Professor Allison Lopatkin

Allison J. Lopatkin

  • Assistant Professor of Chemical Engineering
  • Assistant Professor of Biomedical Engineering

PhD, Duke University, 2017

Office Location
4303 Wegmans Hall
(585) 275-6858
(585) 273-1358
Web Address

Selected Honors and Awards

NIH Research Enhancement Award (R15) (2021)
NSF Research in Undergraduate Institutions Award (2021)
Keewaunee Student Achievement Award, Duke University (2017)
ACS Synthetic biology second place poster prize, Winter qBio (2015)
Howard G. Clark Graduate Research Grant, Duke University (2014 – 2015)


ChE 116: Numerical Methods and Stats

Recent Publications

Ahmad, M.; Prensky, H.; Balestrieri, J.; ElNaggar, S.; Gomez-Simmonds, A.; Uhlemann, A.C.; Traxler, B.; Singh, A.; Lopatkin, A.J., "Tradeoff between lag time and growth rate drives the plasmid acquisition cost," Nature Communications, 2023,

Palomino, A.; Gewurz, D.; DeVine, L.; Zajmi, U.; Moralez, J.; Abu-Rumman, F.; Smith, R.P.; Lopatkin, A.J.,"Metabolic genes on conjugative plasmids are highly prevalent inEscherichia coliand can protect against antibiotic treatment," Nature ISME Journal, 2022.

Gomez-Simmonds, A.; Annavajhala, M.K.; Tang, N.; Rozenberg, F.D.; Ahmad, M.; Park, H.; Lopatkin, A.J.; Uhlemann, A.C., “Population structure of blaKPC-harbouring incN plasmids at a New York City medical center and evidence for multi-species horizontal transmission,” Journal of Antimicrobial Chemotherapy, 2022. doi: 10.1093/jac/dkac114

Persons, J.; Abhilash, L.; Lopatkin, A.J.; Roelofs, A.; Bell, E.V.; Fernandez, M.P.; Shafer III, O.T., “PHASE: A MATLAB based program for the analysis of Drosophila phase, activity, and sleep under entrainment,” bioRxiv, 2021. doi: 10.1101/2021.12.14.472617

Koong, J.; Johnson, C.; Rafei, R.; Hamze, M.; Myers, G.S.A.; Lopatkin, A.J.; Hamidian, M., “Phylogenetic analysis of two antibiotic susceptible non-clinical Acinetobacter baumannii strains belonging to global clone 1 reveals close relationship with multiply-antibiotic resistant clinical strains,” Microbial Genomics, 2021. doi: 10.1099/mgen.0.000705

Shoen, M.E.; Jahne, M.A.; Garland, J.; Ramirez, L.; Lopatkin, A.J.; Hamilton, K., “Quantitative microbial risk assessment of antibacterial resistant and susceptible Staphylococcus aureus in reclaimed wastewater,” Environmental Science & Technology,2021. doi: 10.1021/acs.est.1c04038

Moralez, J.; Szenkiel, K.; Hamilton, K.; Pruden, A.; Lopatkin, A.J., “Quantitative analysis of horizontal gene transfer in complex systems,” Current Opinions in Microbiology, 2021. doi: 10.1016/j.mib.2021.05.001

Williams, S.C.; Forsberg, A.P.; Lee, J.; Vizcarra, C.; Lopatkin, A.J.; Austin, R.N., “Investigation of the prevalence and catalytic activity of fused-rubredoxin alkane monooxygenases (AlkBs),” Journal of Inorganic Biochemistry, 2021.doi: 10.1016/j.jinorgbio.2021.111409

Prensky, H.; Gomez-Simmonds, A.; Uhlemann, A.C.; Lopatkin, A.J., “Conjugation dynamics depend on both the plasmid acquisition cost and the fitness cost,” Molecular Systems Biology, 2021. doi: 10.1525/msb.20209913

Lopatkin, A.J.; Bening, S.C.; Manson, A.L.; Stokes, J.M.; Kohanski, M.A.; Badram, A. H.; Earl, A.M.; Cheney, N.J.; Yang, J.H.; Collins, J.J., "Clinically relevant mutations in core metabolic genes confer antibiotic resistance,” Science, 2021. doi: 10.1126/science.aba0862

Lopatkin, A.J.; Yang, J.H., “Nucleotide metabolism and antibiotic treatment failure,” Frontiers in Microbiology, 2021. doi: 10.3389/fdgth.2021.583468

Lopatkin, A.J.; Collins, J.J., “Predictive biology: modeling, understanding, and harnessing microbial complexity,” Nature Microbiology Reviews, 2020. doi: 10.1038/s41579-020-0372-5

Research Overview

Horizontal gene transfer (HGT) is a natural type of bacterial evolution that allows cells to transfer large pieces of DNA amongst one another. HGT is particularly common in native microbiomes, and serves as the primary way that bacteria, and especially pathogens, adapt to new and often stressful environments. Our lab uses systems and synthetic biology approaches to engineer genetic communication in bacterial communities. Specifically, we integrate computational and experimental techniques, including mathematical modeling, bioinformatics, and molecular microbiology, to understand, predict, and optimize natural HGT machinery for various purposes. This work is applicable to a broad range of clinical and environmental contexts, including inhibiting drug resistance and creating functional human or agricultural microbiome systems. Ongoing projects involve designing optimal HGT recipient strains, and engineering a toolbox of orthogonal HGT components.

Research Interests

  • Systems and Synthetic Biology
  • Computational Biology
  • Engineered Microbial Communities
  • Metabolic Engineering
  • Mathematical Modeling
  • Machine Learning
  • Bacterial Population Dynamics
  • Horizontal Gene transfer
  • Antibiotic Resistance