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Journal articles on the topic 'Biological systems modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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1

Ågren, Göran I. "Modeling biological systems." Forest Ecology and Management 96, no. 1-2 (1997): 185–86. http://dx.doi.org/10.1016/s0378-1127(97)00103-5.

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2

Darbari, Manuj, and Vipin Saxena. "Modeling biological systems." ACM SIGSOFT Software Engineering Notes 30, no. 5 (2005): 1–4. http://dx.doi.org/10.1145/1095430.1095441.

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3

Liu, Zhi-Ping, and Luonan Chen. "Multiscale modeling biological systems." IET Systems Biology 10, no. 1 (2016): 1. http://dx.doi.org/10.1049/iet-syb.2016.0002.

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4

Bagley, R. J., J. D. Farmer, S. A. Kauffman, N. H. Packard, A. S. Perelson, and I. M. Stadnyk. "Modeling adaptive biological systems." Biosystems 23, no. 2-3 (1989): 113–37. http://dx.doi.org/10.1016/0303-2647(89)90016-6.

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5

Wasik, Szymon. "Modeling Biological Systems Using Crowdsourcing." Foundations of Computing and Decision Sciences 43, no. 3 (2018): 219–43. http://dx.doi.org/10.1515/fcds-2018-0012.

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Abstract Crowdsourcing is a very effective technique for outsourcing work to a vast network usually comprising anonymous people. In this study, we review the application of crowdsourcing to modeling systems originating from systems biology. We consider a variety of verified approaches, including well-known projects such as EyeWire, FoldIt, and DREAM Challenges, as well as novel projects conducted at the European Center for Bioinformatics and Genomics. The latter projects utilized crowdsourced serious games to design models of dynamic biological systems, and it was demonstrated that these models could be used successfully to involve players without domain knowledge. We conclude the review of these systems by providing 10 guidelines to facilitate the efficient use of crowdsourcing.

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6

Kiselev, Ilya, and Fedor Kolpakov. "Modular Modeling of Biological Systems." Virtual Biology 1, no. 1 (2013): 30. http://dx.doi.org/10.12704/vb/e11.

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7

Motta, S., and F. Pappalardo. "Mathematical modeling of biological systems." Briefings in Bioinformatics 14, no. 4 (2012): 411–22. http://dx.doi.org/10.1093/bib/bbs061.

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8

Sulpizi, Marialore, Roland Faller, and Sergio Pantano. "Multiscale modeling on biological systems." Biochemical and Biophysical Research Communications 498, no. 2 (2018): 263. http://dx.doi.org/10.1016/j.bbrc.2018.02.179.

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9

Raugei, Simone, Francesco Luigi Gervasio, and Paolo Carloni. "DFT modeling of biological systems." physica status solidi (b) 243, no. 11 (2006): 2500–2515. http://dx.doi.org/10.1002/pssb.200642096.

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10

Lubbock, Alexander L. R., and Carlos F. Lopez. "Programmatic modeling for biological systems." Current Opinion in Systems Biology 27 (September 2021): 100343. http://dx.doi.org/10.1016/j.coisb.2021.05.004.

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11

Coughenour, Michael B., and James W. Haefner. "Modeling Biological Systems: Principles and Applications." Ecology 78, no. 5 (1997): 1609. http://dx.doi.org/10.2307/2266156.

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12

Hatcher, Melanie J., and James W. Haefner. "Modeling Biological Systems: Principles and Applications." Journal of Animal Ecology 66, no. 2 (1997): 294. http://dx.doi.org/10.2307/6032.

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13

Voit, E., Z. Qi, and G. Miller. "Steps of Modeling Complex Biological Systems." Pharmacopsychiatry 41, S 01 (2008): S78—S84. http://dx.doi.org/10.1055/s-2008-1080911.

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14

lyengar, S. Sitharama. "Computer Modeling of Complex Biological Systems." Journal of Clinical Engineering 13, no. 2 (1988): 79–97. http://dx.doi.org/10.1097/00004669-198803000-00004.

