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Journal articles on the topic 'Systems biology model'

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Published: 10 December 2022
Last updated: 29 January 2023

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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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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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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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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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9

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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11

Kirk, Paul, Thomas Thorne, and Michael PH Stumpf. "Model selection in systems and synthetic biology." Current Opinion in Biotechnology 24, no. 4 (August 2013): 767–74. http://dx.doi.org/10.1016/j.copbio.2013.03.012.

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12

Sauro, Herbert M., and Frank T. Bergmann. "Standards and ontologies in computational systems biology." Essays in Biochemistry 45 (September 30, 2008): 211–22. http://dx.doi.org/10.1042/bse0450211.

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With the growing importance of computational models in systems biology there has been much interest in recent years to develop standard model interchange languages that permit biologists to easily exchange models between different software tools. In the present chapter two chief model exchange standards, SBML (Systems Biology Markup Language) and CellML are described. In addition, other related features including visual layout initiatives, ontologies and best practices for model annotation are discussed. Software tools such as developer libraries and basic editing tools are also introduced, together with a discussion on the future of modelling languages and visualization tools in systems biology.
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13

Williamson, M. P. "Systems biology: will it work?" Biochemical Society Transactions 33, no. 3 (June 1, 2005): 503–6. http://dx.doi.org/10.1042/bst0330503.

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Systems biology describes the collection of a set of measurements on a system, integrated with a mathematical model of that system. The model and the measurements must be made together and refined iteratively, requiring close collaboration between biologists and modellers. A complete cell is probably too large and complicated to model yet, but simplified subsystems will probably produce valuable results. I consider various ways of simplifying the system and conclude that the biggest challenge is to get everyone working together productively.
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14

Furber, John D. "SYSTEMS BIOLOGY OF HUMAN AGING - NETWORK MODEL 2019." Innovation in Aging 3, Supplement_1 (November 2019): S973. http://dx.doi.org/10.1093/geroni/igz038.3527.

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Abstract This network schema is presented to aid in conceptualizing the many processes of aging, the causal chains of events, and the interactions among them, including feedback and vicious cycles. Contemplation of this network suggests promising intervention points for therapy development. This diagram is maintained on the Web as a reference for researchers and students. Content is updated as new information comes to light. www.LegendaryPharma.com/chartbg.html At first glance the network looks like a complicated web. However, as a conceptual summary, in one view, we can see how the many biogerontological processes relate to each other. Importantly, examination of these relationships allows us to pick out reasonably plausible causal chains of events. Within these chains, we can see age-related changes or accumulations that appear to be promising targets for future therapy development. Especially harmful is damage to the body's regeneration and repair systems, because they normally repair damage to other structures and systems. The many observable signs of human senescence have been hypothesized by various researchers to result from several primary causes. Inspection of the biochemical and physiological pathways associated with age-related changes and with the hypothesized causes reveals several parallel cascades of events that involve several important interactions and feedback loops. This network model includes both intracellular and extracellular processes. It ranges in scale from the molecular to the whole-body level. Effects due to externalities, lifestyle, environment, and proposed interventions are highlighted around the margins of the network.
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15

Keseler, Ingrid M., Amanda Mackie, Martin Peralta-Gil, Alberto Santos-Zavaleta, Socorro Gama-Castro, César Bonavides-Martínez, Carol Fulcher, et al. "EcoCyc: fusing model organism databases with systems biology." Nucleic Acids Research 41, no. D1 (November 7, 2012): D605—D612. http://dx.doi.org/10.1093/nar/gks1027.

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16

Matthiessen, Dana. "Causal Concepts Guiding Model Specification in Systems Biology." Disputatio 9, no. 47 (December 1, 2017): 499–527. http://dx.doi.org/10.1515/disp-2017-0016.

