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Добірка наукової літератури з теми "Systems biology model"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 29 січня 2023

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Статті в журналах з теми "Systems biology model"

1

Bolker, Jessica A. "Model systems in developmental biology." BioEssays 17, no. 5 (May 1995): 451–55. http://dx.doi.org/10.1002/bies.950170513.

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2

Yalcin, Gizem Damla, Nurseda Danisik, Rana Can Baygin, and Ahmet Acar. "Systems Biology and Experimental Model Systems of Cancer." Journal of Personalized Medicine 10, no. 4 (October 19, 2020): 180. http://dx.doi.org/10.3390/jpm10040180.

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Анотація:
Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer.
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3

van Gend, Carel, and Jacky L. Snoep. "Systems biology model databases and resources." Essays in Biochemistry 45 (September 30, 2008): 223–36. http://dx.doi.org/10.1042/bse0450223.

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Анотація:
Systems biology aims at a quantitative understanding of systemic behaviour as a function of its components and their interactions. In systems biology studies computer models play an important role: (i) to integrate the components’ behaviour and (ii) to analyse experimental data sets. With the growing number of kinetic models that are being constructed for parts of biological systems, it has become important to store these models and make them available in a standard form, such that these models can be combined, eventually leading to a model of a complete system. In the present chapter we describe database initiatives that contain kinetic models for biological systems, together with a number of other systems biology resources related to kinetic modelling.
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4

Garbern, J. C., C. L. Mummery, and R. T. Lee. "Model Systems for Cardiovascular Regenerative Biology." Cold Spring Harbor Perspectives in Medicine 3, no. 4 (April 1, 2013): a014019. http://dx.doi.org/10.1101/cshperspect.a014019.

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5

Robert, Jason Scott. "Model systems in stem cell biology." BioEssays 26, no. 9 (2004): 1005–12. http://dx.doi.org/10.1002/bies.20100.

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6

Edelman, Lucas B., Sriram Chandrasekaran, and Nathan D. Price. "Systems biology of embryogenesis." Reproduction, Fertility and Development 22, no. 1 (2010): 98. http://dx.doi.org/10.1071/rd09215.

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Анотація:
The development of a complete organism from a single cell involves extraordinarily complex orchestration of biological processes that vary intricately across space and time. Systems biology seeks to describe how all elements of a biological system interact in order to understand, model and ultimately predict aspects of emergent biological processes. Embryogenesis represents an extraordinary opportunity (and challenge) for the application of systems biology. Systems approaches have already been used successfully to study various aspects of development, from complex intracellular networks to four-dimensional models of organogenesis. Going forward, great advancements and discoveries can be expected from systems approaches applied to embryogenesis and developmental biology.
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7

Stadtländer, Christian T. K. H. "Systems biology: mathematical modeling and model analysis." Journal of Biological Dynamics 12, no. 1 (November 11, 2017): 11–15. http://dx.doi.org/10.1080/17513758.2017.1400121.

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8

Mushtaq, Mian Yahya, Robert Verpoorte, and Hye Kyong Kim. "Zebrafish as a model for systems biology." Biotechnology and Genetic Engineering Reviews 29, no. 2 (October 2013): 187–205. http://dx.doi.org/10.1080/02648725.2013.801238.

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Kwiatkowska, Marta, Gethin Norman, and David Parker. "Using probabilistic model checking in systems biology." ACM SIGMETRICS Performance Evaluation Review 35, no. 4 (March 2008): 14–21. http://dx.doi.org/10.1145/1364644.1364651.

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10

Van Norman, Jaimie M., and Philip N. Benfey. "Arabidopsisthalianaas a model organism in systems biology." Wiley Interdisciplinary Reviews: Systems Biology and Medicine 1, no. 3 (November 2009): 372–79. http://dx.doi.org/10.1002/wsbm.25.

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Дисертації з теми "Systems biology model"

1

Coskun, Sarp Arda. "PATHCASE-SB MODEL SIMULATION AND MODEL COMPOSITION TOOLS FOR SYSTEMS BIOLOGY MODELS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1328556115.

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Veliz-Cuba, Alan A. "The Algebra of Systems Biology." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28240.

