Natural selection in compartmentalized environment with reshuffling.

The emerging field of high-throughput compartmentalized in vitro evolution is a promising new approach to protein engineering. In these experiments, libraries of mutant genotypes are randomly distributed and expressed in microscopic compartments-droplets of an emulsion. The selection of desirable variants is performed according to the phenotype of each compartment. The random partitioning leads to a fraction of compartments receiving more than one genotype making the whole process a lab implementation of the group selection. From a practical point of view (where efficient selection is typically sought), it is important to know the impact of the increase in the mean occupancy of compartments on the selection efficiency. We carried out a theoretical investigation of this problem in the context of selection dynamics for an infinite non-mutating subdivided population that randomly colonizes an infinite number of patches (compartments) at each reproduction cycle. We derive here an update equation for any distribution of phenotypes and any value of the mean occupancy. Using this result, we demonstrate that, for the linear additive fitness, the best genotype is still selected regardless of the mean occupancy. Furthermore, the selection process is remarkably resilient to the presence of multiple genotypes per compartments, and slows down approximately inversely proportional to the mean occupancy at high values. We extend out results to more general expressions that cover nonadditive and non-linear fitnesses, as well non-Poissonian distribution among compartments. Our conclusions may also apply to natural genetic compartmentalized replicators, such as viruses or early trans-acting RNA replicators.

Microscopic agents programmed by DNA circuits.

Information stored in synthetic nucleic acids sequences can be used in vitro to create complex reaction networks with precisely programmed chemical dynamics. Here, we scale up this approach to program networks of microscopic particles (agents) dispersed in an enzymatic solution. Agents may possess multiple stable states, thus maintaining a memory and communicate by emitting various orthogonal chemical signals, while also sensing the behaviour of neighbouring agents. Using this approach, we can produce collective behaviours involving thousands of agents, for example retrieving information over long distances or creating spatial patterns. Our systems recapitulate some fundamental mechanisms of distributed decision making and morphogenesis among living organisms and could find applications in cases where many individual clues need to be combined to reach a decision, for example in molecular diagnostics.

Pursuit-and-Evasion Reaction-Diffusion Waves in Microreactors with Tailored Geometry.

Out-of-equilibrium chemical systems may self-organize into structures displaying spatiotemporal order, such as traveling waves and Turing patterns. Because of its predictable chemistry, DNA has recently appeared as an interesting candidate to engineer these spatiotemporal structures. However, in addition to the intrinsic chemical parameters, initial and boundary conditions have a major impact on the final structure. Here we take advantage of microfluidics to design controlled reactors and investigate pursuit-and-evasion chemical waves generated by a DNA-based reaction network with Predator-Prey dynamics. We first propose two complementary microfabrication strategies to either control the initial condition or the two-dimensional geometry of the reactor where the waves develop. We subsequently use them to investigate the effect of curvature in wave propagation. We finally show that DNA-based waves can compute the optimal path within a maze. We thus suggest that coupling configurable microfluidics to programmable DNA-based dissipative reaction networks is a powerful route to investigate spatiotemporal order formation in chemistry.