Inhibitory and excitatory networks balance cell coupling in the suprachiasmatic nucleus: A modeling approach

Kingsbury, Nathaniel J.; Taylor, Steven S.; Henson, Michael A.

doi:10.1016/j.jtbi.2016.02.039

Cited by 11 publications

(14 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The effect of TTX on the SCN was studied using previously developed mathematical models of the SCN ( Vasalou and Henson, 2010, 2011 ; Kingsbury et al, 2016 ). All simulations were performed in Matlab.…”

Section: Methodsmentioning

confidence: 99%

“…To generate heterogeneity within the network population, we randomly distributed the parameters determining the baseline rates of Per mRNA transcription ( v sP0 ; 0.94 ± 0.06), Bmal1 mRNA transcription ( v sB ; 1.00 ± 0.01), and Bmal1 mRNA degradation ( v mB ; 0.80 ± 0.01), as described previously ( Vasalou et al, 2009 ), as well as a parameter scaling the entire ODE governing [Ca 2+ ] i (distributed evenly between 0 and 1). All other parameter values were preserved from previous modeling studies ( Vasalou and Henson, 2010 ; Vasalou et al, 2011 ; Kingsbury et al, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Calcium Circadian Rhythmicity in the Suprachiasmatic Nucleus: Cell Autonomy and Network Modulation

Noguchi

Leise

Kingsbury

et al. 2017

eNeuro

Self Cite

View full text Add to dashboard Cite

Circadian rhythms of mammalian physiology and behavior are coordinated by the suprachiasmatic nucleus (SCN) in the hypothalamus. Within SCN neurons, various aspects of cell physiology exhibit circadian oscillations, including circadian clock gene expression, levels of intracellular Ca2+ ([Ca2+]i), and neuronal firing rate. [Ca2+]i oscillates in SCN neurons even in the absence of neuronal firing. To determine the causal relationship between circadian clock gene expression and [Ca2+]i rhythms in the SCN, as well as the SCN neuronal network dependence of [Ca2+]i rhythms, we introduced GCaMP3, a genetically encoded fluorescent Ca2+ indicator, into SCN neurons from PER2::LUC knock-in reporter mice. Then, PER2 and [Ca2+]i were imaged in SCN dispersed and organotypic slice cultures. In dispersed cells, PER2 and [Ca2+]i both exhibited cell autonomous circadian rhythms, but [Ca2+]i rhythms were typically weaker than PER2 rhythms. This result matches the predictions of a detailed mathematical model in which clock gene rhythms drive [Ca2+]i rhythms. As predicted by the model, PER2 and [Ca2+]i rhythms were both stronger in SCN slices than in dispersed cells and were weakened by blocking neuronal firing in slices but not in dispersed cells. The phase relationship between [Ca2+]i and PER2 rhythms was more variable in cells within slices than in dispersed cells. Both PER2 and [Ca2+]i rhythms were abolished in SCN cells deficient in the essential clock gene Bmal1. These results suggest that the circadian rhythm of [Ca2+]i in SCN neurons is cell autonomous and dependent on clock gene rhythms, but reinforced and modulated by a synchronized SCN neuronal network.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Calcium Circadian Rhythmicity in the Suprachiasmatic Nucleus: Cell Autonomy and Network Modulation

Noguchi

Leise

Kingsbury

et al. 2017

eNeuro

Self Cite

View full text Add to dashboard Cite

show abstract

“…Variants of the Kronauer model are still being used to explain properties of jet-lag and to design optimal schedules for fast re-entrainment following transmeridian travel (Serkh & Forger, 2014;Diekman & Bose, 2017). The process of re-entrainment has also been studied in more detailed models of the SCN network (Kingsbury et al, 2016), and hierarchical systems with internal desynchrony between the SCN and clocks in peripheral organs (Leise & Siegelmann, 2006). An area requiring further work in the context of re-entrainment is the incorporation of homeostatic sleep drive and the gating of light input due to sleep (Booth et al, 2017;Skeldon et al, 2017).…”

Section: Modelling Section 2: Mathematical Modelling Of Circadian Entmentioning

confidence: 99%

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

Belle

Diekman

2018

Eur J of Neuroscience

View full text Add to dashboard Cite

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.

show abstract

“…Recently, the role of another important coupling signal GABA [64,144] has been investigated with both classes of models [64,65,[145][146][147].…”

Section: The Synchronized Periods Of Coupled Oscillatorsmentioning

confidence: 99%

Protein sequestration versus Hill‐type repression in circadian clock models

Kim¹

2016

IET syst. biol.

107

View full text Add to dashboard Cite

Circadian (~24hr) clocks are self-sustained endogenous oscillators with which organisms keep track of daily and seasonal time. Circadian clocks frequently rely on interlocked transcriptionaltranslational feedback loops to generate rhythms that are robust against intrinsic and extrinsic perturbations. To investigate the dynamics and mechanisms of the intracellular feedback loops in circadian clocks, a number of mathematical models have been developed. The majority of the models use Hill functions to describe transcriptional repression in a way that is similar to the Goodwin model. Recently, a new class of models with protein sequestration-based repression has been introduced. Here, we discuss how this new class of models differs dramatically from those based on Hill-type repression in several fundamental aspects: conditions for rhythm generation, robust network designs and the periods of coupled oscillators. Consistently, these fundamental properties of circadian clocks also differ among Neurospora, Drosophila, and mammals depending on their key transcriptional repression mechanisms (Hill-type repression or protein sequestration). Based on both theoretical and experimental studies, this review highlights the importance of careful modeling of transcriptional repression mechanisms in molecular circadian clocks.

show abstract

Inhibitory and excitatory networks balance cell coupling in the suprachiasmatic nucleus: A modeling approach

Cited by 11 publications

References 66 publications

Calcium Circadian Rhythmicity in the Suprachiasmatic Nucleus: Cell Autonomy and Network Modulation

Calcium Circadian Rhythmicity in the Suprachiasmatic Nucleus: Cell Autonomy and Network Modulation

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

Protein sequestration versus Hill‐type repression in circadian clock models

Contact Info

Product

Resources

About