Prof Giovanni Stracquadanio

Personal Chair in Engineering Biology / UKRI EPSRC Fellow

Biography

Background

Giovanni Stracquadanio is a Professor of Engineering Biology, UKRI EPSRC fellow and co-director of the Edinburgh Genome Foundry (EGF).

Prof Stracquadanio obtained a PhD in Informatics from the University of Catania (Italy) in 2010, and then received postdoctoral training in synthetic biology in Joel Bader and Jef Boeke labs at the Johns Hopkins University working on the synthetic yeast genome. Dr Stracquadanio was a main contributor to the Synthetic Yeast (Sc2.0) genome project, pioneering algorithms and developing software at the foundation of the first synthetic eukaryotic genome. He has also developed tools used in large-scale synthesis projects, streamlining chromosomes engineering and the assembly of biological pathways.

In 2014, he moved to the Ludwig Institute for Cancer Research at the University of Oxford to work on cancer genetics, in Gareth Bond’s lab; here, he focused on studying how high-frequency inherited p53 mutations affect the risk of cancer and response to treatment developing statistical genetics methods.

In 2016, before moving to the University of Edinburgh, Dr Stracquadanio established the first computational biology lab at the School of Computer Science and Electronic Engineering of the University of Essex, and in 2017 received the Wellcome Trust Seed Award in Science.

Prof Stracquadanio has authored more than 40 research articles published in international peer-reviewed journals, including Science, Cell, Nature Rev. Cancer, Cancer Research and PNAS. He also serves as Associate Editor for BMC Genomics and as reviewer for EPSRC, BBSRC, MRC and FLF. Since 2021, he is also a member of the EPSRC Peer Review College.

 

Qualifications

  • 2018. Fellow of the Higher Education Academy
  • 2010. PhD in Informatics. Department of Mathematics and Informatics. University of Catania, Italy.
  • 2006. MSc in Informatics. Department of Mathematics and Informatics. University of Catania, Italy.
  • 2004. BSc in Informatics. Department of Mathematics and Informatics. University of Catania, Italy.

Responsibilities & affiliations

  • 2024-current. Personal Chair in Engineering Biology. University of Edinburgh.
  • 2021-current. Member of the EPSRC Peer Review College.
  • 2019-current. Co-director of the Edinburgh Genome Foundry.
  • 2019-03/2024. Senior Lecturer in Synthetic Biology. University of Edinburgh.
  • 2016-2018. Lecturer. School of Computer Science and Electrical Engineering. University of Essex, UK.
  • 2014-2016. Postdoctoral Fellow. Ludwig Institute for Cancer Research, University of Oxford, UK.
  • 2010-2014. Postdoctoral Fellow. School of Medicine and Department of Biomedical Engineering. Johns Hopkins University, USA.

Postgraduate teaching

  • Tools for Synthetic Biology (PGBI11092)

Open to PhD supervision enquiries?

Yes

Areas of interest for supervision

Project for prospective doctoral students are available in the following research areas:

  • Computational projects 
    • Design and development of generative AI models for enzyme engineering.
    • Design and development of synthetic genomics engineering tools.
    • Design and development of physics inspired protein engineering models.
  • Wetlab projects
    • Biologics production in CHO or P. pastoris.
    • Genome engineering in CHO or P. pastoris.

Current PhD students supervised

Postdocs 

  • Dr Ginevra Camboni
  • Dr Matthias Dierick
  • Dr Louise Holyoake
  • Dr Josh Littlefair

PhD Students

  • Evgenii Lobzaev, CDT in Biomedical AI.
  • Anima Sutradhar, EastBIO DTP.
  • Martyna Kasprzyk, EastBIO DTP.
  • Sebastien Kamp, CDT in Biomedical AI.

Past PhD students supervised

  • Michael Herrera, Postdoctoral fellow.

  • Sofija Semniuk, Postdoctoral fellow.

  • Viola Fanfani, PhD student. 

  • Angelo Gaeta, PhD student.

  • Fabio Cassano, Postdoctoral fellow.

  • David Zihala, Visiting postdoctoral fellow.

  • Martina Zatopkova, PhD student.

Research summary

High-throughput technologies have generated a deluge of data describing living organisms at unprecedented resolution. The wealth and diversity of biological data available and the new wave of powerful AI models provide unique opportunities to understand the design principles underpinning cellular life. With the democratisation of DNA synthesis, we can use these design rules to engineer new designer systems ranging from genes to chromosomes and proteins.

