Seerup Lorenzen (airfind27)

For higher activity levels, or when homeostatic mechanisms are impaired, the homeostatic mechanisms fail in maintaining ion concentrations close to baseline, and the edPR model diverges from the PR model as it accounts for effects of concentration changes on neuronal firing. We envision that the edPR model will be useful for the field in three main ways. Firstly, as it relaxes commonly made modeling assumptions, the edPR model can be used to test the validity of these assumptions under various firing conditions, as we show here for a few selected cases. Secondly, the edPR model should supplement the PR model when simulating scenarios where ion concentrations are expected to vary over time. Thirdly, being applicable to conditions with failed homeostasis, the edPR model opens up for simulating a range of pathological conditions, such as spreading depression or epilepsy.Uncertainty in the structure and parameters of networks is ubiquitous across computational biology. In constraint-based reconstruction and analysis of metabolic networks, this uncertainty is present both during the reconstruction of networks and in simulations performed with them. Here, we present Medusa, a Python package for the generation and analysis of ensembles of genome-scale metabolic network reconstructions. Medusa builds on the COBRApy package for constraint-based reconstruction and analysis by compressing a set of models into a compact ensemble object, providing functions for the generation of ensembles using experimental data, and extending constraint-based analyses to ensemble scale. We demonstrate how Medusa can be used to generate ensembles and perform ensemble simulations, and how machine learning can be used in conjunction with Medusa to guide the curation of genome-scale metabolic network reconstructions. Medusa is available under the permissive MIT license from the Python Packaging Index (https//pypi.org) and from github (https//github.com/opencobra/Medusa), and comprehensive documentation is available at https//medusa.readthedocs.io/en/latest.Animals often exhibit dramatically behavioral plasticity depending on their internal physiological state, yet little is known about the underlying molecular mechanisms. The migratory locust, Locusta migratoria, provides an excellent model for addressing these questions because of their famous phase polyphenism involving remarkably behavioral plasticity between gregarious and solitarious phases. Here, we report that a major insect hormone, juvenile hormone, is involved in the regulation of this behavioral plasticity related to phase change by influencing the expression levels of olfactory-related genes in the migratory locust. We found that the treatment of juvenile hormone analog, methoprene, can significantly shift the olfactory responses of gregarious nymphs from attraction to repulsion to the volatiles released by gregarious nymphs. In contrast, the repulsion behavior of solitarious nymphs significantly decreased when they were treated with precocene or injected with double-stranded RNA of JHAMT, a juvenile hormone acid O-methyltransferase. Further, JH receptor Met or JH-response gene Kr-h1 knockdown phenocopied the JH-deprivation effects on olfactory behavior. RNA-seq analysis identified 122 differentially expressed genes in antennae after methoprene application on gregarious nymphs. Interestingly, several olfactory-related genes were especially enriched, including takeout (TO) and chemosensory protein (CSP) which have key roles in behavioral phase change of locusts. Furthermore, methoprene application and Met or Kr-h1 knockdown resulted in simultaneous changes of both TO1 and CSP3 expression to reverse pattern, which mediated the transition between repulsion and attraction responses to gregarious volatiles. Our results suggest the regulatory roles of a pleiotropic hormone in locust behavioral plasticity through modulating gene expression in the peripheral olfactory system.Plasmids, when transferred b