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.

LATEST PAPERS:

Hsu IS, Strome B, Lash E, Robbins N, Cowen LE, Moses AM. A functionally divergent intrinsically disordered region underlying the conservation of stochastic signaling PLoS Genet. 2021 Sep 10; 17(9):e1009629
PLoS Genetics Link PDF Mirror PubMed Abstract Article
Regulatory networks Intrinsically Disordered Regions Time lapse microscopy Cell signalingMolecular evolution
Hsu IS Quantitative Experimental and Mathematical Approaches to Extract Information about Transcription Factor Pulsing University of Toronto 2021 Thesis Link PhD thesis
Computational modeling Regulatory networks Time lapse microscopyCell signaling

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.

LATEST PAPERS:

Hua SBZ, Lu AX, Moses AM. CytoImageNet: A large-scale pretraining dataset for bioimage transfer learning Learning Meaningful Representations of Life (LMRL 2021)
arxiv Link PDF Mirror Article
Deep learning Image Analysis Microscopy Screens
Lu AX Unsupervised machine learning for hypothesis discovery and representation learning in biological image and sequence analysis University of Toronto 2021 Thesis Link PhD thesis
Deep Learning Self-supervised Microscopy screensImage Analysis Intrinsically Disordered Regions Subcellular localization

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.

LATEST PAPERS:

Hsu IS, Strome B, Lash E, Robbins N, Cowen LE, Moses AM. A functionally divergent intrinsically disordered region underlying the conservation of stochastic signaling PLoS Genet. 2021 Sep 10; 17(9):e1009629
PLoS Genetics Link PDF Mirror PubMed Abstract Article
Regulatory networks Intrinsically Disordered Regions Time lapse microscopy Cell signalingMolecular evolution
Lu AX Unsupervised machine learning for hypothesis discovery and representation learning in biological image and sequence analysis University of Toronto 2021 Thesis Link PhD thesis
Deep Learning Self-supervised Microscopy screensImage Analysis Intrinsically Disordered Regions Subcellular localization

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!

LATEST PAPERS:

Michowski W, Chick JM, Chu C, Kolodziejczyk A, Wang Y, Suski JM, Abraham B, Anders L, Day D, Dunkl LM, Li Cheong Man M, Zhang T, Laphanuwat P, Bacon NA, Liu L, Fassl A, Sharma S, Otto T, Jecrois E, Han R, Sweeney KE, Marro S, Wernig M, Geng Y, Moses A, Li C, Gygi SP, Young RA, Sicinski P Cdk1 Controls Global Epigenetic Landscape in Embryonic Stem Cells Mol Cell. 2020 May 7;78(3):459-476.e13
Cell Press Link PDF Mirror PubMed AbstractArticle
kinase Regulatory Networks transcription factor Proteomics
Mehdi TF, Singh G, Mitchell JA, Moses AM. Variational Infinite Heterogeneous Mixture Model for Semi-supervised Clustering of Heart Enhancers. Bioinformatics 2019 Feb 7
Bioinformatics link PDF Mirror PubMed Abstract Article
Regulatory networks Transcription factors Genomics Machine Learning