Biochemistry

What I do:

I am a first-year PhD rotation student in the Cambridge Biosciences BBSRC DTP programme. My current rotation project is with the Welch Lab, Department of Biochemistry, where I am investigating transcriptional regulation of biofilm-associated proteins in the opportunist pathogen Pseudomonas aeruginosa.

What I do:

My research combines my expertise in cellular membrane dynamics and host:pathogen interactions. I study how bacterial pathogens interact with and manipulate eukaryotic membranes to enable their own growth and pathogenicity.

What I do:

I am a Postdoctoral Research Associate in synthetic biology and microfluidics at the Department of Biochemistry. I have extensive research experience in the areas of computational biology, molecular biology, microbiology, biocatalysis and organic chemical synthesis.

What I do:

I am a Biochemistry Part III student. For my research project I study the biofilm proteome and secretory systems in Pseudomonas aeruginosa.

Research Interests:

Pseudomonas aeruginosa biology. The application of single chain variable fragment antibodies in microbiology. Rickettsiales biology.

Key Projects:

2022-23: Developing single chain antibodies to Rickettsiales bacteria for use as research tools. Collaboration between the Salje group and the Jackson group.

 

What I do:

• In my research group, we study the host-pathogen cell biology of obligate intracellular bacteria, focussing on organisms within the order Rickettsiales.
• My lab has been partly based at Mahidol University/MORU in Thailand since 2013

What do I do?

I study the structural biology of enzymes involved in bacterial virulence of Pseudomonas spp.

Research Interests:

• Protein Crystallography

• Computational drug design

• Inhibitor design for bacterial enzymes

Key Projects:

• Identification of LsrK kinase Inhibitors to target quorum sensing (University of Eastern Finland, 2020)

What do I do?

I catalogue ecological relationships and potential spatial separations between cystic fibrosis airway microbiome species from a hybrid method of statistics, mathematical modelling and time-series data of clinical and laboratory samples.