Modelling KNDy neurons and gonadotropin-releasing hormone pulse generation

This is the final version. Available on open access from Elsevier via the DOI in this record The pulsatile release of gonadotropin-releasing hormone (GnRH) and its frequency are crucial for healthy reproductive function. To understand what drives GnRH pulses a combination of experimental and mathematical modelling approaches have been used. Early work focussed on the possibility that GnRH pulse generation is an intrinsic feature of GnRH neurons, with autocrine feedback generating pulsatility. However, there is now ample evidence suggesting that a network of upstream KNDy (kisspeptin, neurokinin-B and dynorphin) neurons are the source of this GnRH pulse generator. The interplay of slow positive and negative feedback via neurokinin-B and dynorphin respectively allow the network to act as a relaxation oscillator, driving pulsatile secretion of kisspeptin, and consequently, of GnRH and LH. Here we review the mathematical modelling approaches exploring both scenarios and suggest that with pulsatile GnRH secretion driven by the KNDy pulse generator, autocrine feedback still has the potential to modulate GnRH output.Engineering and Physical Sciences Research Council (EPSRC)Biotechnology & Biological Sciences Research Council (BBSRC

Mathematical models in GnRH research

This is the final version. Available on open access from Wiley via the DOI in this recordMathematical modelling is an indispensable tool in modern biosciences, enabling quantitative analysis and integration of biological data, transparent formulation of our understanding of complex biological systems, and efficient experimental design based on model predictions. This review article provides an overview of the impact that mathematical models had on GnRH research. Indeed, over the last 20 years mathematical modelling has been used to describe and explore the physiology of the GnRH neuron, the mechanisms underlying GnRH pulsatile secretion, and GnRH signalling to the pituitary. Importantly, these models have contributed to GnRH research via novel hypotheses and predictions regarding the bursting behaviour of the GnRH neuron, the role of kisspeptin neurons in the emergence of pulsatile GnRH dynamics, and the decoding of GnRH signals by biochemical signalling networks. We envisage that with the advent of novel experimental technologies, mathematical modelling will have an even greater role to play in our endeavour to understand the complex spatiotemporal dynamics underlying the reproductive neuroendocrine system.Biotechnology & Biological Sciences Research Council (BBSRC)Kings College Londo

Direct functionalization of an optical fiber by a plasmonic nanosensor

International audienceWe explore a rapid route for fabricating silver nanoparticles (NPs) at the end of an optical fiber. The size and number of silver NPs can be controlled by varying the exposure doses. The effect of the refractive index of different solvents on the extinction spectra have been studied as a proof of concept of a fiber integrated plasmon-based sensor

Near-Field and Far-Field Sensitivities of LSPR Sensors

International audienceThe present study compares the near-field and far-field sensitivities of localized surface plasmon resonance (LSPR) sensors. To put into evidence the difference between far-field and near-field sensors, optical extinction measurements have been performed on gold nanoparticle gratings coated with dielectric superstrates of varying thicknesses. The potential of LSPR sensors is usually considered to lie in the near-field regime. Therefore, a comparison of the near-field sensitivities for gold nanoparticle gratings and continuous gold films of 50 nm in thickness is provided. The difference in refractive index sensitivities of both sensors is discussed in relation with the decay length of the evanescent near-field. SPRs sensors are usually considered more sensitive than LSPRs in terms of the m factor, refractive index sensitivity. We argue that the m factor sensitivity can only be defined for thick (15--100 nm) superstrates; for thin superstrates (d < 15 nm), the decay length of the evanescent field must be taken into account to properly compare both sensors

Enhancing LSPR sensitivity of Au gratings through graphene coupling to Au film

A particular interesting plasmonic system is that of metallic nanostructures interacting with metal films. As the localized surface plasmon resonance (LSPR) behavior of gold nanostructures (Au NPs) on the top of a gold thin film is exquisitely sensitive to the spacer distance of the film-Au NPs, we investigate in the present work the influence of a few-layered graphene spacer on the LSPR behavior of the NPs. The idea is to evidence the role of few-layered graphene as one of the thinnest possible spacer. We first show that the coupling to the Au film induces a strong lowering at around 507 nm and sharpening of the main LSPR of the Au NPs. Moreover, a blue shift in the main LSP resonance of about 13 nm is observed in the presence of a few-layered graphene spacer when compared to the case where gold nanostructures are directly linked to a gold thin film. Numerical simulations suggest that this LSP mode is dipolar and that the hot spots of the electric field are pushed to the top corners of the NPs, which makes it very sensitive to surrounding medium optical index changes and thus appealing for sensing applications. A figure of merit of such a system (gold/graphene/Au NPs) is 2.8, as compared to 2.1 for gold/Au NPs. This represents a 33 % gain in sensitivity and opens-up new sensing strategies

Enhanced gold film-coupled graphene-based plasmonic nanosensor

In this work, a trilayer graphene is used as a thin non dielectric spacer with a high index of refraction, between Au film and Au NPs. Encouraged by the sharpness of the localized surface plasmon resonance LSPR induced by this system, we performed sensitivity measurements to refractive index change in the surrounding medium of the sensor. The presence of graphene led to both higher sensitivity and sharper full width at half maximum FWHM and thus higher figure of merit FOM (2.8) compared to the system without graphene (2.1)