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15

Proß, Sabrina, and Bernhard Bachmann. "An Advanced Environment for Hybrid Modeling of Biological Systems Based on Modelica." Journal of Integrative Bioinformatics 8, no. 1 (2011): 1–34. http://dx.doi.org/10.1515/jib-2011-152.

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Summary Biological systems are often very complex so that an appropriate formalism is needed for modeling their behavior. Hybrid Petri Nets, consisting of time-discrete Petri Net elements as well as continuous ones, have proven to be ideal for this task. Therefore, a new Petri Net library was implemented based on the object-oriented modeling language Modelica which allows the modeling of discrete, stochastic and continuous Petri Net elements by differential, algebraic and discrete equations. An appropriate Modelica-tool performs the hybrid simulation with discrete events and the solution of continuous differential equations. A special sub-library contains so-called wrappers for specific reactions to simplify the modeling process.The Modelica-models can be connected to Simulink-models for parameter optimization, sensitivity analysis and stochastic simulation in Matlab.The present paper illustrates the implementation of the Petri Net component models, their usage within the modeling process and the coupling between the Modelica-tool Dymola and Matlab/Simulink. The application is demonstrated by modeling the metabolism of Chinese Hamster Ovary Cells.

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16

Capatana, Cristina. "Mathematical Modeling of Biological Systems Volume 1." Acta Endocrinologica (Bucharest) 4, no. 2 (2008): 235. http://dx.doi.org/10.4183/aeb.2008.235.

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17

Capatana, Cristina. "Mathematical Modeling of Biological Systems Volume 2." Acta Endocrinologica (Bucharest) 4, no. 2 (2008): 236. http://dx.doi.org/10.4183/aeb.2008.236.

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18

De Domenico, Manlio. "Multilayer network modeling of integrated biological systems." Physics of Life Reviews 24 (March 2018): 149–52. http://dx.doi.org/10.1016/j.plrev.2017.12.006.

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19

Wu, Jialiang, and Eberhard Voit. "Integrative biological systems modeling: challenges and opportunities." Frontiers of Computer Science in China 3, no. 1 (2009): 92–100. http://dx.doi.org/10.1007/s11704-007-0011-9.

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20

Rihan, Fathalla A. "Numerical Modeling of Fractional-Order Biological Systems." Abstract and Applied Analysis 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/816803.

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21

Stelling, Jörg, and Mustafa Khammash. "Modeling heterogeneity in biological systems across scales." Current Opinion in Systems Biology 16 (August 2019): iv—v. http://dx.doi.org/10.1016/j.coisb.2019.10.016.

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22

Ji, Zhiwei, Ke Yan, Wenyang Li, Haigen Hu, and Xiaoliang Zhu. "Mathematical and Computational Modeling in Complex Biological Systems." BioMed Research International 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/5958321.

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The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

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23

Caravagna, Giulio, and Jane Hillston. "Modeling biological systems with delays in Bio-PEPA." Electronic Proceedings in Theoretical Computer Science 40 (October 30, 2010): 85–101. http://dx.doi.org/10.4204/eptcs.40.7.

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24

Coughenour, Michael B. "Biological Systems Modeling—The Philosophy and the Mechanics." Ecology 78, no. 5 (1997): 1609–10. http://dx.doi.org/10.1890/0012-9658(1997)078[1609:bsmtpa]2.0.co;2.

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25

Kiparissides, A., S. S. Kucherenko, A. Mantalaris, and E. N. Pistikopoulos. "Global Sensitivity Analysis Challenges in Biological Systems Modeling." Industrial & Engineering Chemistry Research 48, no. 15 (2009): 7168–80. http://dx.doi.org/10.1021/ie900139x.

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26

Kolev, Mikhail K. "Biological systems modeling in the context of fibrosis." Physics of Life Reviews 17 (July 2016): 98–100. http://dx.doi.org/10.1016/j.plrev.2016.05.010.

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27

Wu, Shinq-Jen, Cheng-Tao Wu, and Jyh-Yeong Chang. "Adaptive neural-based fuzzy modeling for biological systems." Mathematical Biosciences 242, no. 2 (2013): 153–60. http://dx.doi.org/10.1016/j.mbs.2013.01.004.

