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Journal articles on the topic 'Biological control systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Rogerson, Clark T., and M. N. Burge. "Fungi in Biological Control Systems." Brittonia 41, no. 4 (1989): 398. http://dx.doi.org/10.2307/2807554.

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2

Stark, Lawrence, and Laurence R. Young. "DEFINING BIOLOGICAL FEEDBACK CONTROL SYSTEMS *." Annals of the New York Academy of Sciences 117, no. 1 (2006): 426–42. http://dx.doi.org/10.1111/j.1749-6632.1964.tb48200.x.

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3

Cavalieri, Liebe F., and Huseyin Koçak. "Chaos in Biological Control Systems." Journal of Theoretical Biology 169, no. 2 (1994): 179–87. http://dx.doi.org/10.1006/jtbi.1994.1139.

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4

Baev, K. V. "Optimal control in biological motor control systems." IEEE Engineering in Medicine and Biology Magazine 11, no. 4 (1992): 82–83. http://dx.doi.org/10.1109/51.257006.

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5

van Emden, H. F., M. A. Hoy, and D. C. Herzog. "Biological Control in Agricultural IPM Systems." Journal of Applied Ecology 23, no. 2 (1986): 728. http://dx.doi.org/10.2307/2404055.

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6

Ames, W. F. "Evolution and control in biological systems." Mathematics and Computers in Simulation 31, no. 6 (1990): 594. http://dx.doi.org/10.1016/0378-4754(90)90064-p.

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7

CABANAC, MICHEL, and MAURICIO RUSSEK. "REGULATED BIOLOGICAL SYSTEMS." Journal of Biological Systems 08, no. 02 (2000): 141–49. http://dx.doi.org/10.1142/s0218339000000092.

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Control theory is concerned mainly with the treatment of signals. This article takes into account that living beings not only treat information, but they are open systems traversed by flows of energy and mass. A new block diagram of the regulation process is proposed, taking into account this fundamental difference between engineered and living systems. This new diagram possesses both didactic and heuristic advantages.

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8

Balchunas, Brian M., Lawrence H. Hentz, and William H. Salley. "ODOR CONTROL CONSIDERATIONS FOR BIOLOGICAL TREATMENT SYSTEMS." Proceedings of the Water Environment Federation 2000, no. 3 (2000): 1042–52. http://dx.doi.org/10.2175/193864700785303376.

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9

Yun, Choamun, Young Kim, Sang Yup Lee, and Sunwon Park. "Metabolic Control Analysis of Complex Biological Systems." IFAC Proceedings Volumes 41, no. 2 (2008): 9823–27. http://dx.doi.org/10.3182/20080706-5-kr-1001.01662.

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10

Iberall, A. S., and S. Z. Cardon. "CONTROL IN BIOLOGICAL SYSTEMS - A PHYSICAL REVIEW *." Annals of the New York Academy of Sciences 117, no. 1 (2006): 445–515. http://dx.doi.org/10.1111/j.1749-6632.1964.tb48202.x.

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11

Houchmandzadeh, Bahram, and Irina Mihalcescu. "Fluctuations importance and control in biological systems." Europhysics News 42, no. 6 (2011): 36–39. http://dx.doi.org/10.1051/epn/2011606.

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12

Sekiguchi, Masayuki. "Biological Systems that Control Conditioned Fear Memory." Anxiety Disorder Research 5, no. 2 (2014): 85–92. http://dx.doi.org/10.14389/adr.5.85.

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13

González-Andújar, J. L., and J. N. Perry. "Reversals of chaos in biological control systems." Journal of Theoretical Biology 175, no. 4 (1995): 603. http://dx.doi.org/10.1006/jtbi.1995.0169.

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14

Madhav, Manu S., and Noah J. Cowan. "The Synergy Between Neuroscience and Control Theory: The Nervous System as Inspiration for Hard Control Challenges." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (2020): 243–67. http://dx.doi.org/10.1146/annurev-control-060117-104856.

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Here, we review the role of control theory in modeling neural control systems through a top-down analysis approach. Specifically, we examine the role of the brain and central nervous system as the controller in the organism, connected to but isolated from the rest of the animal through insulated interfaces. Though biological and engineering control systems operate on similar principles, they differ in several critical features, which makes drawing inspiration from biology for engineering controllers challenging but worthwhile. We also outline a procedure that the control theorist can use to dr

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15

Kholodenko, Boris N., Oleg V. Demin, and Hans V. Westerhoff. "Control Analysis of Periodic Phenomena in Biological Systems." Journal of Physical Chemistry B 101, no. 11 (1997): 2070–81. http://dx.doi.org/10.1021/jp962336u.

