Serotonin as a biomarker of toxin-induced Parkinsonism.

BACKGROUND: Loss of dopaminergic neurons underlies the motor symptoms of Parkinson's disease (PD). However stereotypical PD symptoms only manifest after approximately 80% of dopamine neurons have died making dopamine-related motor phenotypes unreliable markers of the earlier stages of the disease. There are other non-motor symptoms, such as depression, that may present decades before motor symptoms. METHODS: Because serotonin is implicated in depression, here we use niche, fast electrochemistry paired with mathematical modelling and machine learning to, for the first time, robustly evaluate serotonin neurochemistry in vivo in real time in a toxicological model of Parkinsonism, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). RESULTS: Mice treated with acute MPTP had lower concentrations of in vivo, evoked and ambient serotonin in the hippocampus, consistent with the clinical comorbidity of depression with PD. These mice did not chemically respond to SSRI, as strongly as control animals did, following the clinical literature showing that antidepressant success during PD is highly variable. Following L-DOPA administration, using a novel machine learning analysis tool, we observed a dynamic shift from evoked serotonin release in the hippocampus to dopamine release. We hypothesize that this finding shows, in real time, that serotonergic neurons uptake L-DOPA and produce dopamine at the expense of serotonin, supporting the significant clinical correlation between L-DOPA and depression. Finally, we found that this post L-DOPA dopamine release was less regulated, staying in the synapse for longer. This finding is perhaps due to lack of autoreceptor control and may provide a ground from which to study L-DOPA induced dyskinesia. CONCLUSIONS: These results validate key prior hypotheses about the roles of serotonin during PD and open an avenue to study to potentially improve therapeutics for levodopa-induced dyskinesia and depression.

Inflammation-Induced Histamine Impairs the Capacity of Escitalopram to Increase Hippocampal Extracellular Serotonin.

Commonly prescribed selective serotonin reuptake inhibitors (SSRIs) inhibit the serotonin transporter to correct a presumed deficit in extracellular serotonin signaling during depression. These agents bring clinical relief to many who take them; however, a significant and growing number of individuals are resistant to SSRIs. There is emerging evidence that inflammation plays a significant role in the clinical variability of SSRIs, though how SSRIs and inflammation intersect with synaptic serotonin modulation remains unknown. In this work, we use fast in vivo serotonin measurement tools to investigate the nexus between serotonin, inflammation, and SSRIs. Upon acute systemic lipopolysaccharide (LPS) administration in male and female mice, we find robust decreases in extracellular serotonin in the mouse hippocampus. We show that these decreased serotonin levels are supported by increased histamine activity (because of inflammation), acting on inhibitory histamine H3 heteroreceptors on serotonin terminals. Importantly, under LPS-induced histamine increase, the ability of escitalopram to augment extracellular serotonin is impaired because of an off-target action of escitalopram to inhibit histamine reuptake. Finally, we show that a functional decrease in histamine synthesis boosts the ability of escitalopram to increase extracellular serotonin levels following LPS. This work reveals a profound effect of inflammation on brain chemistry, specifically the rapidity of inflammation-induced decreased extracellular serotonin, and points the spotlight at a potentially critical player in the pathology of depression, histamine. The serotonin/histamine homeostasis thus, may be a crucial new avenue in improving serotonin-based treatments for depression.SIGNIFICANCE STATEMENT Acute LPS-induced inflammation (1) increases CNS histamine, (2) decreases CNS serotonin (via inhibitory histamine receptors), and (3) prevents a selective serotonin reuptake inhibitor (SSRI) from effectively increasing extracellular serotonin. A targeted depletion of histamine recovers SSRI-induced increases in extracellular hippocampal serotonin.

Voltammetric Approach for Characterizing the Biophysical and Chemical Functionality of Human Induced Pluripotent Stem Cell-Derived Serotonin Neurons.

