Our research interests

Life is an expression of molecular chemistry. We know that living cells and tissues are an assembly of molecules that are reacting and interacting with each other. However, we also know that molecules are not alive and that a chemical reaction is not alive. So why is it that cells and tissues are alive?

With this intriguing scientific question in mind, the past decade has seen researchers in the field of bottom-up synthetic biology try to fill this gap between biology and chemistry to better understand how the non-living becomes alive. To do this, attempts have been made to construct what are called synthetic cells or protocells. These are cell-like entities created from scratch using inanimate molecules, materials, and chemical reactions. Due to the rapid evolution of bottom-up synthetic biology, today protocells have many different definitions, but can be broadly categorised as typical or non-typical (Review Paper). Typical protocells are by all means artificial cells. They exhibit a cell-like structure and at least one or more of the key characteristics of living biological cells such as self-sustainability, self-reproduction, or the ability to evolve. Non-typical protocells are instead materials engineered to mimic one or more abilities of biological cells. Most importantly, non-typical protocells do not need to strictly mimic living cells and have no restrictions on the type of materials, methodologies and chemical reactions that can be used to build them. Our Research Group works with this latter category and the term protocell here refers to non-typical protocells.

While many research teams are currently focusing on advancing the biochemical complexity of individual protocells, we believe that the future of this research field will see the use of protocells as building blocks to generate unprecedented adaptive and autonomous forms of biomaterials that will be capable of emulating living tissues. We call these tissue-like materials protocellular materials (PCMs).

A “beating” prototissue spheroid. Contractions are induced thermally, the temperature ramp applied is indicated in the animated graph at the bottom. Video from Nat. Mater. 2018, 17, 1145-1153.

PCMs are free-standing 3D networks of interconnected protocell populations that communicate and display emergent behaviours (e.g., collective contractility), which are not observed in the individual protocell building blocks. Significantly, like non-typical protocells, PCMs can be constructed from functional molecules and materials, providing unprecedented opportunities to design centimetre-sized tissue-like architectures that can do more than simply mimic living tissues. They could function under extreme conditions and exhibit a wide range of mechanical properties and bio-inspired metabolic functions. Because of these reasons, PCMs are destined to find applications in the most diverse areas of science and technology, from biomedical science to environmental science, and soft robotics. (Review Article)

Our research therefore aims to pioneer new frontiers in bottom-up synthetic biology by developing the first chemical reactions and experimental methodologies to start organising millions of protocells into free-standing PCMs that are stable in water, are highly modular, and capable of autonomously adapting and responding to their environment.
More specifically, we work at the interface of synthetic chemistry, materials chemistry, and bottom-up synthetic biology to:
1) Design and Construct new adhesive synthetic protocell membranes.
2) Develop new technologies to assemble of PCMs with high spatiotemporal resolution.
3) Engineer forms of PCMs capable of new emergent behaviours.

Overall our research efforts will help bridge the gap between biology and chemistry and lead to important applications in microbioreactor technology, diagnostics, drug delivery, personalised therapy, pollutants removal, and soft robotics.


DNA/clay protocell containing catalytic proto-organelles.

Chemical Programming of Protocells
Researchers in bottom-up synthetic biology are using biological molecules readily available in Nature (e.g., enzymes, DNA, genes, biological cycles etc.) to build a cell from scratch. As Synthetic Chemists, we believe that now we have the knowledge and expertise to diverge from Nature and start developing our own tools to implement cell-like behaviours using fully synthetic functional molecules and materials.
For example, we are currently pioneering:

  • The use of catalytic polyoxometalates for the development of protocells and prototissues capable of photo-assisted water oxidation (project in collaboration with Prof.s Steve Mann – UoB and Marcella Bonchio – University of Padua).
  • The implementation of light-triggered protocell-protocell and protocell-living cell communication based on photocaged molecules (project in collaboration with Prof. Steve Mann – UoB).
  • The use of metallic nanoparticles to develop functional protocell membranes (project in collaboration with Prof. Mark S. Workentin – University of Western Ontario, Canada).

Machine Learning-Assisted Polymer Design
The synthesis of polymers and copolymers with specific physical, chemical and mechanical properties is extremely complex because it relies on a very high number of variables, such as monomer structure, tacticity, polymer-solvent and polymer-polymer interactions, etc. This implies that the design and synthesis of polymers relies on the researcher’s abilities and “chemical sense”. The polymer is first synthesised, analysed, and finally evaluated. If the properties do not meet the target, then the researcher starts back again. This three-step process is extremely time- and resource-consuming. Our idea is reverse this synthetic approach by developing the use Machine Learning to:

  • Obtain detailed synthetic procedure for the synthesis of polymers with specific physical, chemical and mechanical properties.
  • Fill the gaps between structure and properties in polymer chemistry.
  • From the measurement of a single polymer property that is easy to measure (for example TgTmMn) predict other properties (for example dielectric constant, Young’s modulus etc.).