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Journal articles on the topic 'Biological control systems'

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Published: 4 June 2021
Last updated: 29 July 2024

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1

Rogerson, Clark T., and M. N. Burge. "Fungi in Biological Control Systems." Brittonia 41, no. 4 (October 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 (December 16, 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 (July 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 (December 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 (August 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 (February 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 (June 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 (January 1, 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 (December 16, 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 (September 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 (August 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 (May 3, 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 draw inspiration from the biological controller: starting from the intact, behaving animal; designing experiments to deconstruct and model hierarchies of feedback; modifying feedback topologies; perturbing inputs and plant dynamics; using the resultant outputs to perform system identification; and tuning and validating the resultant control-theoretic model using specially engineered robophysical models.
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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 (March 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 (November 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 (December 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 (May 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 (January 16, 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 (June 2, 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 (May 24, 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 (June 5, 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 stimuli to determine whether preparatory imitation control recruits specialized neural systems that are similar to those observed in reactive imitation control. Results indicate that preparatory control involves anterior prefrontal, dorsolateral prefrontal, posterior parietal and early visual cortices regardless of whether automatic responses are evoked by biological (imitative) or non-biological stimuli. These results indicate both that preparatory control of imitation uses general mechanisms, and that preparatory control of imitation draws on different neural systems from reactive imitation control. Based on the regions involved, we hypothesize that preparatory control is implemented through top-down attentional biasing of visual processing.
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23

Li, Min, Hao Gao, Jianxin Wang, and Fang-Xiang Wu. "Control principles for complex biological networks." Briefings in Bioinformatics 20, no. 6 (September 18, 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 structural controllability of complex networks and discuss its advantages and disadvantages. Then, we review the effective control to meet the specific requirements for complex biological networks. Moreover, we summarize the existing methods for finding the unique minimum set of driver nodes via the optimal control for complex networks. Finally, we discuss the relationships between biological networks and structural controllability, effective control and optimal control. Moreover, potential applications of general control principles are pointed out.
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24

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

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25

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 (July 2008): 557–73. http://dx.doi.org/10.1016/j.amc.2007.11.036.

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26

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 (January 2008): 122–25. http://dx.doi.org/10.1109/tac.2007.911341.

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27

NIU, HONG, and QINGLING ZHANG. "GENERALIZED PREDICTIVE CONTROL FOR DIFFERENCE-ALGEBRAIC BIOLOGICAL ECONOMIC SYSTEMS." International Journal of Biomathematics 06, no. 06 (November 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 the proposed control method.
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28

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 (November 13, 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 with reference to control of the quorum-sensing system in the pathogen Pseudomonas aeruginosa . Finally, an optimal control design method to the quorum-sensing system is proposed as a solution to the input trajectory generation problem.
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29

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

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30

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 manipulations of vegetation and crop structure, beneficial ant populations are conserved in a variety of agroecosystems. The first recorded example of biological control was the manipulation of ants throughout citrus orchards in Asia. Augmentation continues in citrus, and methods of ant introduction have been developed in Malaysian and Indonesian cocoa plantations, as well as to control sweet potato and banana weevils in Cuba. Ant species have been formally incorporated into other integrated pest management programs for cashew in Australia, cocoa in Papua New Guinea, and mango in Australia and Vietnam. With efforts to reduce chemical pesticide input in agricultural systems, research evaluating the ability of generalist ant species to control pest insects must continue.
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31

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

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32

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 (April 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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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 fungicides, by fungal plant pathogens, and because of nontarget side effects on other plant pathogens and on beneficial microorganisms. Alternative disease control is sometimes possible through development of crop plants resistant to disease. Unfortunately, however, resistance is lacking or not available for many diseases caused by soilborne plant pathogens. Another biological means of controlling disease which is presently gaining much attention is biological control. Several systems of biological control are presently being explored and may be developed in a few years into reliable alternatives to conventional chemical control methods. The use of the mycoparasite Sporidesmium sclerotivorum, for example, against several diseases caused by Sclerotinia species is promising. Talaromyces flavus may in the future be exploited for use against several wilt diseases caused by Verticillium dahliae. Finally, practical control of several diseases caused by Pythium spp., Rhizoctonia solani, and Sclerotium rolfsii may eventually become possible through the use of Trichoderma spp. and Gliocladium virens. Development of these biological control systems will require much additional research directed toward a better understanding of the basic biology and mechanisms of action of beneficial fungi against plant pathogens. In addition, extensive cooperation will be required among research scientists, governmental agencies responsible for regulating the use of pestcontrol systems, and most importantly, private industry to develop biological control agents for the market and to coordinate acceptance and use by producers and acceptance by consumers.
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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 (December 29, 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 many more features of the population dynamics. Two types of discontinuity are included in the models. One corresponds to a pulse of new parasites at harvest and the other reflects the discontinuous presence of the host due to planting and harvesting. Such discontinuities are characteristic of many ecosystems involving parasitism or other interactions with an annual host. The models are tested against data from an experiment investigating the persistent biological control of the fungal plant parasite of lettuce Sclerotinia minor by the fungal hyperparasite Sporidesmium sclerotivorum , over successive crops. Using a combination of mathematical analysis, model fitting and parameter estimation, the factors that contribute the observed persistence of the parasite are examined. Analytical results show that repeated planting and harvesting of the host allows the parasite to persist by maintaining a quantity of host tissue in the system on which the parasite can reproduce. When the host dynamics are not included explicitly in the model, we demonstrate that homogeneous mixing fails to predict the persistence of the parasite population, while incorporating spatial heterogeneity by allowing for heterogeneous mixing prevents fade–out. Including the host's dynamics lessens the effect of heterogeneous mixing on persistence, though the predicted values for the parasite population are closer to the observed values. An alternative hypothesis for persistence involving a stepped change in rates of infection is also tested and model fitting is used to show that changes in some environmental conditions may contribute to parasite persistence. The importance of disturbances and periodic forcing in models for interacting populations is discussed.
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35

