Daniel Rizzo

Predictive modelling-based design and experiments for synthesis and spinning of bioinspired silk fibres

Scalable computational modelling tools are required to guide the rational design of complex hierarchical materials with predictable functions. Here, we utilize mesoscopic modelling, integrated with genetic block copolymer synthesis and bioinspired spinning process, to demonstrate de novo materials design that incorporates chemistry, processing and material characterization. We find that intermediate hydrophobic/hydrophilic block ratios observed in natural spider silks and longer chain lengths lead to outstanding silk fibre formation. This design by nature is based on the optimal combination of protein solubility, self-assembled aggregate size and polymer network topology. The original homogeneous network structure becomes heterogeneous after spinning, enhancing the anisotropic network connectivity along the shear flow direction. Extending beyond the classical polymer theory, with insights from the percolation network model, we illustrate the direct proportionality between network conductance and fibre Youngs modulus. This integrated approach provides a general path towards de novo functional network materials with enhanced mechanical properties and beyond (optical, electrical or thermal) as we have experimentally verified.

Effect of Sequence Features on Assembly of Spider Silk Block Copolymers

Bioengineered spider silk block copolymers were studied to understand the effect of protein chain length and sequence chemistry on the formation of secondary structure and materials assembly. Using a combination of in vitro protein design and assembly studies, we demonstrate that silk block copolymers possessing multiple repetitive units self-assemble into lamellar microstructures. Additionally, the study provides insights into the assembly behavior of spider silk block copolymers in concentrated salt solutions.

Neuronal growth as diffusion in an effective potential.

Current understanding of neuronal growth is mostly qualitative, as the staggering number of physical and chemical guidance cues involved prohibit a fully quantitative description of axonal dynamics. We report on a general approach that describes axonal growth in vitro, on poly-D-lysine-coated glass substrates, as diffusion in an effective external potential, representing the collective contribution of all causal influences on the growth cone. We use this approach to obtain effective growth rules that reveal an emergent regulatory mechanism for axonal pathfinding on these substrates.