Depression is quickly becoming one of the world's most pressing public health crises, and there is an urgent need for better diagnostics and therapeutics. Behavioral models in animals and humans have not adequately addressed the diagnosis and treatment of depression, and biomarkers of mental illnesses remain ill-defined. It has been very difficult to identify biomarkers of depression because of in vivo measurement challenges. While our group has made important strides in developing in vivo tools to measure such biomarkers (e.g., serotonin) in mice using voltammetry, these tools cannot be easily applied for depression diagnosis and drug screening in humans due to the inaccessibility of the human brain. In this work, we take a chemical approach, ex vivo, to introduce a human-derived system to investigate brain serotonin. We utilize human induced pluripotent stem cells differentiated into serotonin neurons and establish a new ex vivo model of real-time serotonin neurotransmission measurements. We show that evoked serotonin release responds to stimulation intensity and tryptophan preloading, and that serotonin release and reuptake kinetics resemble those found in vivo in rodents. Finally, after selective serotonin reuptake inhibitor (SSRI) exposure, we find dose-dependent internalization of the serotonin reuptake transporters (a signature of the in vivo response to SSRI). Our new human-derived chemical model has great potential to provide an ex vivo chemical platform as a translational tool for in vivo neuropsychopharmacology.

An In Vivo Definition of Brain Histamine Dynamics Reveals Critical Neuromodulatory Roles for This Elusive Messenger.

Histamine is well known for mediating peripheral inflammation; however, this amine is also found in high concentrations in the brain where its roles are much less known. In vivo chemical dynamics are difficult to measure, thus fundamental aspects of histamine's neurochemistry remain undefined. In this work, we undertake the first in-depth characterization of real time in vivo histamine dynamics using fast electrochemical tools. We find that histamine release is sensitive to pharmacological manipulation at the level of synthesis, packaging, autoreceptors and metabolism. We find two breakthrough aspects of histamine modulation. First, differences in H3 receptor regulation between sexes show that histamine release in female mice is much more tightly regulated than in male mice under H3 or inflammatory drug challenge. We hypothesize that this finding may contribute to hormone-mediated neuroprotection mechanisms in female mice. Second, a high dose of a commonly available antihistamine, the H1 receptor inverse agonist diphenhydramine, rapidly decreases serotonin levels. This finding highlights the sheer significance of pharmaceuticals on neuromodulation. Our study opens the path to better understanding and treating histamine related disorders of the brain (such as neuroinflammation), emphasizing that sex and modulation (of serotonin) are critical factors to consider when studying/designing new histamine targeting therapeutics.

Fast serotonin voltammetry as a versatile tool for mapping dynamic tissue architecture: I. Responses at carbon fibers describe local tissue physiology.

It is important to monitor serotonin neurochemistry in the context of brain disorders. Specifically, a better understanding of biophysical alterations and associated biochemical functionality within subregions of the brain will enable better of understanding of diseases such as depression. Fast voltammetric tools at carbon fiber microelectrodes provide an opportunity to make direct evoked and ambient serotonin measurements in vivo in mice. In this study, we characterize novel stimulation and measurement circuitries for serotonin analyses in brain regions relevant to psychiatric disease. Evoked and ambient serotonin in these brain areas, the CA2 region of the hippocampus and the medial prefrontal cortex, are compared to ambient and evoked serotonin in the substantia nigra pars reticulata, an area well established previously for serotonin measurements with fast voltammetry. Stimulation of a common axonal location evoked serotonin in all three brain regions. Differences are observed in the serotonin release and reuptake profiles between these three brain areas which we hypothesize to arise from tissue physiology heterogeneity around the carbon fiber microelectrodes. We validate this hypothesis mathematically and via confocal imaging. We thereby show that fast voltammetric methods can provide accurate information about local physiology and highlight implications for chemical mapping. Cover Image for this issue: doi: 10.1111/jnc.14739.

Autoreceptor control of serotonin dynamics.

BACKGROUND: Serotonin is a neurotransmitter that has been linked to a wide variety of behaviors including feeding and body-weight regulation, social hierarchies, aggression and suicidality, obsessive compulsive disorder, alcoholism, anxiety, and affective disorders. Full understanding involves genomics, neurochemistry, electrophysiology, and behavior. The scientific issues are daunting but important for human health because of the use of selective serotonin reuptake inhibitors and other pharmacological agents to treat disorders. This paper presents a new deterministic model of serotonin metabolism and a new systems population model that takes into account the large variation in enzyme and transporter expression levels, tryptophan input, and autoreceptor function. RESULTS: We discuss the steady state of the model and the steady state distribution of extracellular serotonin under different hypotheses on the autoreceptors and we show the effect of tryptophan input on the steady state and the effect of meals. We use the deterministic model to interpret experimental data on the responses in the hippocampus of male and female mice, and to illustrate the short-time dynamics of the autoreceptors. We show there are likely two reuptake mechanisms for serotonin and that the autoreceptors have long-lasting influence and compare our results to measurements of serotonin dynamics in the substantia nigra pars reticulata. We also show how histamine affects serotonin dynamics. We examine experimental data that show very variable response curves in populations of mice and ask how much variation in parameters in the model is necessary to produce the observed variation in the data. Finally, we show how the systems population model can potentially be used to investigate specific biological and clinical questions. CONCLUSIONS: We have shown that our new models can be used to investigate the effects of tryptophan input and meals and the behavior of experimental response curves in different brain nuclei. The systems population model incorporates individual variation and can be used to investigate clinical questions and the variation in drug efficacy. The codes for both the deterministic model and the systems population model are available from the authors and can be used by other researchers to investigate the serotonergic system.

