Physiology of the circadian timing system: predictive versus reactive homeostasis.
ABSTRACT Since Cannon first formulated the concept of homeostasis 60 years ago, attention has been focused on the corrective responses initiated after the steady state of the organism is perturbed. In this lecture it is argued that the concept of homeostasis should be extended to include the precisely timed mechanisms of the circadian (and circannual) timing system which enables organisms to predict when environmental challenges are most likely to occur. A mature understanding of homeostasis should encompass both "reactive" responses to changes in physiological variables which have already occurred and the "predictive" responses initiated in anticipation of predictably timed challenges. Such predictive responses have particular value because they enable physiological mechanisms to be utilized immediately, even if they involve a delay of several hours, by activating them at a suitable time in advance of a probable challenge. However, conflicts may often occur between predictions and reality. Examples from sleep-wake behavior, thermoregulation, blood volume homeostasis, and the regulation of potassium balance show that predictive responses often compromise the effectiveness of reactive homeostatic mechanisms even to the point of risking the survival of the organism. It must be concluded that the day-night cycle of the natural environment has played a fundamental role in shaping the evolutionary development of homeostatic mechanisms because of the dominating predictability of diurnal changes in illumination, temperature, food availability, and predator activity.
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ABSTRACT: Pregnancy is a complex and well-regulated temporal event in which several steps are finely orchestrated including implantation, decidualization, placentation, and partum and any temporary alteration has serious effects on fetal and maternal health. Interestingly, alterations of circadian rhythms (i.e., shiftwork) have been correlated with increased risk of preterm delivery, intrauterine growth restriction, and preeclampsia. In the last few years evidence is accumulating that the placenta may have a functional circadian system and express the clock genes Bmal1, Per1-2, and Clock. On the other hand, there is evidence that the human placenta synthesizes melatonin, hormone involved in the regulation of the circadian system in other tissues. Moreover, is unknown the role of this local production of melatonin and whether this production have a circadian pattern. Available information indicates that melatonin induces in placenta the expression of antioxidant enzymes catalase and superoxide dismutase, prevents the injury produced by oxidative stress, and inhibits the expression of vascular endothelial growth factor (VEGF) a gene that in other tissues is controlled by clock genes. In this review we aim to analyze available information regarding clock genes and clock genes controlled genes such as VEGF and the possible role of melatonin synthesis in the placenta.Obstetrics and Gynecology International 01/2015; 2015:825802. DOI:10.1155/2015/825802
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ABSTRACT: Homeostatic and adaptive control mechanisms are essential for keeping organisms structurally and functionally stable. Integral feedback is a control theoretic concept which has long been known to keep a controlled variable [Formula: see text] robustly (i.e. perturbation-independent) at a given set-point [Formula: see text] by feeding the integrated error back into the process that generates [Formula: see text]. The classical concept of homeostasis as robust regulation within narrow limits is often considered as unsatisfactory and even incompatible with many biological systems which show sustained oscillations, such as circadian rhythms and oscillatory calcium signaling. Nevertheless, there are many similarities between the biological processes which participate in oscillatory mechanisms and classical homeostatic (non-oscillatory) mechanisms. We have investigated whether biological oscillators can show robust homeostatic and adaptive behaviors, and this paper is an attempt to extend the homeostatic concept to include oscillatory conditions. Based on our previously published kinetic conditions on how to generate biochemical models with robust homeostasis we found two properties, which appear to be of general interest concerning oscillatory and homeostatic controlled biological systems. The first one is the ability of these oscillators ("oscillatory homeostats") to keep the average level of a controlled variable at a defined set-point by involving compensatory changes in frequency and/or amplitude. The second property is the ability to keep the period/frequency of the oscillator tuned within a certain well-defined range. In this paper we highlight mechanisms that lead to these two properties. The biological applications of these findings are discussed using three examples, the homeostatic aspects during oscillatory calcium and p53 signaling, and the involvement of circadian rhythms in homeostatic regulation.PLoS ONE 09/2014; 9(9):e107766. DOI:10.1371/journal.pone.0107766 · 3.53 Impact Factor
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ABSTRACT: In the literature on dynamical models in cognitive science, two issues have recently caused controversy. First, what is the relation between dynamical and mechanistic models? I will argue that dynamical models can be upgraded to be mechanistic as well, and that there are mechanistic and non-mechanistic dynamical models. Second, there is the issue of explanatory power. Since it is uncontested the mechanistic models can explain, I will focus on the non-mechanistic variety of dynamical models. It is often claimed by proponents of mechanistic explanations that such models do not really explain cognitive phenomena (the ‘mere description’ worry). I will argue against this view. Although I agree that the three arguments usually offered to vindicate the explanatory power of non-mechanistic dynamical models (predictive power, counterfactual support, and unification) are not enough, I consider a fourth argument, namely that such models provide understanding. The Voss strong anticipation model is used to illustrate this.Synthese 01/2015; 192(1). DOI:10.1007/s11229-014-0548-5 · 0.64 Impact Factor