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Abstract In this paper I analyze the process by which modelers in systems biology arrive at an adequate representation of the biological structures thought to underlie data gathered from high-throughput experiments. Contrary to views that causal claims and explanations are rare in systems biology, I argue that in many studies of gene regulatory networks modelers aim at a representation of causal structure. In addressing modeling challenges, they draw on assumptions informed by theory and pragmatic considerations in a manner that is guided by an interventionist conception of causal structure. While doubts have been raised about the applicability of this notion of causality to complex biological systems, it is here seen to be an adequate guide to inquiry.
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17

Kornfield, Irv, and Peter F. Smith. "African Cichlid Fishes: Model Systems for Evolutionary Biology." Annual Review of Ecology and Systematics 31, no. 1 (November 2000): 163–96. http://dx.doi.org/10.1146/annurev.ecolsys.31.1.163.

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18

Jeong, Jenny E., Qinwei Zhuang, Mark K. Transtrum, Enlu Zhou, and Peng Qiu. "Experimental design and model reduction in systems biology." Quantitative Biology 6, no. 4 (October 27, 2018): 287–306. http://dx.doi.org/10.1007/s40484-018-0150-9.

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19

E. Louridas, George, and Katerina G. Lourida. "Systems Biology and Biomechanical Model of Heart Failure." Current Cardiology Reviews 8, no. 3 (September 18, 2012): 220–30. http://dx.doi.org/10.2174/157340312803217238.

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20

Nagasaki, M., A. Saito, A. Fujita, G. Tremmel, K. Ueno, E. Ikeda, E. Jeong, and S. Miyano. "Systems biology model repository for macrophage pathway simulation." Bioinformatics 27, no. 11 (April 19, 2011): 1591–93. http://dx.doi.org/10.1093/bioinformatics/btr173.

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21

Nielsen, Jens. "Yeast Systems Biology: Model Organism and Cell Factory." Biotechnology Journal 14, no. 9 (May 20, 2019): 1800421. http://dx.doi.org/10.1002/biot.201800421.

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22

Diamond, Scott L. "Systems Biology to Predict Platelet Function." Blood 116, no. 21 (November 19, 2010): SCI—38—SCI—38. http://dx.doi.org/10.1182/blood.v116.21.sci-38.sci-38.

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Abstract Abstract SCI-38 Systems Biology seeks to provide patient-specific prediction of dynamic cellular response to multiple stimuli, critical information toward predicting risk, disease progression, or response to therapy. We deployed two distinct approaches, bottom-up and top-down analyses, to gain insight into platelet signaling. The bottom-up approach required a definition of reaction network and kinetic equations (topology), kinetic parameters, and initial concentrations in order to simulate platelet signaling. We developed a computational platelet model – assembled from 24 peer-reviewed platelet studies to yield 132 measured kinetic rate constants – that accurately predicts resting levels of cytosolic calcium, IP3, diacylglycerol, phosphatidic acid, phosphoinositol, PIP, and PIP2. The model accurately predicts the full transient calcium dynamics in response to increasing levels of ADP. In the first full stochastic simulation of single platelet response to ADP, the model provides an extremely accurate prediction of the statistics of the asynchronous [Ca]i spikes observed in single platelets. Specifically, this is the first work to provide a quantitative molecular explanation of the asynchronous calcium spiking observed in ADP-activated human platelets. We show the asynchronous spiking is a result of the fundamentally stochastic nature of signal transduction in cells as small as human platelets. Specific testable predictions have emerged about the requirement of high SERCA/IP3R ratios in functional platelets, limits on the concentration of calcium in the DTS, and relative potencies of PAR peptides and ADP. For functional phenotyping platelets, a top-down approach linking multiple inputs to functional outputs was used to understand how human platelets integrate diverse signals encountered during thrombosis. We developed a high-throughput platform that measures the human platelet calcium mobilization in response to all pairwise combinations of six major agonists. Agonists tested in this study were: convulxin (CVX; GPVI activator), ADP, the thromboxane analog U46619, PAR1 agonist peptide (SFLLRN), PAR4 agonist peptide (AYPGKF), and PGE2 (activator of IP and EP receptor). The calcium responses to single agonists at 0.1, 1, 10′ EC50 and 135 pairwise combinations trained a neural network (NN) model to predict the entire 6-dimensional platelet response space. The NN model successfully predicted responses to sequential additions and 27 ternary combinations of [ADP], [convulxin], and [SFLLRN] (R=0.881). With 4077 NN simulations spanning the 6-dimensional agonist space, 45 combinations of 4–6 agonists (ranging from synergism to antagonism) were selected and confirmed experimentally (R=0.883), revealing a highly synergistic condition of high U46619/PGE2 ratio, consistent with the risk of COX-2 therapy. Furthermore, pairwise agonist scanning (PAS) provided a direct measurement of 135 synergy values, thus allowing a unique phenotypic scoring of 10 human donors. Patient-specific training of NNs represent a compact and robust approach for prediction of cellular integration of multiple signals in a complex disease milieu. Either bottom-up models or top-down NN models are ideal for incorporation into systems biology simulations of thrombotic pathways under flow conditions. Disclosures: No relevant conflicts of interest to declare.
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23

