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Gilbert, J. P.

Publications and source records attributed to Gilbert, J. P..

2 recordsLinked to original sources

RTP801 Is A Key Modulator For Motor Learning

RTP801/REDD1 is a stress-regulated protein whose upregulation is necessary and sufficient to trigger neuronal death in in vitro and in vivo models of Parkinsons and Huntingtons diseases and is up regulated in compromised neurons in human postmortem brains of both neurodegenerative disorders. Indeed, in both Parkinsons and Huntingtons disease mouse models, RTP801 knockdown alleviates motor-learning deficits. Here, we investigated the physiological role of RTP801 in neuronal plasticity. RTP801 is found in rat, mouse and human synapses. The absence of RTP801 enhanced excitatory synaptic transmission in both neuronal cultures and brain slices from RTP801 knock-out (KO) mice. Indeed, RTP801 KO mice showed improved motor learning, which correlated with lower spine density but increased basal filopodia and mushroom spines in the motor cortex layer V. This paralleled with higher levels of synaptosomal GluA1 and TrkB receptors in homogenates derived from KO mice motor cortex, proteins that are associated with synaptic strengthening. Altogether, these results indicate that RTP801 has an important role modulating neuronal plasticity in motor learning.

neuroscience↗

Gsmodutils: A python based framework for test-driven genome scale metabolic model development

MotivationGenome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for the continued management of curated large scale models. For example, when genome annotations are updated or new understanding regarding behaviour of is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build and test cycle.\n\nResultsAs part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimising error between model versions.\n\nAvailabilityThe software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils

bioinformatics↗