Using Boolean Modelling and Natural Language Processing to unveils patterns of antimicrobial resistance

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• Marie Zepeda Mendoza (Microbiology and Infection, University of Birmingham)
• Thursday 06 February 2020, 12:00-13:00
• Biosciences 301.

If you have a question about this talk, please contact Fabian Spill.

My main research methods are computational modelling and data science. The talk has two parts. First, I will talk about a study we just completed of the use of Boolean modelling on the virulence network of the opportunistic pathogen Pseudomonas aeruginosa. Treatment of P. aeruginosa infections often fail due to its antibiotic resistance mechanisms, thus novel strategies aim at targeting virulence factors instead of growth-related features. Although the elements of the virulence networks of P. aeruginosa have been identified, how they interact and influence the overall virulence regulation is unclear. We reconstructed the signalling and transcriptional regulatory networks of 12 acute and 8 chronic virulence factors, and the 4 quorum sensing systems of P. aeruginosa, and analysed them with Boolean modelling techniques. Then, I will talk about my ongoing study on the use of natural language processing techniques for the unveiling of patterns of the spread of antimicrobial resistance in pathogenic microbes by analysing the text from all the public research epidemiology papers to date.

This talk is part of the Applied Mathematics Seminar Series series.

This talk is included in these lists:

• ARTS-LR1 (125)
• Applied Mathematics Seminar Series
• Biosciences 301
• School of Mathematics Events

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