Research

Research in the Moses Lab

The research projects we pursue typically weave together many threads from disciplines such as evolutionary genetics, systems biology, machine learning, sequence analysis, computer vision, and more. The projects we have pursued over the years reflect the diverse interests of the graduate students and postdocs who worked in the lab. Below is a listing of the main themes that define and guide many of our research projects. For a complete picture of the work that we’ve accomplished, please visit our Publications page.

Evolution and Dynamics of Regulatory Networks

CRZ1 Pulse – Ian Hsu
Zhang & Mangelsdorf 2002
Kompella et al. 2017

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Most complex cellular processes are carried out by groups of genes working together in so-called pathways or networks. We seek to understand how these networks are encoded in genome sequences, how they create dynamic biological phenotypes, and they are created by evolution.

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Microscope images are big data

Lu et al. 2016
Handfield et al. 2015

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Automated microscopy has made it possible to measuring protein abundance and subcellular localization in millions of single cells. We are developing computational tools to extract basic biology from huge collections of microscope images without have to look at each one.

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Molecular Evolution of Disordered Regions

Zarin et al. 2017
Zarin et al. 2017
Zarin et al. 2017

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Intrinsically Disordered Regions (or IDRs) are enigmatic protein regions that are involved in a wide variety of biological processes. Although they are widespread, they usually show little evolutionary conservation. Is this rapid evolution a sign that they are just “junk” protein, or do they facilitate evolutionary diversity? This question is also of medical relevance: when we find mutations in patients’ IDRs we currently cannot tell what impact (if any) they are having.

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Beautiful bioinformatics for genomics and proteomics

Davey et al. 2015
Lai et al. 2012

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Complete sequencing of genomes is now routine, and yields thousands of genes and proteins, and information about the genetic differences in populations. All of this data needs to be organized and analyzed: bioinformatics!

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