The integration of replication with metabolism has been advanced through the development of self-replicating molecules that can catalyze sugar glycosidic bond hydrolysis, hydrazone bond formation, and Knoevenagel condensation. A new system of compartment-forming molecules has been developed for which the internalized replicator catalyses the production of more compartment-forming molecules, thereby promoting compartment growth. In this system replication is coupled to compartment formation through a simple metabolism for the first time. To enhance evolvability of the self-replicating systems work we have measured the replication fidelity which has enabled us to come up with a design strategy for new replicators with improved fidelity. In addition, an automated chemically mediated replication-destruction setup has been constructed with the potential for improving fidelity by error correction.
To respond to changes in their immediate environment, synthetic protocells need to receive and process signals that originate outside their borders and integrate that information into a unified action plan. We have developed various responsive systems that can be triggered by external signals, replicate, catalyze substrate metabolism, and even react in distinct compartments. We demonstrated the ability to control -sheet catalysis (potentially replication) using light, and to couple the -sheet replication with regio-specific and stereo-specific hydrolytic glycosidase activity. Additionally, -helical replicators were used to drive chemical oscillations, and we have now systematically unravelled the inherent peptide characteristics and environmental parameter space that affect the oscillations sustainability and robustness. In collaboration we have experimentally investigated the -helical replicator feedback behaviour when mutually interacting with dynamic assembly-driven -sheet replicators.
We have previously shown that when the autocatalytic formose reaction, which converts formaldehyde into sugars, is compartmentalized in emulsion droplets, droplet growth becomes coupled to autocatalysis through osmosis and diffusion. This simple physico-chemical system exhibits several hallmarks of living systems: growth, division, variation, competition, selection, and rudimentary heredity. During the first project period, we have focused on extending this work to create compartmentalized autocatalytic systems that display heritable variation and could enable a chemical analogue of natural selection. To this end, we have been investigating whether more complex compartmentalized autocatalytic systems can display multiple stable states in the same environment (multistability), offering multiple heritable states that can be switched (analogous to mutation) by environmental perturbations. Several promising systems are now being evaluated in bulk and in emulsion droplets. We are collaborating to use the data from these experiments to guide generative chemistry approaches for characterizing these novel autocatalytic systems. Experimental collaborations were also established between partners to study the dynamics of -helical replicator oscillations in water-in-oil compartmentalized environments.
One of the key tools for our theoretical investigations is generative chemistry, whereby one can build up complex chemical networks computationally. We have created a complex, smooth workflow. The networks are generated by MØD algorithm. We have created a web interface that allows chemists to decide on the acceptability of reactions and visualisation of reaction networks. Subnetwork generation is a means to identify minimal (or subminimal) reaction network to find pathways from a given set of molecules (e.g. the initial set) to another set (e.g. the detected molecules). We are applying these methods to the experimentally investigated formose reaction (see above) and its possible extensions with other, preferably autocatalytic subnetworks. We can perform Monte-Carlo type dynamical analysis on the probability of coexistence of the proposed reaction networks. We have been approaching a reciprocal interaction between experiment and theory. In another collaborative line we have developed the population biological and the statistical physical model of the “mutation” rates in the assembly-driven -sheet replicators.