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28

Ueda, K., T. Kito, and N. Fujii. "Modeling Biological Manufacturing Systems with Bounded-Rational Agents." CIRP Annals 55, no. 1 (2006): 469–72. http://dx.doi.org/10.1016/s0007-8506(07)60461-2.

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29

Bortolussi, Luca, and Alberto Policriti. "Modeling Biological Systems in Stochastic Concurrent Constraint Programming." Constraints 13, no. 1-2 (2008): 66–90. http://dx.doi.org/10.1007/s10601-007-9034-8.

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30

AMIRJANOV, ADIL. "MODELING SELECTION AND EXTINCTION MECHANISMS OF BIOLOGICAL SYSTEMS." International Journal of Modern Physics C 22, no. 07 (2011): 669–86. http://dx.doi.org/10.1142/s0129183111016531.

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In this paper, the behavior of a genetic algorithm is modeled to enhance its applicability as a modeling tool of biological systems. A new description model for selection mechanism is introduced which operates on a portion of individuals of population. The extinction and recolonization mechanism is modeled, and solving the dynamics analytically shows that the genetic drift in the population with extinction/recolonization is doubled. The mathematical analysis of the interaction between selection and extinction/recolonization processes is carried out to assess the dynamics of motion of the macroscopic statistical properties of population. Computer simulations confirm that the theoretical predictions of described models are in good approximations. A mathematical model of GA dynamics was also examined, which describes the anti-predator vigilance in an animal group with respect to a known analytical solution of the problem, and showed a good agreement between them to find the evolutionarily stable strategies.

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31

Machina, Anna, Arkady Ponosov, and Eberhard O. Voit. "Automated piecewise power-law modeling of biological systems." Journal of Biotechnology 149, no. 3 (2010): 154–65. http://dx.doi.org/10.1016/j.jbiotec.2009.12.016.

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32

Wayman, Joseph A., and Jeffrey D. Varner. "Biological systems modeling of metabolic and signaling networks." Current Opinion in Chemical Engineering 2, no. 4 (2013): 365–72. http://dx.doi.org/10.1016/j.coche.2013.09.001.

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33

Aboulmouna, Lina, Rubesh Raja, Sana Khanum, et al. "Cybernetic modeling of biological processes in mammalian systems." Current Opinion in Chemical Engineering 30 (December 2020): 120–27. http://dx.doi.org/10.1016/j.coche.2020.100660.

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34

Moliner, V. "Special issue in computational modeling on biological systems." Archives of Biochemistry and Biophysics 582 (September 2015): 1–2. http://dx.doi.org/10.1016/j.abb.2015.07.013.

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35

Wagler, Annegret K., and Robert Weismantel. "The combinatorics of modeling and analyzing biological systems." Natural Computing 10, no. 2 (2009): 655–81. http://dx.doi.org/10.1007/s11047-009-9165-5.

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36

Sticklen, Jon, and Rula Tufankji. "Utilizing a functional approach for modeling biological systems." Mathematical and Computer Modelling 16, no. 6-7 (1992): 145–59. http://dx.doi.org/10.1016/0895-7177(92)90159-i.

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37

Kim, Jan T., and Roland Eils. "Systems Biology and Artificial Life: Towards Predictive Modeling of Biological Systems." Artificial Life 14, no. 1 (2008): 1–2. http://dx.doi.org/10.1162/artl.2008.14.1.1.

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38

Scianna, Marco, and Annachiara Colombi. "A coherent modeling procedure to describe cell activation in biological systems." Communications in Applied and Industrial Mathematics 8, no. 1 (2017): 1–22. http://dx.doi.org/10.1515/caim-2017-0001.

