System biological control

As previously mentioned, the glucose-insulin control system is often regarded as a simple system to keep the plasma glucose concentration within narrow limits. In this context it has been compared to technical control systems and described by simple, often linear or linearized models. Section 6.2 of this chapter gives an outline of classic control and underlines some of the peculiarities of biological control systems. 

Biological control systems are often regarded as some sloppy variants of the more precise engineering control systems. Classic control theory considers linear, stable and stationary systems [1-3]. To this could be added well defined. Biological systems are nonlinear, often unstable, and never stationary. They work with small feedback gains, typically less than 10 [4—6] they are interwoven, so completely different systems share common routes (hormones, nerves, etc.) and their properties vary from person to person, even in healthy people. 

In technical systems, the controller compares Cl and CS and sends a signal (CC) to the effector, and the type of control is classified after how CC depends on Cf and CS. It is often possible to identify similar classes of biological control systems as... 

Another shortcoming is that combinations of integral control often give rise to oscillations. Actually, many biological control systems may oscillate (blood glucose, blood pressure, many hormones, etc.), but usually the oscillations are limited. 

L R. Young, L. Stark, Biological control systems - a critical review and evaluation. Developments in manual control, NASA CR 1965,1-121. 

These two complementary systems allow the bacterial cell to metabolize lactose in response to two stimuli. Switching on the expression of the lac operon requires both the absence of glucose and the presence of lactose. This series of switches allows complex expression patterns to be built up from simple components. For this reason, the lac system is a model for other, apparently more complex, biological control systems, such as hormone action or embryonic development. 

Adams, E, B. In Fungi in Biological Control Systems Burge,... 

Our initial studies of dynamics in biochemical networks included spatially localized components [32]. As a consequence, there will be delays involved in the transport between the nuclear and cytoplasmic compartments. Depending on the spatial structure, different dynamical behaviors could be faciliated, but the theoretical methods are useful to help understand the qualitative features. In other (unpublished) work, computations were carried out in feedback loops with cyclic attractors in which a delay was introduced in one of the interactions. Although the delay led to an increase of the period, the patterns of oscillation remained the same. However, delays in differential equations that model neural networks and biological control systems can introduce novel dynamics that are not present without a delay (for example, see Refs. 57 and 58). 

The evolutionary development of an integrated network of specialized compartments in mammalian cells has facilitated the development of biologic control systems which evaluate, store, and process chemical information. Mammalian membranes are macromolecular lipid complexes which physically separate specialized intracellular compartments, provide an interfacial matrix for the interaction of... 

Bartlett MC, Jaronski ST. Mass production of entomogenous fungi for biological control of insects. In Burge MN, ed. Fungi in Biological Control Systems. Manchester, UK Manchester University Press, 1988, pp 61-85. 

The balance of this paper will be devoted to a model that has been used to facilitate the understanding of a complex biological control system. The erythropoietic system is relatively simple. However, when the interactions of associated physiological systems are considered, the control of erythropoiesis becomes highly interactive and complex. 

Nonorganismal BU use communications to support the needs of the organism. Their sensing and responses are parts of overall biological control systems that maintain the ability of an organism to act as one coordinated whole rather than a disorganized assanblage of independent parts (see Section 6.16). 

Milsum, J. H., 1966, Biological Control Systems Analysis, McGraw-Hill, New York. 

Watanabe, A. and Stark, L. 1975. Kernel method for nonlinear analysis identification of a biological control system. Math. Biosci. 27 99. 

Milic-EmUi, J. and Zin, W.A. 1986. Relationship between neuromuscular respiratory drive and ventilatory output. In P.T. Macklem and J. Mead (Eds.), Handbook of Physiology, sec 3, The Respiratory System, Vol. 3, Mechanics of Breathing, part 2, pp. 631-646, Washington, DC, American Physiological Society. Milsum, J.H. 1966. Biological Control Systems Analysis. New York, McGraw-Hill. 

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