Modeling the long term effects of thermoregulation on human sleep.

The connection between human sleep and energy exertion has long been regarded as part of the reasoning for the need to sleep. A recent theory proposes that during REM sleep, energy utilized for thermoregulation is diverted to other relevant biological processes. We present a mathematical model of human sleep/wake regulation with thermoregulatory functions to gain quantitative insight into the effects of ambient temperature on sleep quality. Our model extends previous models by incorporating equations for the metabolic processes that control thermoregulation during sleep. We present numerical simulations that provide a quantitative answer for how humans adjust by changing the normal sleep stage progression when it is challenged with ambient temperatures away from thermoneutral. We explore the dynamics for a single night and several nights. Our results indicate that including the effects of temperature is a vital component of modeling sleep.

A mathematical model for histamine synthesis, release, and control in varicosities.

BACKGROUND: Histamine (HA), a small molecule that is synthesized from the amino acid histidine, plays an important role in the immune system where it is associated with allergies, inflammation, and T-cell regulation. In the brain, histamine is stored in mast cells and other non-neuronal cells and also acts as a neurotransmitter. The histamine neuron cell bodies are in the tuberomammillary (TM) nucleus of the hypothalamus and these neurons send projections throughout the central nervous system (CNS), in particular to the cerebral cortex, amygdala, basal ganglia, hippocampus, thalamus, retina, and spinal cord. HA neurons make few synapses, but release HA from the cell bodies and from varicosities when the neurons fire. Thus the HA neural system seems to modulate and control the HA concentration in projection regions. It is known that high HA levels in the extracellular space inhibit serotonin release, so HA may play a role in the etiology of depression. RESULTS: We compare model predictions to classical physiological experiments on HA half-life, the concentration of brain HA after histidine loading, and brain HA after histidine is dramatically increased or decreased in the diet. The model predictions are also consistent with in vivo experiments in which extracellular HA is measured, using Fast Scan Cyclic Voltammetry, in the premammillary nucleus (PM) after a 2 s antidromic stimulation of the TM, both without and in the presence of the H 3 autoreceptor antagonist thioperamide. We show that the model predicts well the temporal behavior of HA in the extracellular space over 30 s in both experiments. CONCLUSIONS: Our ability to measure in vivo histamine dynamics in the extracellular space after stimulation presents a real opportunity to understand brain function and control. The observed extracellular dynamics depends on synthesis, storage, neuronal firing, release, reuptake, glial cells, and control by autoreceptors, as well as the behavioral state of the animal (for example, depression) or the presence of neuroinflammation. In this complicated situation, the mathematical model will be useful for interpreting data and conducting in silico experiments to understand causal mechanisms. And, better understanding can suggest new therapeutic drug targets.

Analysis of Homeostatic Mechanisms in Biochemical Networks.

Cell metabolism is an extremely complicated dynamical system that maintains important cellular functions despite large changes in inputs. This "homeostasis" does not mean that the dynamical system is rigid and fixed. Typically, large changes in external variables cause large changes in some internal variables so that, through various regulatory mechanisms, certain other internal variables (concentrations or velocities) remain approximately constant over a finite range of inputs. Outside that range, the mechanisms cease to function and concentrations change rapidly with changes in inputs. In this paper we analyze four different common biochemical homeostatic mechanisms: feedforward excitation, feedback inhibition, kinetic homeostasis, and parallel inhibition. We show that all four mechanisms can occur in a single biological network, using folate and methionine metabolism as an example. Golubitsky and Stewart have proposed a method to find homeostatic nodes in networks. We show that their method works for two of these mechanisms but not the other two. We discuss the many interesting mathematical and biological questions that emerge from this analysis, and we explain why understanding homeostatic control is crucial for precision medicine.