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Анотація:
In order to understand biochemical networks we need to know not only how their parts work but also how they interact with each other. The goal of systems biology is to look at biological systems as a whole to understand how interactions of the parts can give rise to complex dynamics. In order to do this efficiently, new techniques have to be developed. This work shows how tools from mathematics are suitable to study problems in systems biology such as modeling, dynamics prediction, reverse engineering and many others. The advantage of using mathematical tools is that there is a large number of theory, algorithms and software available. This work focuses on how algebra can contribute to answer questions arising from systems biology.
Ph. D.
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3

Karlstädt, Anja [Verfasser]. "A systems biology approach to model cardiomyocyte metabolism / Anja Karlstädt." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2013. http://d-nb.info/1043197656/34.

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4

Konieczka, Jay, Kevin Drew, Alex Pine, Kevin Belasco, Sean Davey, Tatiana Yatskievych, Richard Bonneau, and Parker Antin. "BioNetBuilder2.0: bringing systems biology to chicken and other model organisms." BioMed Central, 2009. http://hdl.handle.net/10150/610006.

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Анотація:
BACKGROUND:Systems Biology research tools, such as Cytoscape, have greatly extended the reach of genomic research. By providing platforms to integrate data with molecular interaction networks, researchers can more rapidly begin interpretation of large data sets collected for a system of interest. BioNetBuilder is an open-source client-server Cytoscape plugin that automatically integrates molecular interactions from all major public interaction databases and serves them directly to the user's Cytoscape environment. Until recently however, chicken and other eukaryotic model systems had little interaction data available.RESULTS:Version 2.0 of BioNetBuilder includes a redesigned synonyms resolution engine that enables transfer and integration of interactions across species
this engine translates between alternate gene names as well as between orthologs in multiple species. Additionally, BioNetBuilder is now implemented to be part of the Gaggle, thereby allowing seamless communication of interaction data to any software implementing the widely used Gaggle software. Using BioNetBuilder, we constructed a chicken interactome possessing 72,000 interactions among 8,140 genes directly in the Cytoscape environment. In this paper, we present a tutorial on how to do so and analysis of a specific use case.CONCLUSION:BioNetBuilder 2.0 provides numerous user-friendly systems biology tools that were otherwise inaccessible to researchers in chicken genomics, as well as other model systems. We provide a detailed tutorial spanning all required steps in the analysis. BioNetBuilder 2.0, the tools for maintaining its data bases, standard operating procedures for creating local copies of its back-end data bases, as well as all of the Gaggle and Cytoscape codes required, are open-source and freely available at http://err.bio.nyu.edu/cytoscape/bionetbuilder/ webcite.
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5

Avva, Jayant. "Complex Systems Biology of Mammalian Cell Cycle Signaling in Cancer." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1295625781.

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6

Gay, Steven. "Subgraph Epimorphisms : Theory and Application to Model Reductions in Systems Biology." Sorbonne Paris Cité, 2015. http://www.theses.fr/2015USPCC265.

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Анотація:
Cette thèse développe une méthode de morphismes de graphes et l'applique à la réduction de modèles en biologie des systèmes. Nous nous intéressons au problème suivant: l'ensemble des modèles en biologie des systèmes est en expansion, mais aucune relation formelle entre les modèles de cet ensemble n'a été entreprise. Ainsi, la tâche d'organisation des modèles existants, qui est essentielle pour le raffinement et le couplage de modèles, doit être effectuée par le modélisateur. En biomathématiques, les techniques de réduction de modèle sont étudiées depuis longtemps, mais ces techniques sont bien trop restrictives pour être appliquées aux échelles requises en biologie des systèmes. Nous proposons un cadre de réduction de modèle, basé uniquement sur des graphes, qui permet d'organiser les modèles en un ordre partiel. Les modèles de biologie des systèmes seront représentés par leur graphe de réaction. Pour capturer le processus de réduction lui-même, nous étudierons un type particulier de morphismes de graphes : les épimorphismes de sous-graphe, qui permettent la fusion et l'effacement de sommets. Nous commencerons en analysant l'ordre partiel qui émerge des opérations de fusion et d'effacement, puis nous développerons des outils théoriques pour résoudre les problèmes calculatoires de notre méthode, et pour finir nous montrerons la faisabilité de l'approche et la précision du cadre "graphes de réactions/épimorphismes de sous-graphe", en utilisant un dépôt de modèles de biologie des systèmes
This thesis develops a framework of graph morphisms and applies it to model reduction in systems biology. We are interested in the following problem: the collection of systems biology models is growing, but there is no formai relation between models in this collection. Thus, the task of organizing the existing models, essential for model refinement and coupling, is left to the modeler. In mathematical biology, model reduction techniques have been studied for a long time, however these techniques are far too restrictive to be applied on the scales required by systems biology. We propose a model reduction framework based solely on graphs, allowing to organize models in a partial order. Systems biology models will be represented by their reaction graphs. To capture the process of reduction itself, we study a particular kind of graph morphisms: subgraph epimorphisms, which allow both vertex merging and deletion. We first analyze the partial order emerging from the merge/delete graph operations, then develop tools to solve computational problems raised by this framework, and finally show both the computational feasibility of the approach and the accuracy of the reaction graphs/subgraph epimorphisms framework on a large repository of systems biology models
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7

