Research

zebra finches

My interests range from modeling microbial populations to learning and information processing in nervous systems.

Two-stage learning of birdsong

We are working on a spiking-neuron model of song learning in zebra finches that emphasizes the two-stage nature of this process. This is based on empirical evidence that shows that the brain area LMAN learns a corrective bias that is later copied onto the pre-motor area RA. Also in agreement with experimental results, our models do not require a reward signal to reach RA directly, but rather rely on the biased variability received from LMAN. Our models also capture some of the qualitative details of the statistics of spiking in RA neurons.

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CRISPR mechanism

CRISPR immunity in bacteria

We model bacteria-phage interactions when bacteria are capable of CRISPR-mediated adaptive immunity. Our model can exhibit a variety of behaviors, from long-term coexistence of bacteria and phage, to extinction of one of them. We characterize the way in which the immune repertoire of a bacterial population depends on various characteristics of the interaction.

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Statistics of protein alignments

protein multiple sequence alignment

We investigated was of extracting structural and function information from statistical properties of protein alignments. We focused mainly on statistical coupling analysis (SCA) and direct coupling analysis (DCA). We showed that experimental evidence for many claims related to SCA is currently lacking and suggested focusing on proteins in which the method predicts the existence of several different sectors. We are also looking at how the global probability model used by DCA relates to protein function.

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Models of transcriptional regulation

enhancer, promoter, and transcription factors

We worked on generating quantitative models for describing transcriptional regulation in prokaryotes and eukaryotes. The models assume that the interaction between a transcription factor and a promoter or enhancer region is mediated by a sequence-dependent binding energy. Using a high-throughput mutational assay, we were able to accurately model the transcription profile of a mammalian enhancer, and use this information to generate artificial enhancer sequences better suited for a given purpose

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