Mammalian Rest/Activity Patterns Explained by Physiologically Based Modeling

Circadian rhythms are fundamental to life. In mammals, these rhythms are generated by pacemaker neurons in the suprachiasmatic nucleus (SCN) of the hypothalamus. The SCN is remarkably consistent in structure and function between species, yet mammalian rest/activity patterns are extremely diverse, including diurnal, nocturnal, and crepuscular behaviors. Two mechanisms have been proposed to account for this diversity: (i) modulation of SCN output by downstream nuclei, and (ii) direct effects of light on activity. These two mechanisms are difficult to disentangle experimentally and their respective roles remain unknown. To address this, we developed a computational model to simulate the two mechanisms and their influence on temporal niche. In our model, SCN output is relayed via the subparaventricular zone (SPZ) to the dorsomedial hypothalamus (DMH), and thence to ventrolateral preoptic nuclei (VLPO) and lateral hypothalamus (LHA). Using this model, we generated rich phenotypes that closely resemble experimental data. Modulation of SCN output at the SPZ was found to generate a full spectrum of diurnal-to-nocturnal phenotypes. Intriguingly, we also uncovered a novel mechanism for crepuscular behavior: if DMH/VLPO and DMH/LHA projections act cooperatively, daily activity is unimodal, but if they act competitively, activity can become bimodal. In addition, we successfully reproduced diurnal/nocturnal switching in the rodent Octodon degu using coordinated inversions in both masking and circadian modulation. Finally, the model correctly predicted the SCN lesion phenotype in squirrel monkeys: loss of circadian rhythmicity and emergence of ∼4-h sleep/wake cycles. In capturing these diverse phenotypes, the model provides a powerful new framework for understanding rest/activity patterns and relating them to underlying physiology. Given the ubiquitous effects of temporal organization on all aspects of animal behavior and physiology, this study sheds light on the physiological changes required to orchestrate adaptation to various temporal niches

Neuronal oscillations on an ultra-slow timescale: daily rhythms in electrical activity and gene expression in the mammalian master circadian clockwork

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Neuronal oscillations of the brain, such as those observed in the cortices and hippocampi of behaving animals and humans, span across wide frequency bands, from slow delta waves (0.1 Hz) to ultra-fast ripples (600 Hz). Here, we focus on ultra-slow neuronal oscillators in the hypothalamic suprachiasmatic nuclei (SCN), the master daily clock that operates on interlocking transcription-translation feedback loops to produce circadian rhythms in clock gene expression with a period of near 24 h (< 0.001 Hz). This intracellular molecular clock interacts with the cell's membrane through poorly understood mechanisms to drive the daily pattern in the electrical excitability of SCN neurons, exhibiting an up-state during the day and a down-state at night. In turn, the membrane activity feeds back to regulate the oscillatory activity of clock gene programs. In this review, we emphasise the circadian processes that drive daily electrical oscillations in SCN neurons, and highlight how mathematical modelling contributes to our increasing understanding of circadian rhythm generation, synchronisation and communication within this hypothalamic region and across other brain circuits.M.D.C.B is supported by the University ofExeter Medical School (UEMS). C.O.D’s work was partially supported bythe National Science Foundation under grant nos. DMS-1412877 and DMS-155237, and the U.S. Army Research Laboratory and the U.S. ArmyResearch Office under Grant No. W911NF-16-1-0584

Developing a tool to characterize the ultradian rhythm in diploid Saccharomyces cervisiae using the reporter gene green fluorescent protein