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16

Zheng, Likun, Meng Chen, and Qing Nie. "External noise control in inherently stochastic biological systems." Journal of Mathematical Physics 53, no. 11 (2012): 115616. http://dx.doi.org/10.1063/1.4762825.

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17

Hogan, N. "Control of contact in robots and biological systems." IEEE Engineering in Medicine and Biology Magazine 11, no. 4 (1992): 81–82. http://dx.doi.org/10.1109/51.257003.

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18

Awrejcewicz, Jan. "Biological and mechanical systems in modern control theory." Communications in Nonlinear Science and Numerical Simulation 16, no. 5 (2011): 2203–4. http://dx.doi.org/10.1016/j.cnsns.2010.06.005.

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19

Salek, S. S., A. G. van Turnhout, R. Kleerebezem, and M. C. M. van Loosdrecht. "pH control in biological systems using calcium carbonate." Biotechnology and Bioengineering 112, no. 5 (2015): 905–13. http://dx.doi.org/10.1002/bit.25506.

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20

Somvanshi, Pramod R., Anilkumar K. Patel, Sharad Bhartiya, and K. V. Venkatesh. "Implementation of integral feedback control in biological systems." Wiley Interdisciplinary Reviews: Systems Biology and Medicine 7, no. 5 (2015): 301–16. http://dx.doi.org/10.1002/wsbm.1307.

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21

Goulet-Hanssens, Alexis, and Christopher J. Barrett. "Photo-control of biological systems with azobenzene polymers." Journal of Polymer Science Part A: Polymer Chemistry 51, no. 14 (2013): 3058–70. http://dx.doi.org/10.1002/pola.26735.

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22

Cross, Katy A., and Marco Iacoboni. "Neural systems for preparatory control of imitation." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1644 (2014): 20130176. http://dx.doi.org/10.1098/rstb.2013.0176.

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Humans have an automatic tendency to imitate others. Previous studies on how we control these tendencies have focused on reactive mechanisms, where inhibition of imitation is implemented after seeing an action. This work suggests that reactive control of imitation draws on at least partially specialized mechanisms. Here, we examine preparatory imitation control, where advance information allows control processes to be employed before an action is observed. Drawing on dual route models from the spatial compatibility literature, we compare control processes using biological and non-biological st

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23

Hussain, Shahid, Clayton Yates, and Moray J. Campbell. "Vitamin D and Systems Biology." Nutrients 14, no. 24 (2022): 5197. http://dx.doi.org/10.3390/nu14245197.

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The biological actions of the vitamin D receptor (VDR) have been investigated intensively for over 100 years and has led to the identification of significant insights into the repertoire of its biological actions. These were initially established to be centered on the regulation of calcium transport in the colon and deposition in bone. Beyond these well-known calcemic roles, other roles have emerged in the regulation of cell differentiation processes and have an impact on metabolism. The purpose of the current review is to consider where applying systems biology (SB) approaches may begin to ge

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24

Liu, Peiyong, Qingling Zhang, Xiaoguang Yang, and Li Yang. "Passivity and Optimal Control of Descriptor Biological Complex Systems." IEEE Transactions on Automatic Control 53, Special Issue (2008): 122–25. http://dx.doi.org/10.1109/tac.2007.911341.

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25

NIU, HONG, and QINGLING ZHANG. "GENERALIZED PREDICTIVE CONTROL FOR DIFFERENCE-ALGEBRAIC BIOLOGICAL ECONOMIC SYSTEMS." International Journal of Biomathematics 06, no. 06 (2013): 1350037. http://dx.doi.org/10.1142/s179352451350037x.

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In this paper, a nonlinear difference-algebraic system is used to model some populations with stage structure when the harvest behavior and the economic interest are considered. The stability analysis is studied at the equilibrium points. After the nonlinear difference-algebraic system is changed into a linear system with the unmodeled dynamics, a generalized predictive controller with feedforward compensator is designed to stabilize the system. Adaptive-network-based fuzzy inference system (ANFIS) is used to make the unmodeled dynamic compensated. An example illustrates the effectiveness of t

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26

Imura, Jun-Ichi, Kenji Kashima, Masami Kusano, Tsukasa Ikeda, and Tomohiro Morohoshi. "Piecewise affine systems approach to control of biological networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1930 (2010): 4977–93. http://dx.doi.org/10.1098/rsta.2010.0176.