Prescott, Thomas Paul. "Large-scale layered systems and synthetic biology : model reduction and decomposition." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:205a18fb-b21f-4148-ba7d-3238f4b1f25b.

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Анотація:
This thesis is concerned with large-scale systems of Ordinary Differential Equations that model Biomolecular Reaction Networks (BRNs) in Systems and Synthetic Biology. It addresses the strategies of model reduction and decomposition used to overcome the challenges posed by the high dimension and stiffness typical of these models. A number of developments of these strategies are identified, and their implementation on various BRN models is demonstrated. The goal of model reduction is to construct a simplified ODE system to closely approximate a large-scale system. The error estimation problem seeks to quantify the approximation error; this is an example of the trajectory comparison problem. The first part of this thesis applies semi-definite programming (SDP) and dissipativity theory to this problem, producing a single a priori upper bound on the difference between two models in the presence of parameter uncertainty and for a range of initial conditions, for which exhaustive simulation is impractical. The second part of this thesis is concerned with the BRN decomposition problem of expressing a network as an interconnection of subnetworks. A novel framework, called layered decomposition, is introduced and compared with established modular techniques. Fundamental properties of layered decompositions are investigated, providing basic criteria for choosing an appropriate layered decomposition. Further aspects of the layering framework are considered: we illustrate the relationship between decomposition and scale separation by constructing singularly perturbed BRN models using layered decomposition; and we reveal the inter-layer signal propagation structure by decomposing the steady state response to parametric perturbations. Finally, we consider the large-scale SDP problem, where large scale SDP techniques fail to certify a system’s dissipativity. We describe the framework of Structured Storage Functions (SSF), defined where systems admit a cascaded decomposition, and demonstrate a significant resulting speed-up of large-scale dissipativity problems, with applications to the trajectory comparison technique discussed above.
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8

Feist, Adam Michael. "Model-driven metabolic engineering of Escherichia coli a systems biology approach /." Diss., [La Jolla] : University of California, San Diego, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3354731.

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Анотація:
Thesis (Ph. D.)--University of California, San Diego, 2008.
Title from first page of PDF file (viewed June 2, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
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9

Vesty, Eleanor Fay. "Understanding developmental processes in early-diverging plant model systems." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7498/.

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The study of evolutionary developmental biology relies on a detailed understanding of model systems. Whilst the flowering plants are the most successful and valuable plant group today, they don’t tell us much about the change and progression that was initiated by an ancestral aquatic photosynthetic unicell millions of years ago. The expansion of bryophyte and algal model systems was developed as part of this research The moss \(Physcomitrella\) \( patens\) is descended from the ancestral bryophytes that first colonised land. As such it is well-placed, as a model organism, to provide insight into terrestrialisation. The germination of spores or seeds is one of the key stages in the land plant life cycle. Comparison of the influences on spore and seed germination provides insight into the conservation of functions spanning 450 million years of evolution. The role of phytohormones in the control of spore germination was assessed by analysing the response of \(P. patens\) spores to different exogenously applied hormones. Endogenous roles were explored using hormone biosynthesis mutants and semi-quantitative analysis of signalling genes. This research shows that \(P. patens\) spore germination is regulated by some of the same hormones that regulate seed germination. The extent of regulation varies between hormone types but this has demonstrated previously unknown characteristics of the \(P. patens\) hormone signalling network. This work also highlights the importance of establishing tractable model systems with robust methodological procedures.
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10

Ghosh, Krishnendu. "Formal Analysis of Automated Model Abstractions under Uncertainty: Applications in Systems Biology." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1330024977.

