Are There Biomimetic Lessons from Genetic Regulatory Networks for Developing a Lunar Industrial Ecology?

We examine the prospect for employing a bio-inspired architecture for a lunar industrial ecology based on genetic regulatory networks. The lunar industrial ecology resembles a metabolic system in that it comprises multiple chemical processes interlinked through waste recycling. Initially, we examine lessons from factory organisation which have evolved into a bio-inspired concept, the reconfigurable holonic architecture. We then examine genetic regulatory networks and their application in the biological cell cycle. There are numerous subtleties that would be challenging to implement in a lunar industrial ecology but much of the essence of biological circuitry (as implemented in synthetic biology, for example) is captured by traditional electrical engineering design with emphasis on feedforward and feedback loops to implement robustness.

Dimensionality reduction and data integration for scRNA-seq data based on integrative hierarchical Poisson factorisation

Single-cell RNA sequencing (scRNA-seq) data sets consist of high-dimensional, sparse and noisy feature vectors, and pose a challenge for classic methods for dimensionality reduction. We show that application of Hierarchical Poisson Factorisation (HPF) to scRNA-seq data produces robust factors, and outperforms other popular methods. To account for batch variability in composite data sets, we introduce Integrative Hierarchical Poisson Factorisation (IHPF), an extension of HPF that makes use of a noise ratio hyper-parameter to tune the variability attributed to technical (batches) vs. biological (cell phenotypes) sources. We exemplify the advantageous application of IHPF under data integration scenarios with varying alignments of technical noise and cell diversity, and show that IHPF produces latent factors with a dual block structure in both cell and gene spaces for enhanced biological interpretability.

Research Progress of Chitosan-Based Biomimetic Materials

Chitosan is a linear polysaccharide produced by deacetylation of natural biopolymer chitin. Owing to its good biocompatibility and biodegradability, non-toxicity, and easy processing, it has been widely used in many fields. After billions of years of survival of the fittest, many organisms have already evolved a nearly perfect structure. This paper reviews the research status of biomimetic functional materials that use chitosan as a matrix material to mimic the biological characteristics of bivalves, biological cell matrices, desert beetles, and honeycomb structure of bees. In addition, the application of biomimetic materials in wound healing, hemostasis, drug delivery, and smart materials is briefly overviewed according to their characteristics of adhesion, hemostasis, release, and adsorption. It also discusses prospects for their application and provides a reference for further research and development.

Porous Polymers from High Internal Phase Emulsions as Scaffolds for Biological Applications

High internal phase emulsions (HIPEs), with densely packed droplets of internal phase and monomers dispersed in the continuous phase, are now an established medium for porous polymer preparation (polyHIPEs). The ability to influence the pore size and interconnectivity, together with the process scalability and a wide spectrum of possible chemistries are important advantages of polyHIPEs. In this review, the focus on the biomedical applications of polyHIPEs is emphasised, in particular the applications of polyHIPEs as scaffolds/supports for biological cell growth, proliferation and tissue (re)generation. An overview of the polyHIPE preparation methodology is given and possibilities of morphology tuning are outlined. In the continuation, polyHIPEs with different chemistries and their interaction with biological systems are described. A further focus is given to combined techniques and advanced applications.

Printing a Pacinian Corpuscle: Modeling and Performance

The Pacinian corpuscle is a highly sensitive mammalian sensor cell that exhibits a unique band-pass sensitivity to vibrations. The cell achieves this band-pass response through the use of 20 to 70 elastic layers entrapping layers of viscous fluid. This paper develops and explores a scalable mechanical model of the Pacinian corpuscle and uses the model to predict the response of synthetic corpuscles, which could be the basis for future vibration sensors. The −3dB point of the biological cell is accurately mimicked using the geometries and materials available with off-the-shelf 3D printers. The artificial corpuscles here are constructed using uncured photoresist within structures printed in a commercial stereolithography (SLA) 3D printer, allowing the creation of trapped fluid layers analogous to the biological cell. Multi-layer artificial Pacinian corpuscles are vibration tested over the range of 20–3000 Hz and the response is in good agreement with the model.

SETS: A Seed-Dense-Expanding Model-Based Topological Structure for the Prediction of Overlapping Protein Complexes

Defining protein complexes by analysing the protein–protein interaction (PPI) networks is a crucial task in understanding the principles of a biological cell. In the last few decades, researchers have proposed numerous methods to explore the topological structure of a PPI network to detect dense protein complexes. In this paper, the overlapping protein complexes with different densities are predicted within an acceptable execution time using seed expanding model and topological structure of the PPI network (SETS). SETS depend on the relation between the seed and its neighbours. The algorithm was compared with six algorithms on six datasets: five for yeast and one for human. The results showed that SETS outperformed other algorithms in terms of F-measure, coverage rate and the number of complexes that have high similarity with real complexes.