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 protocells. These are cell-like entities created from scratch using molecules, materials, and chemical reactions. 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, termed prototissues.
Prototissues and Protocellular Materials (PCMs)
Prototissues comprise free-standing 3D networks of interconnected protocell populations that communicate and display synergistic behaviours such as collective contractility and self-folding, which are not observed in the individual protocell building blocks. Significantly, prototissues 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. We call these specific types of prototissues protocellular materials (PCMs) and they 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 self-standing protocellular materials 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 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 synergistic behaviours within protocellular materials.
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.
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.
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 Tg, Tm, Mn) predict other properties (for example dielectric constant, Young’s modulus etc.).