A voltammetric and mathematical analysis of histaminergic modulation of serotonin in the mouse hypothalamus.

Histamine and serotonin are neuromodulators which facilitate numerous, diverse neurological functions. Being co-localized in many brain regions, these two neurotransmitters are thought to modulate one another's chemistry and are often implicated in the etiology of disease. Thus, it is desirable to interpret the in vivo chemistry underlying neurotransmission of these two molecules to better define their roles in health and disease. In this work, we describe a voltammetric approach to monitoring serotonin and histamine simultaneously in real time. Via electrical stimulation of the axonal bundles in the medial forebrain bundle, histamine release was evoked in the mouse premammillary nucleus. We found that histamine release was accompanied by a rapid, potent inhibition of serotonin in a concentration-dependent manner. We developed mathematical models to capture the experimental time courses of histamine and serotonin, which necessitated incorporation of an inhibitory receptor on serotonin neurons. We employed pharmacological experiments to verify that this serotonin inhibition was mediated by H3 receptors. Our novel approach provides fundamental mechanistic insights that can be used to examine the full extent of interconnectivity between histamine and serotonin in the brain. Histamine and serotonin are co-implicated in many of the brain's functions. In this paper, we develop a novel voltammetric method for simultaneous real-time monitoring of histamine and serotonin in the mouse premammillary nucleus. Electrical stimulation of the medial forebrain bundle evokes histamine and inhibits serotonin release. We show voltammetrically, mathematically, and pharmacologically that this serotonin inhibition is H3 receptor mediated.

Using mathematical models to understand metabolism, genes, and disease.

Mathematical models are a useful tool for investigating a large number of questions in metabolism, genetics, and gene-environment interactions. A model based on the underlying biology and biochemistry is a platform for in silico biological experimentation that can reveal the causal chain of events that connect variation in one quantity to variation in another. We discuss how we construct such models, how we have used them to investigate homeostatic mechanisms, gene-environment interactions, and genotype-phenotype mapping, and how they can be used in precision and personalized medicine.

Voltammetric and mathematical evidence for dual transport mediation of serotonin clearance in vivo.

The neurotransmitter serotonin underlies many of the brain's functions. Understanding serotonin neurochemistry is important for improving treatments for neuropsychiatric disorders such as depression. Antidepressants commonly target serotonin clearance via serotonin transporters and have variable clinical effects. Adjunctive therapies, targeting other systems including serotonin autoreceptors, also vary clinically and carry adverse consequences. Fast scan cyclic voltammetry is particularly well suited for studying antidepressant effects on serotonin clearance and autoreceptors by providing real-time chemical information on serotonin kinetics in vivo. However, the complex nature of in vivo serotonin responses makes it difficult to interpret experimental data with established kinetic models. Here, we electrically stimulated the mouse medial forebrain bundle to provoke and detect terminal serotonin in the substantia nigra reticulata. In response to medial forebrain bundle stimulation we found three dynamically distinct serotonin signals. To interpret these signals we developed a computational model that supports two independent serotonin reuptake mechanisms (high affinity, low efficiency reuptake mechanism, and low affinity, high efficiency reuptake system) and bolsters an important inhibitory role for the serotonin autoreceptors. Our data and analysis, afforded by the powerful combination of voltammetric and theoretical methods, gives new understanding of the chemical heterogeneity of serotonin dynamics in the brain. This diverse serotonergic matrix likely contributes to clinical variability of antidepressants.

Modulation of serotonin transporter expression by escitalopram under inflammation.

Selective serotonin reuptake inhibitors (SSRIs) are widely used for depression based on the monoamine deficiency hypothesis. However, the clinical use of these agents is controversial, in part because of their variable clinical efficacy and in part because of their delayed onset of action. Because of the complexities involved in replicating human disease and clinical dosing in animal models, the scientific community has not reached a consensus on the reasons for these phenomena. In this work, we create a theoretical hippocampal model incorporating escitalopram's pharmacokinetics, pharmacodynamics (competitive and non-competitive inhibition, and serotonin transporter (SERT) internalization), inflammation, and receptor dynamics. With this model, we simulate chronic oral escitalopram in mice showing that days to weeks are needed for serotonin levels to reach steady-state. We show escitalopram's chemical efficacy is diminished under inflammation. Our model thus offers mechanisms for how chronic escitalopram affects brain serotonin, emphasizing the importance of optimized dose and time for future antidepressant discoveries.

