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ANC Workshop: Ian Simpson and Daniel Trejo Banos, Chair: Colin Buchanan

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  • ANC Workshop Talk
When Apr 08, 2014
from 11:00 AM to 12:00 PM
Where IF 4.31/4.33
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Ian Simpson

Learning to taste

Learning about novel tastes is necessary for animal survival and wellbeing. New taste memories are consolidated to form long term memories which are dependent on modulation of translation regulation in the gustatory cortex (GC) hours following acquisition. However, the role of transcription regulation in the process is unknown. In this talk I will briefly present evidence that transcription in the GC is necessary for taste learning and that the act of drinking as well as the novel taste experience affects transcription in the GC during taste memory consolidation. I will show that the expression of a specific set of genes in the GC, including Arc/Arg3.1 and Npas4, known to regulate the homeostasis of excitatory and inhibitory synapses, respectively are differentially expressed during the learning process. Interestingly, there is a correlation between Arc/Arg3.1 and Npas4 mRNA expression in the GC following familiar, but not novel taste learning. These results demonstrate that in taste learning, transcription programs depend on physiological (i.e., fluid consumption following a water restriction regime) and specific information about a given taste (i.e., taste novelty) working in concert to allow novel taste memory consolidation. Moreover, I will discuss the possibility that the differential and prolonged kinetics of mRNA turnover taking place in the cortex following novel versus familiar taste learning may imply new mechanisms underlying memory consolidation.


 Daniel Trejo Banos

Structure learning of the Arabidopsis Thaliana circadian clock network

The circadian clock is a biological process in charge of synchronizing many biological functions. Its main characteristic is a period of approximately 24 hours. The discovery of the clock's components and their relationship is a topic of great interest in biological modelling. With this in mind, a hierarchical Bayesian model, with sparsity constrains  for network reconstruction is presented; using gene expression data and promoter sequence similarity scores as data sources.