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Книги з теми "Systems biology model"

1

Kremling, Andreas. Systems biology: Mathematical modeling and model analysis. Boca Raton: CRC Press, 2014.

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2

R, Goldsmith Marian, and Wilkins A. S. 1945-, eds. Molecular model systems in the Lepidoptera. Cambridge [England]: Cambridge University Press, 2006.

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3

Kloc, Malgorzata, and Jacek Z. Kubiak, eds. Marine Organisms as Model Systems in Biology and Medicine. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92486-1.

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4

NATO Advanced Study Institute on Physical Methods on Biological Membranes and Their Model Systems (1982 Altavilla Milicia, Italy). Physical methods on biological membranes and their model systems. New York: Plenum Press, 1985.

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5

Antonella, Macagnano, ed. Advanced topics in cell model systems. Hauppauge, NY: Nova Science Publishers, 2009.

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6

service), ScienceDirect (Online, ed. Electron microscopy of model systems. Amsterdam: Academic Press/Elsevier, 2010.

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7

Sylvia, Nagl, ed. The role of model integration in complex systems modelling: An example from cancer biology. Berlin: Springer, 2010.

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8

Comeau, P. G. LITE: A model for estimating light under broadleaf and conifer tree canopies. Victoria, BC: British Columbia Ministry of Forests Research Program, 1998.

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9

Systems biology. New York: Humana Press, 2009.

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10

Isidore, Rigoutsos, and Stephanopoulos G, eds. Systems biology. New York: Oxford University Press, 2006.

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Частини книг з теми "Systems biology model"

1

Fisher, Jasmin, and Nir Piterman. "Model Checking in Biology." In A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations, 255–79. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9041-3_10.

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Alfieri, Roberta, and Luciano Milanesi. "Mathematical Model, Model Theory." In Encyclopedia of Systems Biology, 1181. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1071.

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Marin-Sanguino, Alberto. "Model Repositories." In Encyclopedia of Systems Biology, 1403–4. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1058.

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Anderson, James, and Antonis Papachristodoulou. "Model Invalidation." In Encyclopedia of Systems Biology, 1395–98. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1221.

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Voit, Eberhard O. "Metabolic Model." In Encyclopedia of Systems Biology, 1248–49. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1251.

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Voit, Eberhard O. "Symbolic Model." In Encyclopedia of Systems Biology, 2034. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1252.

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Voit, Eberhard O. "Canonical Model." In Encyclopedia of Systems Biology, 198. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1336.

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Rodriguez-Fernandez, Maria, and Francis J. Doyle. "Nonlinear Model." In Encyclopedia of Systems Biology, 1545. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1421.

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Rodriguez-Fernandez, Maria, and Francis J. Doyle. "Model Validation." In Encyclopedia of Systems Biology, 1406. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1423.

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Wang, Yong. "Logical Model." In Encyclopedia of Systems Biology, 1142. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_366.

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Тези доповідей конференцій з теми "Systems biology model"

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Liu, Bing, and Sara Safa. "A Model Checking-based Analysis Framework for Systems Biology Models." In 2020 57th ACM/IEEE Design Automation Conference (DAC). IEEE, 2020. http://dx.doi.org/10.1109/dac18072.2020.9218655.

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Falugi, Paola, and Laura Giarre. "Application of model quality evaluation to systems biology." In 2008 3rd International Symposium on Communications, Control, and Signal Processing (ISCCSP 2008). IEEE, 2008. http://dx.doi.org/10.1109/isccsp.2008.4537206.

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Yang, Yang, and Hai Lin. "Reachability analysis based model validation in systems biology." In 2010 IEEE Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 2010. http://dx.doi.org/10.1109/iccis.2010.5518589.

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von Zychlinski, A., S. Baginsky, and W. Gruissem. "Rice: an emerging model for plant systems biology." In Proceedings of the Fifth International Rice Genetics Symposium. World Scientific Publishing Company, 2007. http://dx.doi.org/10.1142/9789812708816_0023.

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Mordecai, Yaniv, Judith Somekh, and Dov Dori. "Presence-awareness: A conceptual model-based systems biology approach." In 2014 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2014. http://dx.doi.org/10.1109/smc.2014.6974043.

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Altenburg, Karl, and Karthik Namasivayam. "A multiagent multicellular systems biology model of Trichoplax adhaerens." In 2008 IEEE International Conference on Electro/Information Technology (EIT 2008). IEEE, 2008. http://dx.doi.org/10.1109/eit.2008.4554346.

