olfactory periphery

With a focus on how natural systems adapt to varying environmental conditions, my interests range from modeling microbial populations to learning and information processing in nervous systems. Below I briefly describe some of my current and past work; for the technical details, see the links on the publications page.

Optimal olfactory receptor repertoire

We are working on a theoretical model explaining the uneven distribution of different receptor types in the olfactory epithelium. We suggest that the reason for which some receptor types are much more abundant than others is related to the affinities that these receptors have for different odors, and the natural statistics of odors. In particular, this means that in mammalian species, where the olfactory epithelium is regularly replaced, the distribution of receptors changes with olfactory experience, a phenomenon that has been observed experimentally.


Two-stage learning of birdsong

zebra finches

We built a novel model of song learning in zebra finches that emphasizes the two-stage nature of this process. Based on experimental evidence, our model assumes that a “tutor” circuit (corresponding to brain area LMAN in the bird) first learns a corrective bias for the song, and this is later solidified in a “student” circuit (the pre-motor area RA). This requires a match between the tutor signal and the synaptic plasticity rule in the student whose structure can be derived analytically in a firing-rate approximation. The resulting learning rules also work in spiking networks, and the tutor signal can also be generated using a reinforcement rule.

CRISPR mechanism

CRISPR immunity in bacteria

We modeled bacteria-phage interactions when bacteria are capable of CRISPR-mediated adaptive immunity. Our model exhibits a variety of behaviors, from long-term coexistence of bacteria and phage, to extinction of one of the populations. We characterized the way in which the immune repertoire of a bacterial population depends on various characteristics of the interaction, showing how the rate at which immunity is acquired can lead to more or less diverse immune repertoires.


Statistics of protein alignments

protein multiple sequence alignment

We investigated ways of extracting structural and functional 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 better ways to test it. We are also looking at how the global probability model used by DCA relates to protein function, and whether machine-learning methods applied directly to protein sequences can better predict fitness.


Models of transcriptional regulation

enhancer, promoter, and transcription factors

We worked on building 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 transcriptional profile of a mammalian enhancer, and used this information to generate artificial enhancer sequences better suited for a given purpose.