About my work

Single cells · Microscopy · Simulation

I study how individual bacterial cells respond to changing environments and antibiotic treatment, and build tools for single-cell research.

I’m Georgeos, an assistant professor working in quantitative single-cell microbiology at UAE University. My research brings together live-cell microscopy, microfluidics, computational modelling, and machine learning.

From the mother machine to individual cellsA mother-machine microfluidic device, parallel nutrient-feeding lanes, and individual bacterial cells in narrow trenches.Mother Machine MicrofluidicsFeeding lanesCells in trenches
Mother Machine Microfluidics
Mother Machine Microfluidics
Feeding lanes
Feeding lanes
Cells in trenches

Research & software

All publications
Bacterial survival through antibiotic exposure and subsequent regrowth An illustrative antibiotic pulse ends two cell lineages. A surviving cell persists through treatment and divides after the antibiotic is removed. The proportions are schematic. Antibiotic Regrowth

Bacterial survival

My research focuses on antibiotic persistence: how individual bacterial cells behave, and what helps some survive changing conditions.

Explore the research
SyMBac generates synthetic microscopy and matching ground-truth labels from simulated cells Simulated cell geometry branches into two paired outputs: a phase-contrast-like microscopy image and coloured instance masks of the same four cells. Cell model Synthetic image Ground truth

SyMBac

I develop SyMBac, an open-source platform for simulating bacterial growth and microscopy, to make single-cell measurements more accurate.

Read about SyMBac

Research & background

Hello! My name is Georgeos, and I’m an assistant professor working in quantitative single cell microbiology at UAE University. Previously I was a PhD student in the Bakshi Lab at the Department of Engineering at the University of Cambridge. I recently completed my PhD under Dr. Somenath Bakshi’s supervision.

My research focusses on the use of widefield microscopy for the study of single cell behaviour, specifically that of bacteria, and even more specifically, antibiotic persistence. I like widefield microscopy as a tool because of its (relatively) low cost, its simplicity, and ability to generate large volumes of timelapse data when combined with microfludic devices such as the mother machine.

Thusfar my research has focussed on gaining accurate and precise insights from widefield (in my case fluorescence and phase contrast) microscopy data. To achieve this I’ve been building and maintaining a tool called SyMBac, which generates synthetic images of cells growing and dividing under the microscope under arbitrary conditions. Using SyMBac generated images to train AI based segmentation models leads to vastly enhanced precision - this allowed us to reveal a novel width regulation behaviour in E. coli as it enters and exits stationary phase.

My aim is to expand SyMBac into a full virtual microscopy platform, allowing researchers to fully simulate their single-cell microbioogy experiments end-to-end, from the biophysics of bacterial growth, to their metabolic kinetics, to image formation as cells are observed under the microscope. We have demonstrated this capability, and quantified many sources of error which can be introduced by the image formation process which could skew or corrupt biological conclusions.