Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Pearce S

PROVIDER: S-EPMC10906081 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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

Pearce Samuel S Lin Chenyu C Pérez-Mercader Juan J

Nano letters 20240219 8

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 population ...[more]

PMID: 38373157

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Project description:BackgroundProtocell refers to the basic unit of life and synthetic molecular assembly with cell structure and function. The protocells have great applications in the field of biomedical technology. Simulating the morphology and function of cells is the key to the preparation of protocells. However, some organic solvents used in the preparation process of protocells would damage the function of the bioactive substance. Perfluorocarbon, which has no toxic effect on bioactive substances, is an ideal solvent for protocell preparation. However, perfluorocarbon cannot be emulsified with water because of its inertia.MethodsSpheroids can be formed in nature even without emulsification, since liquid can reshape the morphology of the solid phase through the scouring action, even if there is no stable interface between the two phases. Inspired by the formation of natural spheroids such as pebbles, we developed non-interfacial self-assembly (NISA) of microdroplets as a step toward synthetic protocells, in which the inert perfluorocarbon was utilized to reshape the hydrogel through the scouring action.ResultsThe synthetic protocells were successfully obtained by using NISA-based protocell techniques, with the morphology very similar to native cells. Then we simulated the cell transcription process in the synthetic protocell and used the protocell as an mRNA carrier to transfect 293T cells. The results showed that protocells delivered mRNAs, and successfully expressed proteins in 293T cells. Further, we used the NISA method to fabricate an artificial cell by extracting and reassembling the membrane, proteins, and genomes of ovarian cancer cells. The results showed that the recombination of tumor cells was successfully achieved with similar morphology as tumor cells. In addition, the synthetic protocell prepared by the NISA method was used to reverse cancer chemoresistance by restoring cellular calcium homeostasis, which verified the application value of the synthetic protocell as a drug carrier.ConclusionThis synthetic protocell fabricated by the NISA method simulates the occurrence and development process of primitive life, which has great potential application value in mRNA vaccine, cancer immunotherapy, and drug delivery.

Project description:Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing task, called the balance beam problem. Previous simulations of this developmental task do not reflect a necessary premise underlying development: a more complex structure can be built out of less complex ones, while ensuring that the more complex structure does not replace the less complex one. In order to address this necessity, we segregated the input set by subsets of increasing Orders of Hierarchical Complexity. This is a complexity measure that has been extensively shown to underlie the complexity behavior and hypothesized to underlie the complexity of the neural structure of the brain. After segregating the input set, minimal neural network models were trained separately for each input subset, and adjacent complexity models were analyzed sequentially to observe whether there was a structural progression. Results show that three different network structural progressions were found, performing with similar accuracy, pointing towards self-organization. Also, more complex structures could be built out of less complex ones without substituting them, successfully addressing catastrophic forgetting and leveraging performance of previous models in the literature. Furthermore, the model structures trained on the two highest complexity subsets performed better than simulations of the balance beam present in the literature. As a major contribution, this work was successful in addressing hierarchical complexity structural growth in neural networks, and is the first that segregates inputs by Order of Hierarchical Complexity. Since this measure can be applied to all domains of data, the present method can be applied to future simulations, systematizing the simulation of developmental and evolutionary structural growth in neural networks.

Dataset Information

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

Publications

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

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