Frequency separation by an excitatory-inhibitory network.

We consider a situation in which individual features of the input are represented in the neural system by different frequencies of periodic firings. Thus, if two of the features are presented concurrently, the input to the system will consist of a superposition of two periodic trains. In this paper we present an algorithm that is capable of extracting the individual features from the composite signal by separating the signal into periodic spike trains with different frequencies. We show that the algorithm can be implemented in a biophysically based excitatory-inhibitory network model. The frequency separation process works over a range of frequencies determined by time constants of the model's intrinsic variables. It does not rely on a "resonance" phenomenon and is not tuned to a discrete set of frequencies. The frequency separation is still reliable when the timing of incoming spikes is noisy.

Dynamical questions in volume transmission.

In volume transmission (or neuromodulation) neurons do not make one-to-one connections to other neurons, but instead simply release neurotransmitter into the extracellular space from numerous varicosities. Many well-known neurotransmitters including serotonin (5HT), dopamine (DA), histamine (HA), Gamma-Aminobutyric Acid (GABA) and acetylcholine (ACh) participate in volume transmission. Typically, the cell bodies are in one volume and the axons project to a distant volume in the brain releasing the neurotransmitter there. We introduce volume transmission and describe mathematically two natural homeostatic mechanisms. In some brain regions several neurotransmitters in the extracellular space affect each other's release. We investigate the dynamics created by this comodulation in two different cases: serotonin and histamine; and the comodulation of 4 neurotransmitters in the striatum and we compare to experimental data. This kind of comodulation poses new dynamical questions as well as the question of how these biochemical networks influence the electrophysiological networks in the brain.

Serotonin is a Common Thread Linking Different Classes of Antidepressants.

Depression pathology remains elusive. The monoamine hypothesis has placed much focus on serotonin, but due to the variable clinical efficacy of monoamine reuptake inhibitors, the community is looking for alternative therapies such as ketamine (synaptic plasticity and neurogenesis theory of antidepressant action). There is evidence that different classes of antidepressants may affect serotonin levels; a notion we test here. We measure hippocampal serotonin in mice with voltammetry and study the effects of acute challenges of antidepressants. We find that pseudo-equivalent doses of these drugs similarly raise ambient serotonin levels, despite their differing pharmacodynamics because of differences in Uptake 1 and 2, rapid SERT trafficking and modulation of serotonin by histamine. These antidepressants have different pharmacodynamics but have strikingly similar effects on extracellular serotonin. Our findings suggest that serotonin is a common thread that links clinically effective antidepressants, synergizing different theories of depression (synaptic plasticity, neurogenesis and the monoamine hypothesis).

Serotonin is a common thread linking different classes of antidepressants.

Depression pathology remains elusive. The monoamine hypothesis has placed much focus on serotonin, but due to the variable clinical efficacy of monoamine reuptake inhibitors, the community is looking for alternative therapies such as ketamine (neurogenesis theory of antidepressant action). There is evidence that different classes of antidepressants may affect serotonin levels; a notion we test here. We measure hippocampal serotonin in mice with voltammetry and study the effects of acute challenges of escitalopram, fluoxetine, reboxetine, and ketamine. We find that pseudo-equivalent doses of these drugs similarly raise ambient serotonin levels, despite their differing pharmacodynamics because of differences in Uptake 1 and 2, rapid SERT trafficking, and modulation of serotonin by histamine. These antidepressants have different pharmacodynamics but have strikingly similar effects on extracellular serotonin. Our findings suggest that serotonin is a common thread that links clinically effective antidepressants, synergizing different theories of depression (synaptic plasticity, neurogenesis, and the monoamine hypothesis).

Hormone secretion in transgenic rats and electrophysiological activity in their gonadotropin releasing-hormone neurons.