Biological rhythms control many temporal behaviors of organism, such as the sleep cycle, hearts rhythms, seasonal animal migrations etc. Understanding these rhythms would provide insight into the temporal process of living organisms. Saccharomyces cerevisiae, a budding yeast, is an ideal model organism to study biological rhythms in eukaryotic cells because of its sequenced genome and discerned processes. By characterizing the biological rhythm in budding yeast, insight can be gained into more complex organisms. Previous studies have exhibited oscillatory behavior of oxygen consumption and determined that deletion of the GTS1 gene dissipates this rhythm. However, to further understand the specific behavior of this gene, GTS1 needs to be simultaneous monitored as it is expressed. In this study to monitor this ultradian rhythm regulating gene, a promoter-reporter construct was inserted through homologous recombination to track the expression of GTS1 in a diploid yeast strain, BY 4743. The promoter-reporter construct replaced one copy of the GTS1. As the GTS1 was expressed, the construct was expressed and detected by its reporter gene, green fluorescent protein (GFP). Synchronization of the cell cycle and ultradian rhythm was achieved by addition of hydroxyurea and nocodazole to the growth media. GFP levels were quantified by flow cytometry, with samples taken every 10 minutes. The results showed GFP expression level from the transformed yeast strain exhibiting a 3.33-fold increase relative to the non-transformed yeast strain. GFP expression yielded a biological rhythm with two identifiable periods, each with a 70 minute period. The first oscillation began at time zero and had a GFP expression maximum of 2.96 times the control level and a minimum of 2.62. The second oscillation began at 70 minutes had a GFP expression maximum of 3.09 times the control and a minimum of 2.76. The biological rhythm observed was shorter than its own cell cycle, roughly 111 minutes. Oscillatory behavior was observed as long as the culture remained synchronous. This study characterized the behavior of GTS1, an ultradian rhythm gene. By characterizing the behavior of this gene in S. cerevisiae, homologous genes in more complex organisms such as rodents or humans can be better understood. By extrapolating temporal behavior in yeast to humans, a cost effective drug prescreening can be implemented to evaluate possible biological rhythmic changes

Circuit-based interrogation of sleep control.

Sleep is a fundamental biological process observed widely in the animal kingdom, but the neural circuits generating sleep remain poorly understood. Understanding the brain mechanisms controlling sleep requires the identification of key neurons in the control circuits and mapping of their synaptic connections. Technical innovations over the past decade have greatly facilitated dissection of the sleep circuits. This has set the stage for understanding how a variety of environmental and physiological factors influence sleep. The ability to initiate and terminate sleep on command will also help us to elucidate its functions within and beyond the brain

Simulation of activity rhythms in the hamster

In this research, an effort was made to better understand light-dark cycles which influence the physiological and psychological rhythms, circadian rhythms and their behavior. The research concentrated on mathematically modeling circadian rhythms (specifically hamsters\u27 circadian activity rhythms) and establishing a physical correlation and creating a meaningful relationship between the mathematical model\u27s parameters and the real biological oscillators which are responsible for these rhythms. The internal nature of the circadian rhythms is unclear. There is insufficient information and empirical data that describe them. Indirect means have to be employed for the description and exploration of such rhythms. Extensive real data analysis must be performed in the time and frequency domains and every possible aspect of the circadian rhythms should be investigated. In our research, we studied and analyzed two types of circadian data, temperature and activity, which were extracted from rhesus monkeys and hamsters respectively by means of two separate data acquisition systems. The analysis which was accomplished in both the time and frequency domains revealed many important aspects of the circadian system and its characteristics. It was found that the circadian rhythms\u27 period is approximately 24 hours. This period showed a small deviation when the animal was subjected to different environmental conditions (light, food, etc.). The frequency spectrum of the real circadian data showed its harmonics structure and revealed the existence of two distinct frequency components (bimodality). Based on our analysis results and the knowledge of previous researchers work, we developed a nonlinear two coupled-oscillator mathematical model to approach the real circadian data. The numerical solutions of the model were obtained by computer simulation and were compared to the real circadian data in both the time and frequency domains. Certain modifications of the model were necessary to achieve the desired outcome. These modifications not only included the changes in the value of the model\u27s parameters, but also the addition of a high frequency oscillator. Later in the research, a periodic external stimulus was applied to the model in order to simulate entrainment. Our research has proven the ability of our mathematical model to simulate the circadian system