Pan, Michael, Peter J. Gawthrop, Joseph Cursons, and Edmund J. Crampin. "Modular assembly of dynamic models in systems biology." PLOS Computational Biology 17, no. 10 (October 13, 2021): e1009513. http://dx.doi.org/10.1371/journal.pcbi.1009513.

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It is widely acknowledged that the construction of large-scale dynamic models in systems biology requires complex modelling problems to be broken up into more manageable pieces. To this end, both modelling and software frameworks are required to enable modular modelling. While there has been consistent progress in the development of software tools to enhance model reusability, there has been a relative lack of consideration for how underlying biophysical principles can be applied to this space. Bond graphs combine the aspects of both modularity and physics-based modelling. In this paper, we argue that bond graphs are compatible with recent developments in modularity and abstraction in systems biology, and are thus a desirable framework for constructing large-scale models. We use two examples to illustrate the utility of bond graphs in this context: a model of a mitogen-activated protein kinase (MAPK) cascade to illustrate the reusability of modules and a model of glycolysis to illustrate the ability to modify the model granularity.
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24

Saad, Christian, Bernhard Bauer, Ulrich R. Mansmann, and Jian Li. "AutoAnalyze in Systems Biology." Bioinformatics and Biology Insights 13 (January 2019): 117793221881845. http://dx.doi.org/10.1177/1177932218818458.

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AutoAnalyze is a highly customizable framework for the visualization and analysis of large-scale model graphs. Originally developed for use in the automotive domain, it also supports efficient computation within molecular networks represented by reaction equations. A static analysis approach is used for efficient treatment-condition-specific simulation. The chosen method relies on the computation of a global network data-flow resulting from the evaluation of individual genetic data. The approach facilitates complex analyses of biological components from a molecular network under specific therapeutic perturbations, as demonstrated in a case study. In addition to simulating the complex networks in a stable and reproducible way, kinetic constants can also be fine-tuned using a genetic algorithm and built-in statistical tools.
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25

van Meer, Peter, and Jacob Raber. "Mouse behavioural analysis in systems biology." Biochemical Journal 389, no. 3 (July 26, 2005): 593–610. http://dx.doi.org/10.1042/bj20042023.