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Rosati, Elise, Morgan Madec, Jean-Baptiste Kammerer, Abir Rezgui, Christophe Lallement, and Jacques Haiech. "Verilog-A compact space-dependent model for biology." In 2015 MIXDES - 22nd International Conference "Mixed Design of Integrated Circuits & Systems". IEEE, 2015. http://dx.doi.org/10.1109/mixdes.2015.7208505.

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Chueh Loo Poh, Chueh Loo Poh. "Model driven reprogramming of biological systems." In IET/SynbiCITE Engineering Biology Conference. Institution of Engineering and Technology, 2016. http://dx.doi.org/10.1049/cp.2016.1214.

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"Characteristic of the spinal muscular atrophy cell model." In SYSTEMS BIOLOGY AND BIOINFORMATICS. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/sbb-2019-32.

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Hussain, Faraz, Christopher J. Langmead, Qi Mi, Joyeeta Dutta-Moscato, Yoram Vodovotz, and Sumit K. Jha. "Parameter discovery for stochastic computational models in systems biology using Bayesian model checking." In 2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2014. http://dx.doi.org/10.1109/iccabs.2014.6863925.

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Звіти організацій з теми "Systems biology model"

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Nielsen, Lars. A Unique Model Platform for C4 Plant Systems and Synthetic Biology. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ada627801.

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Cucinotta, Francis A. Systems Biology Model of Interactions between Tissue Growth Factors and DNA Damage Pathways: Low Dose Response and Cross-Talk in TGFβ and ATM Signaling. Office of Scientific and Technical Information (OSTI), вересень 2016. http://dx.doi.org/10.2172/1335567.

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O'Neill, Peter, and Jennifer Anderson. Systems Biology Model of Interactions Between Tissue Growth Factors and DNA Damage Pathways: Low Dose Response and Cross-Talk in TGFbeta and ATM Signaling. Office of Scientific and Technical Information (OSTI), October 2014. http://dx.doi.org/10.2172/1158919.

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Fleming, Ronan. Final report for U.S. Department of Energy Award DE-SC0010429 to the University of Luxembourg on Multi-scale Molecular Systems Biology: Reconstruction and Model Optimization. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1572377.

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Nadathur, Govind S. Debaryomyces hansenii: A Model System for Marine Molecular Biology. Fort Belvoir, VA: Defense Technical Information Center, May 1991. http://dx.doi.org/10.21236/ada236966.

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Hahn, Jin-Oh. Solving Inverse Problems for Mechanistic Systems Biology Models with Unknown Inputs. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada622227.

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Hawkins, Brian T., and Sonia Grego. A Better, Faster Road From Biological Data to Human Health: A Systems Biology Approach for Engineered Cell Cultures. RTI Press, June 2017. http://dx.doi.org/10.3768/rtipress.2017.rb.0015.1706.

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Анотація:
Traditionally, the interactions of drugs and toxicants with human tissue have been investigated in a reductionist way—for example, by focusing on specific molecular targets and using single-cell-type cultures before testing compounds in whole organisms. More recently, “systems biology” approaches attempt to enhance the predictive value of in vitro biological data by adopting a comprehensive description of biological systems and using computational tools that are sophisticated enough to handle the complexity of these systems. However, the utility of computational models resulting from these efforts completely relies on the quality of the data used to construct them. Here, we propose that recent advances in the development of bioengineered, three-dimensional, multicellular constructs provide in vitro data of sufficient complexity and physiological relevance to be used in predictive systems biology models of human responses. Such predictive models are essential to maximally leveraging these emerging bioengineering technologies to improve both therapeutic development and toxicity risk assessment. This brief outlines the opportunities presented by emerging technologies and approaches for the acceleration of drug development and toxicity testing, as well as the challenges lying ahead for the field.
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Elias, Dwayne, Christopher Schadt, Lance Miller, Tommy Phelps, S. D. Brown, Adam Arkin, Terry Hazen, Megin Drake, Z. K. Yang, and Mircea Podar. Development of a Model, Metal-reducing Microbial Community for a System Biology Level Assessment of Desulfovibrio vulgaris as part of a Community. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/985918.

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Oron, Gideon, Raphi Mandelbaum, Carlos E. Enriquez, Robert Armon, Yoseph Manor, L. Gillerman, A. Alum, and Charles P. Gerba. Optimization of Secondary Wastewater Reuse to Minimize Environmental Risks. United States Department of Agriculture, December 1999. http://dx.doi.org/10.32747/1999.7573077.bard.