A Fast-Slow Analysis of the Dynamics of REM Sleep

Waking and sleep states are regulated by the coordinated activity of a number of neuronal population in the brainstem and hypothalamus whose synaptic interactions compose a sleep-wake regulatory network. Physiologically based mathematical models of the sleep-wake regulatory network contain mechanisms operating on multiple time scales including relatively fast synaptic-based interations between neuronal populations, and much slower homeostatic and circadian processes that modulate sleep-wake temporal patterning. In this study, we exploit the naturally arising slow time scale of the homeostatic sleep drive in a reduced sleep-wake regulatory network model to utilize fast-slow analysis to investigate the dynamics of rapid eye movement (REM) sleep regulation. The network model consists of a reduced number of wake-, non-REM (NREM) sleep-, and REM sleep-promoting neuronal populations with the synaptic interactions reflecting the mutually inhibitory flip-flop conceptual model for sleep-wake regulation and the reciprocal interaction model for REM sleep regulation. Network dynamics regularly alternate between wake and sleep states as goverend by the slow homeostatic sleep drive. By varying a parameter associated with the activation of the REM-promoting population, we cause REM dynamics during sleep episodes to vary from supression to single activations to regular REM-NREM cycling, corresponding to changes in REM patterning induced by circadian modulation and observed in different mammalian species. We also utilize fast-slow analysis to explain complex effects on sleep-wake patterning of simulated experiments in which agonists and antagonists of different neurotransmitters are microinjected into specific neuronal populations participating in the sleep-wake regulatory network

The two-process model of sleep regulation: a reappraisal

In the last three decades the two-process model of sleep regulation has served as a major conceptual framework in sleep research. It has been applied widely in studies on fatigue and performance and to dissect individual differences in sleep regulation. The model posits that a homeostatic process (Process S) interacts with a process controlled by the circadian pacemaker (Process C), with time-courses derived from physiological and behavioural variables. The model simulates successfully the timing and intensity of sleep in diverse experimental protocols. Electrophysiological recordings from the suprachiasmatic nuclei (SCN) suggest that S and C interact continuously. Oscillators outside the SCN that are linked to energy metabolism are evident in SCN-lesioned arrhythmic animals subjected to restricted feeding or methamphetamine administration, as well as in human subjects during internal desynchronization. In intact animals these peripheral oscillators may dissociate from the central pacemaker rhythm. A sleep/fast and wake/feed phase segregate antagonistic anabolic and catabolic metabolic processes in peripheral tissues. A deficiency of Process S was proposed to account for both depressive sleep disturbances and the antidepressant effect of sleep deprivation. The model supported the development of novel non-pharmacological treatment paradigms in psychiatry, based on manipulating circadian phase, sleep and light exposure. In conclusion, the model remains conceptually useful for promoting the integration of sleep and circadian rhythm research. Sleep appears to have not only a short-term, use-dependent function; it also serves to enforce rest and fasting, thereby supporting the optimization of metabolic processes at the appropriate phase of the 24-h cycle

Non-equilibrium critical dynamics of bursts in θ and δ rhythms as fundamental characteristic of sleep and wake micro-architecture

Origin and functions of intermittent transitions among sleep stages, including short awakenings and arousals, constitute a challenge to the current homeostatic framework for sleep regulation, focusing on factors modulating sleep over large time scales. Here we propose that the complex micro-architecture characterizing the sleep-wake cycle results from an underlying non-equilibrium critical dynamics, bridging collective behaviors across spatio-temporal scales. We investigate θ and δ wave dynamics in control rats and in rats with lesions of sleep-promoting neurons in the parafacial zone. We demonstrate that intermittent bursts in θ and δ rhythms exhibit a complex temporal organization, with long-range power-law correlations and a robust duality of power law (θ-bursts, active phase) and exponential-like (δ-bursts, quiescent phase) duration distributions, typical features of non-equilibrium systems self-organizing at criticality. Crucially, such temporal organization relates to anti-correlated coupling between θ- and δ-bursts, and is independent of the dominant physiologic state and lesions, a solid indication of a basic principle in sleep dynamics