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Molecular techniques allowing in vivo modulation of gene expression have provided unique opportunities and challenges for behavioural studies aimed at understanding the function of particular genes or biological systems under physiological or pathological conditions. Although various animal models are available, the laboratory mouse (Mus musculus) has unique features and is therefore a preferred animal model. The mouse shares a remarkable genetic resemblance and aspects of behaviour with humans. In this review, first we describe common mouse models for behavioural analyses. As both genetic and environmental factors influence behavioural performance and need to be carefully evaluated in behavioural experiments, considerations for designing and interpretations of these experiments are subsequently discussed. Finally, common behavioural tests used to assess brain function are reviewed, and it is illustrated how behavioural tests are used to increase our understanding of the role of histaminergic neurotransmission in brain function.
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26

Toni, Tina, and Michael P. H. Stumpf. "Simulation-based model selection for dynamical systems in systems and population biology." Bioinformatics 26, no. 1 (October 29, 2009): 104–10. http://dx.doi.org/10.1093/bioinformatics/btp619.

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27

Lederman, Lynne. "Model Systems." BioTechniques 44, no. 4 (April 2008): 1–5. http://dx.doi.org/10.2144/000112784.

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28

Kool, Eric T., and Marcey L. Waters. "The model student: what chemical model systems can teach us about biology." Nature Chemical Biology 3, no. 2 (February 2007): 70–73. http://dx.doi.org/10.1038/nchembio0207-70.

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29

Vashist, Surender, Dalan Bailey, Akos Putics, and Ian Goodfellow. "Model systems for the study of human norovirus biology." Future Virology 4, no. 4 (July 2009): 353–67. http://dx.doi.org/10.2217/fvl.09.18.

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30

Balsa-Canto, Eva, and Julio R. Banga. "COMPUTATIONAL PROCEDURES FOR OPTIMAL MODEL IDENTIFICATION IN SYSTEMS BIOLOGY." IFAC Proceedings Volumes 42, no. 10 (2009): 1247–52. http://dx.doi.org/10.3182/20090706-3-fr-2004.00207.

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31

Mandoli, Dina F., and Richard Olmstead. "The Importance of Emerging Model Systems in Plant Biology." Journal of Plant Growth Regulation 19, no. 3 (September 1, 2000): 249–52. http://dx.doi.org/10.1007/s003440000038.

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32

Serrano, Luis. "A quantitative systems biology study on a model bacterium." New Biotechnology 29 (September 2012): S30. http://dx.doi.org/10.1016/j.nbt.2012.08.076.

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33

Kim, Jihoon, Bon-Kyoung Koo, and Juergen A. Knoblich. "Human organoids: model systems for human biology and medicine." Nature Reviews Molecular Cell Biology 21, no. 10 (July 7, 2020): 571–84. http://dx.doi.org/10.1038/s41580-020-0259-3.

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34

Silk, Daniel, Paul D. W. Kirk, Chris P. Barnes, Tina Toni, and Michael P. H. Stumpf. "Model Selection in Systems Biology Depends on Experimental Design." PLoS Computational Biology 10, no. 6 (June 12, 2014): e1003650. http://dx.doi.org/10.1371/journal.pcbi.1003650.

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35

Xirasagar, S., S. Gustafson, B. A. Merrick, K. B. Tomer, S. Stasiewicz, D. D. Chan, K. J. Yost, et al. "CEBS object model for systems biology data, SysBio-OM." Bioinformatics 20, no. 13 (March 25, 2004): 2004–15. http://dx.doi.org/10.1093/bioinformatics/bth189.

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36

De Meester, Luc, Steven Declerck, Robby Stoks, Gerald Louette, Frank Van De Meutter, Tom De Bie, Erik Michels, and Luc Brendonck. "Ponds and pools as model systems in conservation biology, ecology and evolutionary biology." Aquatic Conservation: Marine and Freshwater Ecosystems 15, no. 6 (2005): 715–25. http://dx.doi.org/10.1002/aqc.748.

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37

Kiefer, Julie C. "Emerging developmental model systems." Developmental Dynamics 235, no. 10 (July 31, 2006): 2895–99. http://dx.doi.org/10.1002/dvdy.20900.

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38

Finney, A., and M. Hucka. "Systems biology markup language: Level 2 and beyond." Biochemical Society Transactions 31, no. 6 (December 1, 2003): 1472–73. http://dx.doi.org/10.1042/bst0311472.

