Dr Giovanni Stracquadanio (UKRI EPSRC Fellow / FHEA)
UKRI EPSRC Fellow / Senior Lecturer in Synthetic Biology
Michael Swann Building, room 2.35
Max Born Crescent
- The King's Buildings, Edinburgh
- Post code
- EH9 3BF
Giovanni Stracquadanio is an UKRI EPSRC fellow, Senior Lecturer in Synthetic Biology and co-director of the Edinburgh Genome Foundry (EGF).
Dr 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 using 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.
Dr Stracquadanio has authored more than 40 research articles published in international peer-reviewed journals, including Science, 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 Associate College.
- 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
- 2021-current. Member of the EPSRC Peer Review Associate College.
- 2019-current. Co-director of the Edinburgh Genome Foundry.
- 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.
- Biotechnology 3 (BILG09014)
- Tools for Synthetic Biology (PGBI11092)
Open to PhD supervision enquiries?
Areas of interest for supervision
Project for prospective doctoral students are available in the following research areas:
- Computational projects
- Design and development of Deep learning methods for enzyme engineering.
- Design and development of Deep learning methods for multi-omic cancer data integration and pathway discovery.
- Design and development of synthetic genomics engineering tools.
- Design and development of statistical genetics tools for cancer GWAS.
- Wetlab projects
- Development of automated workflows for enzymatic assays.
- Development of pooled high-throughput sequencing assays for sequence verification of synthetic constructs.
- Development of pooled high-throughput sequencing assays for gene expression quantification.
- Development of pooled high-throughput sequencing assays for synthetic genomes evolution.
Current PhD students supervised
- Evgenii Lobzaev, CDT in Biomedical AI
- Anima Sutradhar, EastBIO DTP
- Aidan Marnane, CDT in Data Science
Past PhD students supervised
Viola Fanfani, PhD student.
Angelo Gaeta, PhD student.
Fabio Cassano, Postdoctoral fellow.
David Zihala, Visiting postdoctoral fellow.
Martina Zatopkova, PhD student.
Dr Stracquadanio is a multi-disciplinary scientist interested in understanding the molecular mechanisms underpinning complex phenotypes and diseases using two of the most disruptive technologies available: synthetic biology and machine learning. Our long term goal is to reverse-engineer biological systems to create generative algorithms to design, build and test biological agents for addressing healthcare problems, such as rare metabolic diseases and cancer, and industrial biotechnology challenges, like de-novo enzyme engineering.
Synthetic human enzyme engineering
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. However, enzymes loose catalytic activity in blood and often cause a severe immune response; moreover, current manufacturing technologies have low yield, which dramatically raises the cost of treatment. Here we are building on our expertise in machine learning and synthetic genomics to design and build human enzymes at scale.
Our goal is to establish technologies to optimise the therapeutic properties of synthetic enzymes and to engineer expression systems for inexpensive production of these molecules, in order to provide new sustainable treatment for patients with rare metabolic disorder.
Recently, Dr Stracquadanio was awarded the UKRI EPSRC Fellowship (~£1.5M) to engineer new computational and experimental methods for designing and manufacturing enzyme replacement therapies for Fabry's disease.
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 genetics 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.
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. Synthetic chromosomes represent also a flexible chassis to integrate synthetic pathways into existing expression systems.
However, the design principles to build new, functional chromosomes are mostly unknown, and the current technology limits the DNA molecules that can be synthesized. We are addressing these issues by developing statistical models to learn how wild-type genomes change upon the integration of synthetic chromosomes, and by developing methods to optimise manufacturing of chromosome scale molecules, by repositioning mathematical programming methods we developed for electronic engineering. Dr Stracquadanio works in close collaboration with the Edinburgh Genome Foundry to scale-up our experimental work.
Computational biology algorithms and software engineering
Computational methods are now cornerstone of modern biology. The lab is committed to release high-quality, open-source tools that can be easily integrated into analysis workflows. To do that, we adopt software engineering principles and methods that are standard in industry. Currently, our ecosystem relies on Python, Git, GitHub and GPU computing. All our analyses are implemented using either Nextflow or Snakemake workflow management systems. We also maintain a collection of Docker containers to facilitate the adoption of our tools. You can check our growing suite of software on GitHub.
Current research interestsComputational Biology - Variational deep learning models for de-novo enzyme engineering - Bayesian methods to identify genomic loci associated with cancer heritability - Graph neural networks for discovering cancer genes and pathways - Statistical methods for biological network analysis - Computational methods to engineer, verify and characterise synthetic chromosomes - Sequencing analysis of solid and liquid tumors - Transcriptomic analysis of solid and liquid tumors - Scientific software development Synthetic Biology - Enzyme engineering - Whole genome synthesis - Pathway assembly - High-throughput and combinatorial screens - Lab automation
Past research interests- Electronic design automation - Parameters estimation of metabolic networks - Space trajectory optimisation.
- iScience (Journal)
- Synthetic Biology (Journal)
- SLAS Technology (Journal)
- BMC Genomics (Journal)
- Experimental Biology and Medicine (Journal)
- IEEE/ACM Transactions on Computational Biology and Bioinformatics (Journal)
- Bioinformatics (Journal)
- PLoS ONE (Journal)
Current project grants
2022-2026. UKRI EPSRC fellowship: "GREET: Generative Recombinant Enzyme Engineering for Therapeutics".
Past project grants
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".