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The main purpose of the research was to examine approaches and to evaluate methods for minimizing the risks during applying treated domestic wastewater for agricultural irrigation. This general purpose consisted of examining under field conditions the possibilities when implementing different application technologies for minimizing health and environmental risks. It was assumed that Subsurface Drip Irrigation (SDI) will provide adequate conditions for safe effluent reuse. Controlled field experiments where conducted in commercial fields to evaluate the alternatives. Main efforts where conducted in Israel in the grape vineyard in Arad heights, in the field crops in Kibbutz Chafets Chaim and in Arizona in fields adjacent to the University campus. The complementary part was to examine the behavior of the various pathogens in the effluent-soil-plant system. The analysis is based on controlled experiments, primarily in greenhouse along with field experiments. Molecular biology methods were used to identify the behavior of the pathogens in the components of the system. The project included as well examining the effluent quality in various sites, primarily those in which treated wastewater is reused for agricultural irrigation. The monitoring included conventional parameters however, also parasites such as Giardia and Cryptosporidium. The results obtained indicate the prominent advantages of using Subsurface Drip Irrigation (SDI) method for minimizing health and environmental risks during application of secondary effluent. A theoretical model for assessing the risks while applying treated wastewater was completed as well. The management model shows the risks during various scenarios of wastewater quality, application technology and related human exposure.
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Droby, Samir, Michael Wisniewski, Martin Goldway, Wojciech Janisiewicz, and Charles Wilson. Enhancement of Postharvest Biocontrol Activity of the Yeast Candida oleophila by Overexpression of Lytic Enzymes. United States Department of Agriculture, November 2003. http://dx.doi.org/10.32747/2003.7586481.bard.

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Enhancing the activity of biocontrol agents could be the most important factor in their success in controlling fruit disease and their ultimate acceptance in commercial disease management. Direct manipulation of a biocontrol agent resulting in enhancement of diseases control could be achieved by using recent advances in molecular biology techniques. The objectives of this project were to isolate genes from yeast species that were used as postharvest biocontrol agents against postharvest diseases and to determine their role in biocontrol efficacy. The emphasis was to be placed on the yeast, Candida oleophila, which was jointly discovered and developed in our laboratories, and commercialized as the product, Aspire. The general plan was to develop a transformation system for C . oleophila and either knockout or overexpress particular genes of interest. Additionally, biochemical characterization of the lytic peptides was conducted in the wild-type and transgenic isolates. In addition to developing a better understanding of the mode of action of the yeast biocontrol agents, it was also our intent to demonstrate the feasibility of enhancing biocontrol activity via genetic enhancement of yeast with genes known to code for proteins with antimicrobial activity. Major achievements are: 1) Characterization of extracellular lytic enzymes produced by the yeast biocontrol agent Candida oleophila; 2) Development of a transformation system for Candida oleophila; 3) Cloning and analysis of C.oleophila glucanase gene; 4) Overexpression of and knockout of C. oleophila glucanase gene and evaluating its role in the biocontrol activity of C. oleophila; 5) Characterization of defensin gene and its expression in the yeast Pichiapastoris; 6) Cloning and Analysis of Chitinase and Adhesin Genes; 7) Characterization of the rnase secreted by C . oleophila and its inhibitory activity against P. digitatum. This project has resulted in information that enhanced our understanding of the mode of action of the yeast C . oleophila. This was important step towards enhancing the biocontrol activity of the yeast. Fungal cell wall enzymes produced by the yeast antagonist were characterized. Different substrates were identified to enhance there production in vitro. Exo-b-1, 3 glucanase, chitinase and protease production was stimulated by the presence of cell-wall fragments of Penicillium digitatum in the growing medium, in addition to glucose. A transformation system developed was used to study the role of lytic enzymes in the biocontrol activity of the yeast antagonist and was essential for genetic manipulation of C . oleqphila. After cloning and characterization of the exo-glucanase gene from the yeast, the transformation system was efficiently used to study the role of the enzyme in the biocontrol activity by over-expressing or knocking out the activity of the enzyme. At the last phase of the research (still ongoing) the transformation system is being used to study the role of chitinase gene in the mode of action. Knockout and over expression experiments are underway.
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