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A chemogenomic Approach for Antibiotic Discovery

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

Secondary bacterial infections from COVID19 highlight the need for new antibiotics. We are looking for a Ph.D. student to work on solving the antibiotic crisis!

The program is interdisciplinary and involves bacterial functional genomics, chemoinformatics, and machine learning. Specifically, we are building high-density transposon mutant libraries and CRISPRi libraries in Gram-negative bacteria. The libraries will be exposed to large collection of chemical compounds and the relative abundance of the mutants will be assessed by next-generation sequencing of barcodes. The data gathered will be used to feed a machine-learning algorithm to predict antibiotic activity and mode of action of virtual compounds.

Individuals with interest in joining an interdisciplinary group that strive for excellence in a diverse and inclusive environment are welcome to apply.


Degree in Chemistry, Biochemistry, Microbiology, Genetics, or Computer Science

Excellent academic performance (GPA of at least 80%, or first-class level, or A, A+ in all courses)

Contact Dr. Cardona at with a CV and a copy of University transcripts.

Funding Notes

The program has recently received 5-year funding by the Canadian Institute of Health Research (CIHR)


Hogan AM, Scoffone VC, Makarov V, Gislason AS, Tesfu H, Stietz MS, Brassinga AKC, Domaratzki M, Li X, Azzalin A, Biggiogera M, Riabova O, Monakhova N, Chiarelli LR, Riccardi G, Buroni S, Cardona ST. 2018. Competitive fitness of essential gene knockdowns reveals a broad-spectrum antibacterial inhibitor of the cell division protein FtsZ. Antimicrobial Agents and Chemotherapy. 62(12). pii: e01231. Highlighted in UM today.

Hogan AM, Rahman Z, Lightly TJ, Cardona ST.2019. A broad-host range CRISPRi toolkit for silencing gene expression in Burkholderia. ACS Synthetic Biology doi: 10.1021/acssynbio.9b00232.

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