Our mission

Developing next generation therapies for Lysosomal Storage Diseases

Enzymes are building blocks of cellular life and act as natural catalysts able to accelerate almost any reaction. Enzymatic deficiencies are usually associated with devastating rare diseases, which can only be treated by providing the defective enzyme through intravenous injections, a treatment knows enzyme replacement therapy (ERT). However, recombinant enzymes loose catalytic activity in blood and often cause a severe immune response. Moreover, current manufacturing technologies are sub optimal, which lead to a dramatically high cost of treatment. We are building on our expertise in AI and synthetic genomics to design and build a next-generation of enzyme replacement therapies. Our goal is to establish technologies to optimise the therapeutic properties of recombinant enzymes and to engineer expression systems for inexpensive production of these molecules, in order to provide new sustainable treatment for patients with lysosomal storage diseases, particularly focusing on Fabry disease.

 

Our technologies

Generative Artificial Intelligence for biologics engineering

Designing biologics is a complex task, which requires identifying amino acids or nucleotide changes to maximise their therapeutic properties or maximising manufacturing yield. Since the number of possible changes easily outnumbers the total number of atoms in the Universe, our group takes advantage of the wealth genomic, transcriptomic and proteomic data available to train generative AI models to sample this vast design space to identify candidates for downstream experimental screening. Our backbone framework are variational autoencoders, which we optimise for training and design efficiency using our experience built across 20 years of statistical learning, optimisation and biological data analysis methods development. Our methods build on robust engineering frameworks and industry standards, such as Python, Git, GitHub and GPU computing, and deployed using the Nextflow workflow management systems.

Genome engineering for biologics production

Recent advances in DNA synthesis, high-throughput sequencing and computer aided design (CAD) tools allow us to engineer the genomes of living cells and address questions and tackle problems intractable with standard technologies. We pioneered CAD software for synthetic genome engineering, which has been instrumental to design the synthetic yeast genome (Saccharomyces cerevisiae 2.0, Sc2.0) the first synthetic eukaryotic genome ever built. Sc2.0 allows us to address a number of open questions in genome biology, including the identification of a minimal eukaryotic genome compatible with life. We are now building on our expertise in synthetic genomics and engineering biology to engineering microbial and mammalian cells to optimise biologics production. Our workhorse systems are Komagathella Phaffi (aka P. Pastoris) and Chinese Hamster Ovary (CHO) cell lines, primarily engineered to efficiently produce human lysosomal enzymes.

Automated high-throughput biology

Artificial Intelligence models can generate thousands of putatively functional biologics, but experimental testing remains paramount to make sure they are effective for therapeutic use. However, classical, low throughput manual experimental protocols do not scale for an AI-driven drug discovery workflow.  To make sure our experimental workflow keeps up with our generative AI models, we are developing miniaturized and fully automated protocols for protein expression in both Komagathella Phaffi (aka P. Pastoris) and Chinese Hamster Ovary (CHO), using high-throughput electroporation systems, liquid handling robots, and highly throughput assays. Our lab works in close collaboration with the Edinburgh Genome Foundry to scale-up our experimental work.

Other areas of interest

Cancer genetics and genomics

Decades of research have shown that genomic mutations in key genic regions are responsible for the transformation of normal cells into cancer cells. However, while a causal role for somatic mutations has been shown for many common malignancies, the role of high frequency inherited mutations has remained elusive. We are addressing this question by developing statistical learning methods to dissect the heritable risk of cancer at the gene level. Nonetheless, the polygenic architecture of cancer requires linking gene level information into pathways. To do that, we are developing deep graph neural networks to integrate transcriptomic and proteomic data and infer aberrant pathways involved in cancer metabolism and affecting response to therapy.

Research activities

View all 8 activities on Research Explorer

Current project grants

2024-2029. BBSRC. Engineering Biology Mission Hub. "Engineered Genetic Control Systems for Advanced Therapeutics".
2023-2025. UKRI. Artificial intelligence innovation to accelerate health research. "IDERT: Intelligent Deimmunization for Enzyme Replacement Therapies."
2023-2024. Scottish Enterprise - Opportunity Qualification. ZYTHERA.
2022-2025. UKRI EPSRC fellowship: "GREET: Generative Recombinant Enzyme Engineering for Therapeutics".

Past project grants

2022-2024. BBSRC. Engineering Biology Transition Award. "Engineering Biology for Cell and Gene Therapy Applications."
2022-2023. BioIC Feasibility Fund. "Robust enzyme engineering using AI."
2017-2021. Wellcome Trust Seed Award in Science: "Network analysis of the effect of hypoxia and nutrient deprivation on motility and metabolic switching of cancer cells".

View all 44 publications on Research Explorer

This article was published on