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 grapes (BOTRYZen) and fire blight in apples and pears (Blossom Bless PomaVita) and the development of a biological control agent for sclerotinia in kiwifruit The importance of understanding disease epidemiology as a prerequisite for developing a biological control system is discussed along with future prospects for biological control of these pathogens
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36

Ch'ng, Eugene. "Modelling the Adaptability of Biological Systems." Open Cybernetics & Systemics Journal 1, no. 1 (December 26, 2007): 13–20. http://dx.doi.org/10.2174/1874110x00701010013.

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37

Hussain, Shahid, Clayton Yates, and Moray J. Campbell. "Vitamin D and Systems Biology." Nutrients 14, no. 24 (December 7, 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 generate a more precise understanding of where the VDR is, and is not, biologically impactful. Two SB approaches have been developed and begun to reveal insight into VDR biological functions. In a top-down SB approach genome-wide scale data are statistically analyzed, and from which a role for the VDR emerges in terms of being a hub in a biological network. Such approaches have confirmed significant roles, for example, in myeloid differentiation and the control of inflammation and innate immunity. In a bottom-up SB approach, current biological understanding is built into a kinetic model which is then applied to existing biological data to explain the function and identify unknown behavior. To date, this has not been applied to the VDR, but has to the related ERα and identified previously unknown mechanisms of control. One arena where applying top-down and bottom-up SB approaches may be informative is in the setting of prostate cancer health disparities.
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38

Narcross, Fredric. "Artificial nervous systems – a technology to achieve biologically modeled intelligence and control for robotics." Journal of Physics: Conference Series 2506, no. 1 (May 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 as having the ability to act within the external environment using locomotion and manipulators. Robots are a natural answer for the resultant mechanism and if supplied with an artificial nervous system, the robot might be expected to achieve biologically modelled intelligence (BMI) and control. This paper will provide an overview of the tools for creating artificial nervous systems, as well as provide a roadmap for utilizing the tools to develop robots with general-purpose learning skills and biologically modelled intelligence.
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39

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 (February 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. includensmortality were second and fourth instars. The key mortality factors were predation by ants in the second instar and predation by Vespidae in the fourth instar. The elimination of these factors can cause an increase of 77.52 and 85.17% ofC. includenspopulation, respectively. This study elucidates the importance of natural enemies and other natural mortality factors inC. includenspopulation regulation. These factors should be considered in developing and implementingC. includensmanagement strategies and tactics in order to achieve effective and sustainable pest control.
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40

Marković, Dimitrije. "Crop Diversification Affects Biological Pest Control." АГРОЗНАЊЕ 14, no. 3 (December 13, 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 agricultural systems to enhance pest management.
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41

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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42

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 (April 26, 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 are calculated, taking into account the noise of the ecosystem of the control object. The existing systems using monomodal control signals that do not carry a semantic load are analyzed, and the disadvantages and limited possibilities of improving the control systems under consideration are described. Using the example of one of the insect behavior control systems with a trichromic view, a comparison of the results obtained using various technical means used as control signal sources was performed. For improving the efficiency of control systems, which use a multimodal control signal structure, proposed various algorithms for constructing control systems using a multimodal control signal structure are considered, namely: serial and parallel circuits, combined, combining serial and parallel algorithms for using control signal sources. As a method for further improving the efficiency of control systems for biological objects with a high degree of uncertainty, it is proposed to use biological signals containing a large semantic load for the control object, which determines its behavioral response. The directions of further research are formulated in order to maximize the amount of information transmitted to the control object.
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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 (January 2018): 629–49. http://dx.doi.org/10.1137/17m1116143.

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44

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 (June 2018): 709–21. http://dx.doi.org/10.1109/tcns.2018.2809959.

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45

Elahi, Yara, and Matthew Arthur Barrington Baker. "Light Control in Microbial Systems." International Journal of Molecular Sciences 25, no. 7 (April 3, 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 and transduce light signals, including engineered photoreceptors and light-sensitive actuators, and various strategies employed to modulate gene expression, protein function, and bacterial motility. Furthermore, we highlight recent developments in light-integrated methods of controlling microbial responses, such as upconversion nanoparticles and optical tweezers, which can enhance the spatial and temporal control of bacteria and open new horizons for biomedical applications.
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46

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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47

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

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48

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 (January 14, 2022): 2100224. http://dx.doi.org/10.1002/bies.202100224.

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49

Culshaw‐Maurer, Michael, Andrew Sih, and Jay A. Rosenheim. "Bugs scaring bugs: enemy‐risk effects in biological control systems." Ecology Letters 23, no. 11 (September 9, 2020): 1693–714. http://dx.doi.org/10.1111/ele.13601.

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50

Bullinaria, John A. "From biological models to the evolution of robot control systems." Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 361, no. 1811 (August 20, 2003): 2145–64. http://dx.doi.org/10.1098/rsta.2003.1249.

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