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In terms of a piecewise affine system representation, which is a kind of hybrid system model, this article discusses a series of approaches to modelling, analysing and synthesizing a biological network such as a gene-regulatory network. First, the input assignment problem, the controllable state set problem (CSP) and the input trajectory generation problem are emphasized as control problems to be addressed for biological networks. Subsequently, after the modelling issue on biological networks developed in the systems and control community is briefly explained, the CSP is described in detail wi

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27

White, S. M. "Applications of biological control in resistant host-pathogen systems." Mathematical Medicine and Biology 22, no. 3 (2005): 227–45. http://dx.doi.org/10.1093/imammb/dqi006.

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28

Drummond, Frank, and Beth Choate. "Ants as biological control agents in agricultural cropping systems." Terrestrial Arthropod Reviews 4, no. 2 (2011): 157–80. http://dx.doi.org/10.1163/187498311x571979.

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AbstractAnts positively impact agricultural systems by rapidly consuming large numbers of pest insects, disturbing pests during feeding and oviposition, and increasing soil quality and nutrients. The ability of ants to control pest species has been recognized since the year 300 A.D. and farmers continue to conserve and promote ant populations in agricultural systems worldwide. Naturally occurring ant species in milpas, mango, citrus, coconut, cashews, and cotton control many pest insects. Through judicious insecticide application and changes in management practices such as tillage, and other m

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29

Hahn, Juergen, Thomas Edison, and Thomas F. Edgar. "Adaptive IMC control for drug infusion for biological systems." Control Engineering Practice 10, no. 1 (2002): 45–56. http://dx.doi.org/10.1016/s0967-0661(01)00108-3.

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30

RAFIKOV, MARAT, JOSÉ MANOEL BALTHAZAR, and HUBERTUS F. VON BREMEN. "MANAGEMENT OF COMPLEX SYSTEMS: MODELING THE BIOLOGICAL PEST CONTROL." Biophysical Reviews and Letters 03, no. 01n02 (2008): 241–56. http://dx.doi.org/10.1142/s1793048008000721.

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The aim of this paper is to study the cropping system as complex one, applying methods from theory of dynamic systems and from the control theory to the mathematical modeling of the biological pest control. The complex system can be described by different mathematical models. Based on three models of the pest control, the various scenarios have been simulated in order to obtain the pest control strategy only through natural enemies' introduction.

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31

Mills, Nick J. "Factors influencing top-down control of insect pest populationsin biological control systems." Basic and Applied Ecology 2, no. 4 (2001): 323–32. http://dx.doi.org/10.1078/1439-1791-00070.

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32

Rafikov, M., J. M. Balthazar, and H. F. von Bremen. "Mathematical modeling and control of population systems: Applications in biological pest control." Applied Mathematics and Computation 200, no. 2 (2008): 557–73. http://dx.doi.org/10.1016/j.amc.2007.11.036.

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33

Lumsden, Robert D., and George C. Papavizas. "Biological control of soilborne plant pathogens." American Journal of Alternative Agriculture 3, no. 2-3 (1988): 98–101. http://dx.doi.org/10.1017/s0889189300002253.

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AbstractSoilborne plant pathogens cause major economic losses in agricultural crops, and the present methods for control of diseases brought about by these pathogens are inadequate. Alternatives are also needed to substitute for the use of chemical fungicides. Many of these are known to induce tumors in experimental animals and are thus regarded by some investigators as potential human carcinogens when present as residues in food and water. In addition, such alternative control measures are needed because of the potential threat of development of resistance to fungicides, especially systemic f

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34

Gubbins, Simon, and Christopher A. Gilligan. "Biological control in a disturbed environment." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 352, no. 1364 (1997): 1935–49. http://dx.doi.org/10.1098/rstb.1997.0180.

