haunschild, E. Qeli, B. Freisleben, and W. Wiechert. "Visualizing regulatory interdependencies and parameter sensitivities in biochemical network models." Mathematics and Computers in Simulation 79, no. 4 (2008): 991–98. http://dx.doi.org/10.1016/j.matcom.2008.02.008.

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