Dr Eszter Csibra

Research interests

Bacterial strains such as model laboratory strains of E. coli have been used for decades as microbial cell factories for the production of high value proteins for applications ranging from healthcare to environmental sustainability. However, traditional biotechnology is limited in focusing its attention on the genetic engineering of the desired protein or pathway in question. 

The synthetic biology (or ‘engineering biology’) field promises to revolutionise biotechnology by integrating the aims of biotechnology with principles from engineering and computational fields in order to engineer cells, strains and proteins with characteristics such as robustness to changing environments and reproducibility between settings.

In our lab we are particularly interested in engineering the chassis (the microbial cell), its genome, and its protein synthesis machinery, alongside the genetic construct itself, to fine tune the performance of such microbial cell factories for a range of applications. We use both high-throughput screening and directed evolution, as well as biochemical and cell biology approaches, to achieve these aims. Finally, we are very interested in Open Science. As such we endeavour not only to publish our data and analytical methods, but invest time in authoring research software for our data sets that we publish as open source tools for the community. 

Engineering Microbial Protein Synthesis

A major area of research in synthetic biology in recent years concerns the observed trade-off between synthetic protein yield and cellular growth rate of engineered microbial strains. This is typically formalised as an issue of ‘cellular capacity’. Simply put, this is a model in which the host cell and synthetic circuit are considered to be in competition for a finite pool of cellular ‘resources’ (be it enzymes, substrates, or energy), resulting in a situation in which the resource requirements of each system negatively impact the performance of the other.

A promising route to tackle this competition is to separate the synthetic system’s resources from the host’s by producing ‘orthogonal’ systems that work side by side with the host systems without interacting with host substrates. We are studying one such system that uses tethered ribosomal RNAs to create an orthogonal ribosome which is used to produce the synthetic circuit (Csibra et al., 2023). We are interested in investigating methods for optimising such systems as well as using them as tools to observe and understand microbial protein synthesis in situ.

Engineering Microbial Protein Secretion

The downstream processing of microbially produced synthetic (recombinant) proteins is simplified by directing the proteins out of the cell. Secretion of proteins has been successfully used in a variety of contexts, but akin to intracellular protein production, is also affected by cellular resource challenges.

Inspired by the success of synthetic biologists in generating resource-responsive genetic circuits, in which synthetic feedback loops control protein production in response to stress, we are exploring methods for generating auto-regulatory circuits that are specialised for producing secreted proteins.

Quantitative Biology of Fluorescence

As illustrated by the Design-Build-Test-Learn framework of classical synthetic biology, the successful development of engineered cells typically requires multiple iterative cycles of variant generation and testing. Fluorescence remains the most powerful method for functional library screening, as its detection does not require cell lysis or processing, it can be detected effectively across many orders of magnitude, and enables detection spatially, kinetically, and in bulk or single cell formats. However, it is impossible to extract biologically meaningful quantitative information from a typical fluorescence data set, limiting its usefulness.

We are interested in developing methods to extract information about absolute quantities (eg. protein copy numbers per cell) from fluorescence data. To do this, we have developed a straightforward method for accurate and precise protein number quantification using multiwell plate reader assay data (Csibra and Stan, 2022), and co-published this with a freely available open source software tool (FPCountR). We are actively exploring work to broaden the application of such methods, including with novel software tools (Csibra and Stan, 2023).

Qualifications

• BA, University of Cambridge
• PhD, University of Cambridge

Professional memberships

• Biochemical Society
• Microbiology Society
• Applied Microbiology International

Student education

I contribute to teaching in microbiology and synthetic (aka engineering) biology.

Research groups and institutes

• Biotechnology
• Engineering of Biomolecules
• Microbiology
• Protein structure, stability and dynamics