Expression of GFP in GnRH neurons has allowed for studies of individual GnRH neurons. We have demonstrated previously the preservation of physiological function in male GnRH-GFP mice. In the present study, we confirm using biocytin-filled GFP-positive neurons in the hypothalamic slice preparation that GFP-expressing somata, axons, and dendrites in hypothalamic slices from GnRH-GFP rats are GnRH1 peptide positive. Second, we used repetitive sampling to study hormone secretion from GnRH-GFP transgenic rats in the homozygous, heterozygous, and wild-type state and between transgenic and Wistar males after ~4 yr of backcrossing. Parameters of hormone secretion were not different between the three genetic groups or between transgenic males and Wistar controls. Finally, we performed long-term recording in as many GFP-identified GnRH neurons as possible in hypothalamic slices to determine their patterns of discharge. In some cases, we obtained GnRH neuronal recordings from individual males in which blood samples had been collected the previous day. Activity in individual GnRH neurons was expressed as total quiescence, a continuous pattern of firing of either low or relatively high frequencies or an intermittent pattern of firing. In males with both intensive blood sampling (at 6-min intervals) and recordings from their GnRH neurons, we analyzed the activity of GnRH neurons with intermittent activity above 2 Hz using cluster analysis on both data sets. The average number of pulses was 3.9 ± 0.6/h. The average number of episodes of firing was 4.0 ± 0.6/h. Therefore, the GnRH pulse generator may be maintained in the sagittal hypothalamic slice preparation.

Serotonin synthesis, release and reuptake in terminals: a mathematical model.

BACKGROUND: Serotonin is a neurotransmitter that has been linked to a wide variety of behaviors including feeding and body-weight regulation, social hierarchies, aggression and suicidality, obsessive compulsive disorder, alcoholism, anxiety, and affective disorders. Full understanding of serotonergic systems in the central nervous system involves genomics, neurochemistry, electrophysiology, and behavior. Though associations have been found between functions at these different levels, in most cases the causal mechanisms are unknown. The scientific issues are daunting but important for human health because of the use of selective serotonin reuptake inhibitors and other pharmacological agents to treat disorders in the serotonergic signaling system. METHODS: We construct a mathematical model of serotonin synthesis, release, and reuptake in a single serotonergic neuron terminal. The model includes the effects of autoreceptors, the transport of tryptophan into the terminal, and the metabolism of serotonin, as well as the dependence of release on the firing rate. The model is based on real physiology determined experimentally and is compared to experimental data. RESULTS: We compare the variations in serotonin and dopamine synthesis due to meals and find that dopamine synthesis is insensitive to the availability of tyrosine but serotonin synthesis is sensitive to the availability of tryptophan. We conduct in silico experiments on the clearance of extracellular serotonin, normally and in the presence of fluoxetine, and compare to experimental data. We study the effects of various polymorphisms in the genes for the serotonin transporter and for tryptophan hydroxylase on synthesis, release, and reuptake. We find that, because of the homeostatic feedback mechanisms of the autoreceptors, the polymorphisms have smaller effects than one expects. We compute the expected steady concentrations of serotonin transporter knockout mice and compare to experimental data. Finally, we study how the properties of the the serotonin transporter and the autoreceptors give rise to the time courses of extracellular serotonin in various projection regions after a dose of fluoxetine. CONCLUSIONS: Serotonergic systems must respond robustly to important biological signals, while at the same time maintaining homeostasis in the face of normal biological fluctuations in inputs, expression levels, and firing rates. This is accomplished through the cooperative effect of many different homeostatic mechanisms including special properties of the serotonin transporters and the serotonin autoreceptors. Many difficult questions remain in order to fully understand how serotonin biochemistry affects serotonin electrophysiology and vice versa, and how both are changed in the presence of selective serotonin reuptake inhibitors. Mathematical models are useful tools for investigating some of these questions.

A mathematical model of the sleep/wake cycle.

We present a biologically-based mathematical model that accounts for several features of the human sleep/wake cycle. These features include the timing of sleep and wakefulness under normal and sleep-deprived conditions, ultradian rhythms, more frequent switching between sleep and wakefulness due to the loss of orexin and the circadian dependence of several sleep measures. The model demonstrates how these features depend on interactions between a circadian pacemaker and a sleep homeostat and provides a biological basis for the two-process model for sleep regulation. The model is based on previous "flip-flop" conceptual models for sleep/wake and REM/NREM and we explore whether the neuronal components in these flip-flop models, with the inclusion of a sleep-homeostatic process and the circadian pacemaker, are sufficient to account for the features of the sleep/wake cycle listed above. The model is minimal in the sense that, besides the sleep homeostat and constant cortical drives, the model includes only those nuclei described in the flip-flop models. Each of the cell groups is modeled by at most two differential equations for the evolution of the total population activity, and the synaptic connections are consistent with those described in the flip-flop models. A detailed analysis of the model leads to an understanding of the mathematical mechanisms, as well as insights into the biological mechanisms, underlying sleep/wake dynamics.