Dr Nicole Wheeler

Dr Nicole Wheeler

Institute of Microbiology and Infection
School of Computer Science
Birmingham Fellow

Contact details

Address
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Dr Wheeler’s work focuses on the development of computational screening tools for identifying DNA from emerging biological threats, establishing genomic pathogen surveillance in resource-limited settings, One Health surveillance of antimicrobial resistance, and the ethical development of artificial intelligence (AI) for health applications.

Qualifications

  • PhD in Biochemistry, University of Canterbury, 2017
  • BSc in Biochemistry, University of Canterbury, 2013

Biography

Dr Wheeler has a background in biochemistry and microbial genomics, complemented by experience in developing machine learning methods for predicting the effects of genetic variation on the virulence of pathogens. She has provided expertise on machine learning for genomic pathogen surveillance for several international programs, including a world-first AI-driven One Health AMR surveillance system. She is also actively involved in public outreach and the development of governance frameworks to ensure the safe and responsible development of technologies for health improvement. 

Teaching

  • Genomic Medicine - Application of Genomics in Infectious Diseases
  • Intercalated BMedSc Clinical Sciences - Microbiology and Infectious Diseases module

Research

Research Interests:

Dr Wheeler develops computational tools for the genomic surveillance of emerging infectious disease threats.

 Current Projects:

An international common mechanism for DNA synthesis screening: In collaboration with NTI | bio, this project is developing screening software for DNA synthesis orders, to flag orders that contain a biological threat.

Graph-based analysis of multi-omic data for threat assessment of microbial pathogens: This project aims to use machine learning approaches to link genetic variation to phenotypic variation via changes in the expression and function of genes.

Other activities

In addition to research work, Dr Wheeler is heavily involved in public engagement activities, winning the Wellcome Genome Campus Commitment to Public Engagement Prize in 2020. Her current projects guide students through:

  1. AI in Schools: Students program an Arduino to gather data on light, movement and temperature across their school. School Smart Meter and student-gathered data are used to model and forecast school energy usage. Students explain their findings, identify any biases in the model, and make recommendations about reducing the school’s carbon footprint. 
  2. Bioinformatics in Schools: Teaches high school students programming and bioinformatics. Students conduct four-week research projects exploring questions that arise from scientific research.
  3. Infectious Disease and AMR: Students investigate real data from infectious disease outbreaks, communicate their findings to the public, and recommend interventions to end or prevent outbreaks.
Interest in collaborating on these activities or developing new programs is welcome.

Publications

NIHR Global Health Research Unit on Genomic Surveillance of AMR. Whole-genome sequencing as part of national and international surveillance programmes for antimicrobial resistance: a roadmap. BMJ Global Health. 2020;5. doi:10.1136/bmjgh-2019-002244

Bawn M, Alikhan N-F, Thilliez G, Kirkwood M, Wheeler NE and Petrovska L, et al. Evolution of Salmonella enterica serotype Typhimurium driven by anthropogenic selection and niche adaptation. PLoS Genetics. 2020;16: e1008850.

Wheeler NE, Sánchez-Busó L, Argimón S and Jeffrey B. Lean, mean, learning machines. Nature Reviews Microbiology. May 2020, 18(5):266. doi: 10.1038/s41579-020-0357-4.

Van Puyvelde S, Pickard D, Vandelannoote K, Heinz E, Barbé B and de Block T, et al. An African Salmonella Typhimurium ST313 sublineage with extensive drug-resistance and signatures of host adaptation. Nature Communications. 2019;Sep 19;10(1):4280. doi: 10.1038/s41467-019-11844-z

Hicks AL, Wheeler N, Sánchez-Busó L, Rakeman JL, Harris SR and Grad YH. Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data. PLoS Computational Biology. 2019 Sep 3;15(9):e1007349. doi: 10.1371/journal.pcbi.1007349. eCollection 2019 Sep.#

Sackton TB, Grayson P, Cloutier A, Hu Z, Liu JS and Wheeler NE, et al. Convergent regulatory evolution and loss of flight in paleognathous birds. Science. 2019 Apr 5;364(6435):74-78. doi: 10.1126/science.aat7244

Wheeler NE. Tracing outbreaks with machine learning. Nature Reviews Microbiology. 2019 May;17(5):269. doi: 10.1038/s41579-019-0153-1

Wheeler NE, Blackmore T, Reynolds AD, Midwinter AC, Marshall J and French NP, et al. Genomic correlates of extraintestinal infection are linked with changes in cell morphology in Campylobacter jejuni. Microbial Genomics. 2019 Feb;5(2):e000251. doi: 10.1099/mgen.0.000251. Epub 2019 Feb 19

Wheeler NE, Gardner PP and Barquist L. Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica. PLoS Genetics. 2018 May 8;14(5):e1007333. doi: 10.1371/journal.pgen.1007333. eCollection 2018 May.

Wheeler NE, Barquist L, Kingsley RA and Gardner PP. A profile-based method for identifying functional divergence of orthologous genes in bacterial genomes. Bioinformatics. 2016 Dec 1;32(23):3566-3574. doi: 10.1093/bioinformatics/btw518. Epub 2016 Aug 8

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