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Most ecological and epidemiological models describe systems with continuous uninterrupted interactions between populations. Many systems, though, have ecological disturbances, such as those associated with planting and harvesting of a seasonal crop. In this paper, we introduce host—parasite—hyperparasite systems as models of biological control in a disturbed environment, where the host—parasite interactions are discontinuous. One model is a parasite—hyperparasite system designed to capture the essence of biological control and the other is a host—parasite—hyperparasite system that incorporates

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35

Li, Min, Hao Gao, Jianxin Wang, and Fang-Xiang Wu. "Control principles for complex biological networks." Briefings in Bioinformatics 20, no. 6 (2018): 2253–66. http://dx.doi.org/10.1093/bib/bby088.

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Abstract Networks have been widely used to model the structure of various biological systems. Currently, a series of approaches have been developed to construct reliable biological networks. However, the ultimate understanding of a biological system is to steer its states to the desired ones by imposing signals. The control process is dominated by the intrinsic structure and the dynamic propagation. To understand the underlying mechanisms behind the life process, the control theory can be applied to biological networks with specific target requirements. In this article, we first introduce the

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36

Narcross, Fredric. "Artificial nervous systems – a technology to achieve biologically modeled intelligence and control for robotics." Journal of Physics: Conference Series 2506, no. 1 (2023): 012008. http://dx.doi.org/10.1088/1742-6596/2506/1/012008.

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Abstract Migrating from machine learning and deep learning into the next wave of technology will likely require biological replication rather than biological inspiration. An approach to achieving this requires duplicating entire nervous systems, or at least parts thereof. In theory, these artificial nervous systems (ANS) could reproduce everything required for a system to be biologically intelligent even to the point of being self-aware. This would additionally entail that the resultant systems have the ability to acquire information from both their internal and external environments as well a

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37

Elmer, R. A. G., S. M. Hoyte, J. L. Vanneste, T. Reglinski, R. N. Wood, and F. J. Parry. "Biological control of fruit pathogens." New Zealand Plant Protection 58 (August 1, 2005): 47–54. http://dx.doi.org/10.30843/nzpp.2005.58.4253.

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Disease management in fruit crops worldwide is heavily dependent upon the application of synthetic fungicides for pathogen control However restrictions on fungicide use and widespread emergence of pathogen resistance has increased global demand for more sustainable production systems and driven research towards alternative disease control strategies Biological control which includes elicitors of host defence microbial antagonists and natural products offers an attractive alternative to synthetic pesticides This paper reviews the commercialisation of biological control agents for botrytis in gr

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38

Ch'ng, Eugene. "Modelling the Adaptability of Biological Systems." Open Cybernetics & Systemics Journal 1, no. 1 (2007): 13–20. http://dx.doi.org/10.2174/1874110x00701010013.

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39

Zheleznyakov, Dmitry Victorovich, Julia Aleksandrovna Golovko, Sergey Vladimirovich Golovko, and Anatoliy Mikhailovich Likhter. "Algorithms for information transmission in biological object management systems." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2024, no. 2 (2024): 38–46. http://dx.doi.org/10.24143/2072-9502-2024-2-38-46.

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One of the problems of the theory of management of objects with a high degree of uncertainty of behavior is solved: the development of theoretical foundations of analysis, modeling methods and improvement of bio cybernetic systems, their algorithmic and software necessary to improve the efficiency of the management process is proposed. To solve this problem, it is proposed to use the mathematical apparatus of information theory, on the basis of which the information characteristics of the control signal transmission channel from the source to the control object through the external environment

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40

Marković, Dimitrije. "Crop Diversification Affects Biological Pest Control." АГРОЗНАЊЕ 14, no. 3 (2013): 449. http://dx.doi.org/10.7251/agren1303449m.

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Crop monocultures encourage the multiplication and spread of pest insects on massive and uniform crop. Numerous studies have evaluated the impact of plant diversification on pests and beneficial arthropods population dynamics in agricultural ecosystems and provided some evidence that habitat manipulation techniques like intercropping can significantly influence pest control. This paper describes various potential options of habitat management and design that enhance ecological role of biodiversity in agroecosystems. The focus of this review is the application and mechanisms of biodiversity in

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41

Pereira, R. R., D. V. C. Neves, J. N. Campos, P. A. Santana Júnior, T. E. Hunt, and M. C. Picanço. "Natural biological control ofChrysodeixis includens." Bulletin of Entomological Research 108, no. 6 (2018): 831–42. http://dx.doi.org/10.1017/s000748531800007x.