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Abstract Biological systems are typically formed by different cell phenotypes, characterized by specific biological properties and behaviors. In particular, cells are able to undergo phenotypic transitions (i.e., activation or differentiation) upon internal or external stimuli. In order to take these phenomena into account, we here propose a modelling framework in which cell ensembles can be described collectively (i.e., through a distributed mass density) or individually (i.e., as a group of pointwise/concentrated particles) according to their biological determinants. A set of suitable rules involving the introduction of a cell shape function then defines a coherent procedure to model cell activation mechanisms, which imply a switch between the two mathematical representations. The theoretical environment describing cell transition is then enriched by including cell migratory dynamics and duplication/apoptotic processes, as well as the kinetics of selected diffusing chemicals inuencing the system evolution. Remarkably, our approach provides consistency of the same modeling framework across all types of cell representation, as it is suitable to cope with the often ambiguous translation of individual cell arguments (i.e., cell dimensions and interaction radii) into collective cell descriptions. Biologically relevant numerical realizations are also presented: in particular, they deal with phenotypic transitions within cell colonies and with the growth of a tumor spheroid. These phenomena constitute biological systems particularly suitable to assess the advantages of the proposed model and to analyze the role on cell dynamics both of relevant parameters and of the specific form given to the cell shape function.

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39

Губаль, Г. "Mathematical modeling of biochemical processes rates in biological systems." COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, no. 42 (March 26, 2021): 43–49. http://dx.doi.org/10.36910/6775-2524-0560-2021-42-07.

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40

Eskov, V. V., D. Yu Filatova, L. K. Ilyashenko, and Yu V. Vochmina. "Classification of Uncertainties in Modeling of Complex Biological Systems." Moscow University Physics Bulletin 74, no. 1 (2019): 57–63. http://dx.doi.org/10.3103/s0027134919010089.

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41

Mjolsness, Eric. "Prospects for Declarative Mathematical Modeling of Complex Biological Systems." Bulletin of Mathematical Biology 81, no. 8 (2019): 3385–420. http://dx.doi.org/10.1007/s11538-019-00628-7.

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42

Ouerfelli, Mohamed, Vijay Kumar, and William S. Harwin. "Methods for kinematic modeling of biological and robotic systems." Medical Engineering & Physics 22, no. 7 (2000): 509–20. http://dx.doi.org/10.1016/s1350-4533(00)00063-1.

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43

Doyle, Francis J. "A Systems Approach to Modeling and Analyzing Biological Regulation." IFAC Proceedings Volumes 37, no. 1 (2004): 11–22. http://dx.doi.org/10.1016/s1474-6670(17)38704-9.

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44

Mansouri, Majdi M., Hazem N. Nounou, Mohamed N. Nounou, and Aniruddha A. Datta. "Modeling of nonlinear biological phenomena modeled by S-systems." Mathematical Biosciences 249 (March 2014): 75–91. http://dx.doi.org/10.1016/j.mbs.2014.01.011.

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45

Madec, Morgan, Christophe Lallement, and Jacques Haiech. "Modeling and simulation of biological systems using SPICE language." PLOS ONE 12, no. 8 (2017): e0182385. http://dx.doi.org/10.1371/journal.pone.0182385.

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46

Gluskin, E. "Modeling the mechanical action of fiber-type biological systems." IEEE Engineering in Medicine and Biology Magazine 18, no. 5 (1999): 112–14. http://dx.doi.org/10.1109/51.790994.

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47

Broderick, Gordon, and Eitan Rubin. "The Realistic Modeling of Biological Systems: A Workshop Synopsis." Complexus 3, no. 4 (2006): 217–30. http://dx.doi.org/10.1159/000106145.

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48

Rosati, Elise, Morgan Madec, Jean-Baptiste Kammerer, Luc Hébrard, Christophe Lallement, and Jacques Haiech. "Efficient Modeling and Simulation of Space-Dependent Biological Systems." Journal of Computational Biology 25, no. 8 (2018): 917–33. http://dx.doi.org/10.1089/cmb.2018.0012.

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49

Vande Wouwer, Alain, Philippe Bogaerts, Jan Van Impe, and Alejandro Vargas. "Mathematical Modeling and Dynamic Analysis of Complex Biological Systems." Complexity 2019 (February 25, 2019): 1–2. http://dx.doi.org/10.1155/2019/4858423.

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50

Yesylevskyy, S. O. "MOLECULAR MODELING OF BIOLOGICAL SYSTEMS: CURRENT PROGRESS AND PROSPECTS." Visnik Nacional'noi' academii' nauk Ukrai'ni 06 (June 20, 2018): 43–49. http://dx.doi.org/10.15407/visn2018.06.043.

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