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The SBML (systems biology markup language) is a standard exchange format for computational models of biochemical networks. We continue developing SBML collaboratively with the modelling community to meet their evolving needs. The recently introduced SBML Level 2 includes several enhancements to the original Level 1, and features under development for SBML Level 3 include model composition, multistate chemical species and diagrams.
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39

Foty, Ramsey A., and Malcolm S. Steinberg. "Differential adhesion in model systems." Wiley Interdisciplinary Reviews: Developmental Biology 2, no. 5 (January 30, 2013): 631–45. http://dx.doi.org/10.1002/wdev.104.

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40

Fox, Donald T., Erez Cohen, and Rachel Smith-Bolton. "Model systems for regeneration: Drosophila." Development 147, no. 7 (April 1, 2020): dev173781. http://dx.doi.org/10.1242/dev.173781.

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41

Vogg, Matthias C., Brigitte Galliot, and Charisios D. Tsiairis. "Model systems for regeneration: Hydra." Development 146, no. 21 (November 1, 2019): dev177212. http://dx.doi.org/10.1242/dev.177212.

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42

Phipps, Lauren S., Lindsey Marshall, Karel Dorey, and Enrique Amaya. "Model systems for regeneration: Xenopus." Development 147, no. 6 (March 15, 2020): dev180844. http://dx.doi.org/10.1242/dev.180844.

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43

Ivankovic, Mario, Radmila Haneckova, Albert Thommen, Markus A. Grohme, Miquel Vila-Farré, Steffen Werner, and Jochen C. Rink. "Model systems for regeneration: planarians." Development 146, no. 17 (September 1, 2019): dev167684. http://dx.doi.org/10.1242/dev.167684.

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44

Marques, Ines J., Eleonora Lupi, and Nadia Mercader. "Model systems for regeneration: zebrafish." Development 146, no. 18 (September 15, 2019): dev167692. http://dx.doi.org/10.1242/dev.167692.

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45

Joven, Alberto, Ahmed Elewa, and András Simon. "Model systems for regeneration: salamanders." Development 146, no. 14 (July 15, 2019): dev167700. http://dx.doi.org/10.1242/dev.167700.

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46

Bradshaw, H. D., Reinhart Ceulemans, John Davis, and Reinhard Stettler. "Emerging Model Systems in Plant Biology: Poplar (Populus) as A Model Forest Tree." Journal of Plant Growth Regulation 19, no. 3 (September 1, 2000): 306–13. http://dx.doi.org/10.1007/s003440000030.

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47

Denoncourt, Alix, and Michael Downey. "Model systems for studying polyphosphate biology: a focus on microorganisms." Current Genetics 67, no. 3 (January 9, 2021): 331–46. http://dx.doi.org/10.1007/s00294-020-01148-x.

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48

Konieczka, Jay H., Kevin Drew, Alex Pine, Kevin Belasco, Sean Davey, Tatiana A. Yatskievych, Richard Bonneau, and Parker B. Antin. "BioNetBuilder2.0: bringing systems biology to chicken and other model organisms." BMC Genomics 10, Suppl 2 (2009): S6. http://dx.doi.org/10.1186/1471-2164-10-s2-s6.

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49

Yu, R. C., A. Colman-Lerner, L. Lok, M. Holl, D. Endy, A. E. Tsong, S. S. Andrews, et al. "The Alpha Project: a model system for systems biology research." IET Systems Biology 2, no. 5 (September 1, 2008): 222–33. http://dx.doi.org/10.1049/iet-syb:20080127.

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50

Bult, C. J., J. T. Eppig, J. A. Kadin, J. E. Richardson, and J. A. Blake. "The Mouse Genome Database (MGD): mouse biology and model systems." Nucleic Acids Research 36, Database (December 23, 2007): D724—D728. http://dx.doi.org/10.1093/nar/gkm961.

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