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AbstractA wide variety of abiotic and biotic factors act on insect pests to regulate their populations. Knowledge of the time and magnitude of these factors is fundamental to understanding population dynamics and developing efficient pest management systems. We investigate the natural mortality factors, critical pest stages, and key mortality factors that regulateChrysodeixis includenspopulations via ecological life tables. The total mortality caused by natural factors was 99.99%. Natural enemies were the most important mortality factors in all pest stages. The critical stages ofC. includensmo

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42

Rivas-García, Tomás, Ramsés Ramón González-Estrada, Roberto Gregorio Chiquito-Contreras, et al. "Biocontrol of Phytopathogens under Aquaponics Systems." Water 12, no. 7 (2020): 2061. http://dx.doi.org/10.3390/w12072061.

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Aquaponics is an alternative method of food production that confers advantages of biological and economic resource preservations. Nonetheless, one of the main difficulties related to aquaponics systems could be the outbreak and dissemination of pathogens. Conventional treatments need to be administrated carefully because they could be harmful to human, fish, plants and beneficial microorganisms. Aquaponics practitioners are relatively helpless against plant diseases when they occur, especially in the case of root pathogens. Biological control agents (BCAs) may be an effective alternative to ch

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43

Jin, Suoqin, Dingjie Wang, and Xiufen Zou. "Trajectory Control in Nonlinear Networked Systems and Its Applications to Complex Biological Systems." SIAM Journal on Applied Mathematics 78, no. 1 (2018): 629–49. http://dx.doi.org/10.1137/17m1116143.

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44

Chase, J. Geoffrey, Marcos de Sales Guerra Tsuzuki, Balázs Benyó, and Thomas Desaive. "Editorial: Special Section on Biological Medical Systems." Annual Reviews in Control 48 (2019): 357–58. http://dx.doi.org/10.1016/j.arcontrol.2019.08.005.

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45

Tzoumas, Vasileios, Yuankun Xue, Sergio Pequito, Paul Bogdan, and George J. Pappas. "Selecting Sensors in Biological Fractional-Order Systems." IEEE Transactions on Control of Network Systems 5, no. 2 (2018): 709–21. http://dx.doi.org/10.1109/tcns.2018.2809959.

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46

Knudsen, Guy R., and Louise-Marie C. Dandurand. "Ecological Complexity and the Success of Fungal Biological Control Agents." Advances in Agriculture 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/542703.

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Fungal biological control agents against plant pathogens, especially those in soil, operate within physically, biologically, and spatially complex systems by means of a variety of trophic and nontrophic interspecific interactions. However, the biocontrol agents themselves are also subject to the same types of interactions, which may reduce or in some cases enhance their efficacy against target plant pathogens. Characterization of these ecologically complex systems is challenging, but a number of tools are available to help unravel this complexity. Several of these tools are described here, inc

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47

Elahi, Yara, and Matthew Arthur Barrington Baker. "Light Control in Microbial Systems." International Journal of Molecular Sciences 25, no. 7 (2024): 4001. http://dx.doi.org/10.3390/ijms25074001.

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Light is a key environmental component influencing many biological processes, particularly in prokaryotes such as archaea and bacteria. Light control techniques have revolutionized precise manipulation at molecular and cellular levels in recent years. Bacteria, with adaptability and genetic tractability, are promising candidates for light control studies. This review investigates the mechanisms underlying light activation in bacteria and discusses recent advancements focusing on light control methods and techniques for controlling bacteria. We delve into the mechanisms by which bacteria sense

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48

Skirvin, D. J. "CHASING THE DREAM: A SYSTEMS MODELLING APPROACH TO BIOLOGICAL CONTROL." Acta Horticulturae, no. 916 (December 2011): 129–39. http://dx.doi.org/10.17660/actahortic.2011.916.13.

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49

Paulitz, T. C. "Biological Control of Root Pathogens in Soilless and Hydroponic Systems." HortScience 32, no. 2 (1997): 193–96. http://dx.doi.org/10.21273/hortsci.32.2.193.

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50

Pavel, Mariana, Radu Tanasa, So Jung Park, and David C. Rubinsztein. "The complexity of biological control systems: An autophagy case study." BioEssays 44, no. 3 (2022): 2100224. http://dx.doi.org/10.1002/bies.202100224.

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