Chemoadaptive Polymeric Assemblies by Integrated Chemical Feedback in Self-Assembled Synthetic Protocells

Artificial cell synthesis using biocatalytic polymerization-induced self-assembly

Artificial cells are biomimetic microstructures that mimic functions of natural cells, can be applied as building blocks for molecular systems engineering, and host synthetic biology pathways. Here we report enzymatically synthesized polymer-based artificial cells with the ability to express proteins. Artificial cells were synthesized using biocatalytic atom transfer radical polymerization-induced self-assembly, in which myoglobin synthesizes amphiphilic block co-polymers that self-assemble into structures such as micelles, worm-like micelles, polymersomes and giant unilamellar vesicles (GUVs). The GUVs encapsulate cargo during the polymerization, including enzymes, nanoparticles, microparticles, plasmids and cell lysate. The resulting artificial cells act as microreactors for enzymatic reactions and for osteoblast-inspired biomineralization. Moreover, they can express proteins such as a fluorescent protein and actin when fed with amino acids. Actin polymerizes in the vesicles and alters the artificial cells' internal structure by creating internal compartments. Thus, biocatalytic atom transfer radical polymerization-induced self-assembly-derived GUVs can mimic bacteria as they are composed of a microscopic reaction compartment that contains genetic information for protein expression upon induction.

Degradable Block Copolymer Nanoparticles Synthesized by Polymerization-Induced Self-Assembly

Polymerization-induced self-assembly (PISA) combines polymerization and in situ self-assembly of block copolymers in one system and has become a widely used method to prepare block copolymer nanoparticles at high concentrations. The persistence of polymers in the environment poses a huge threat to the ecosystem and represents a significant waste of resources. There is an urgent need to develop novel chemical approaches to synthesize degradable polymers. To meet with this demand, it is crucial to install degradability into PISA nanoparticles. Most recently, degradable PISA nanoparticles have been synthesized by introducing degradation mechanisms into either shell-forming or core-forming blocks. This Minireview summarizes the development in degradable block copolymer nanoparticles synthesized by PISA, including shell-degradable, core-degradable, and all-degradable nanoparticles. Future development will benefit from expansion of polymerization techniques with new degradation mechanisms and adaptation of high-throughput approaches for both PISA syntheses and degradation studies.

Adaptive and Dissipative Hierarchical Population Crowding of Synthetic Protocells through Click-PISA under Gradient Energy Inputs

The ability of living objects to respond rapidly en masse to various stimuli or stress is an important function in response to externally applied changes in the local environment. This occurs across many length scales, for instance, bacteria swarming in response to different stimuli or stress and macromolecular crowding within cells. Currently there are few mechanisms to induce similar autonomous behaviors within populations of synthetic protocells. Herein, we report a system in which populations of individual objects behave in a coordinated manner in response to changes in the energetic environment by the emergent self-organization of large object swarms. These swarms contain protocell populations of approximately 60 000 individuals. We demonstrate the dissipative nature of the hierarchical constructs, which persist under appropriate UV stimulation. Finally, we identify the ability of the object populations to change behaviors in an adaptive population-wide response to the local energetic environment.

Triggered Polymersome Fusion

The contents of biological cells are retained within compartments formed of phospholipid membranes. The movement of material within and between cells is often mediated by the fusion of phospholipid membranes, which allows mixing of contents or excretion of material into the surrounding environment. Biological membrane fusion is a highly regulated process that is catalyzed by proteins and often triggered by cellular signaling. In contrast, the controlled fusion of polymer-based membranes is largely unexplored, despite the potential application of this process in nanomedicine, smart materials, and reagent trafficking. Here, we demonstrate triggered polymersome fusion. Out-of-equilibrium polymersomes were formed by ring-opening metathesis polymerization-induced self-assembly and persist until a specific chemical signal (pH change) triggers their fusion. Characterization of polymersomes was performed by a variety of techniques, including dynamic light scattering, dry-state/cryogenic-transmission electron microscopy, and small-angle X-ray scattering (SAXS). The fusion process was followed by time-resolved SAXS analysis. Developing elementary methods of communication between polymersomes, such as fusion, will prove essential for emulating life-like behaviors in synthetic nanotechnology.

Photoswitchable gating of non-equilibrium enzymatic feedback in chemically communicating polymersome nanoreactors

The circadian rhythm generates out-of-equilibrium metabolite oscillations that are controlled by feedback loops under light/dark cycles. Here we describe a non-equilibrium nanosystem comprising a binary population of enzyme-containing polymersomes capable of light-gated chemical communication, controllable feedback and coupling to macroscopic oscillations. The populations consist of esterase-containing polymersomes functionalized with photo-responsive donor-acceptor Stenhouse adducts (DASA) and light-insensitive semipermeable urease-loaded polymersomes. The DASA-polymersome membrane becomes permeable under green light, switching on esterase activity and decreasing the pH, which in turn initiates the production of alkali in the urease-containing population. A pH-sensitive pigment that absorbs green light when protonated provides a negative feedback loop for deactivating the DASA-polymersomes. Simultaneously, increased alkali production deprotonates the pigment, reactivating esterase activity by opening the membrane gate. We utilize light-mediated fluctuations of pH to perform non-equilibrium communication between the nanoreactors and use the feedback loops to induce work as chemomechanical swelling/deswelling oscillations in a crosslinked hydrogel. We envision possible applications in artificial organelles, protocells and soft robotics.

Thermodynamic State Machine Network

We describe a model system—a thermodynamic state machine network—comprising a network of probabilistic, stateful automata that equilibrate according to Boltzmann statistics, exchange codes over unweighted bi-directional edges, update a state transition memory to learn transitions between network ground states, and minimize an action associated with fluctuation trajectories. The model is grounded in four postulates concerning self-organizing, open thermodynamic systems—transport-driven self-organization, scale-integration, input-functionalization, and active equilibration. After sufficient exposure to periodically changing inputs, a diffusive-to-mechanistic phase transition emerges in the network dynamics. The evolved networks show spatial and temporal structures that look much like spiking neural networks, although no such structures were incorporated into the model. Our main contribution is the articulation of the postulates, the development of a thermodynamically motivated methodology addressing them, and the resulting phase transition. As with other machine learning methods, the model is limited by its scalability, generality, and temporality. We use limitations to motivate the development of thermodynamic computers—engineered, thermodynamically self-organizing systems—and comment on efforts to realize them in the context of this work. We offer a different philosophical perspective, thermodynamicalism, addressing the limitations of the model and machine learning in general.