Biological Systems Research Articles

Thermoregulatory integration in hand prostheses and humanoid robots through blood vessel simulation

In this paper, we introduce an innovative approach for generating robotic faces with a thermal signature similar to that of humans and equipping prosthetic or robotic hands with a lifelike temperature distribution. This approach enhances their detection via infrared cameras and promotes more natural interactions between humans and robots. This method integrates a temperature regulation system into artificial skin, drawing inspiration from the human body’s natural temperature control via blood flow. Central to this technique is a fiber network simulating blood vessels within the artificial skin. Water flows through these fibers under specific temperature and flow conditions, forming a controlled heat release system. The heat emission can be adjusted by changing the dilation of these fibers, primarily by modulating the frequency of circulation. Our findings indicate that this approach can replicate the varied thermal characteristics of different human faces and hand areas. Consequently, the robotic faces appear more human-like in infrared images, aiding their identification by infrared cameras. At the same time, the prosthetic hands achieve a more natural temperature, reducing the discomfort typically felt in direct contact with synthetic limbs. The aim of this study was to address the challenges faced by the users of prosthetic hands. The results from this study show a promising direction in humanoid robotics, fostering improved tactile interactions and redefining human–robot relationships. This innovative technique facilitates further advancements, blurring the lines between artificial aids and natural biological systems.

Drug-loaded nanoparticles for cancer therapy: a high-throughput multicellular agent-based modeling study.

Interactions between biological systems and engineered nanomaterials have become an important area of study due to the application of nanomaterials in medicine. In particular, the application of nanomaterials for cancer diagnosis or treatment presents a challenging opportunity due to the complex biology of this disease spanning multiple time and spatial scales. A system-level analysis would benefit from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated parameters driving this system and a patient's overall response. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, "inheritance" of NPs by daughter cells at cell division, cell pharmacodynamic response to the intracellular drug, and overall drug effect on tumor dynamics. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on the tumor dynamics. In particular, through the exploration of NP "inheritance" at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for cytotoxic chemotherapy. Moreover, smaller dosage of cytostatic chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by "heritable" NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.

Biomimetic Exploration and Reflection on Switchable Coordination and Narrow-Band Electrofluorochromic Devices.

Electrofluorochromic (EFC) materials and devices with controllable fluorescence properties show great application potential in advanced anticounterfeiting, information storage and display. However, the low color purity caused by the broad emission spectra and underperforming switching time of the existing EFC materials limit their application. Through biomimetic exploration and the study of reversible electrochemical responsive coordination reactions, boron-nitrogen embedded polyaromatics (B,N-PAHs) with narrow-band emission and high color purity have been successfully integrated into EFC systems, which also help to better understand the role of boron in biological activity. The EFC device achieve good performance containing quenching efficiency greater than 90% within short switching time (ton: 0.6s, toff: 2.4s), and nearly no performance change after 200 cycles test. Three primary color (red, green, and blue) EFC devices are successfully prepared. In addition, new phenomena are obtained and discussed in this biomimetic exploration of related boron reactions. The success and harvest of this exploration are expected to provide new ideas for optimizing properties and broadening applications of EFC materials. Moreover, it may provide ideas and reference significance for further exploring and understanding the function of boron compounds in biological systems.

Refining computational inference of gene regulatory networks: integrating knockout data within a multi-task framework.

Constructing accurate gene regulatory network s (GRNs), which reflect the dynamic governing process between genes, is critical to understanding the diverse cellular process and unveiling the complexities in biological systems. With the development of computer sciences, computational-based approaches have been applied to the GRNs inference task. However, current methodologies face challenges in effectively utilizing existing topological information and prior knowledge of gene regulatory relationships, hindering the comprehensive understanding and accurate reconstruction of GRNs. In response, we propose a novel graph neural network (GNN)-based Multi-Task Learning framework for GRN reconstruction, namely MTLGRN. Specifically, we first encode the gene promoter sequences and the gene biological features and concatenate the corresponding feature representations. Then, we construct a multi-task learning framework including GRN reconstruction, Gene knockout predict, and Gene expression matrix reconstruction. With joint training, MTLGRN can optimize the gene latent representations by integrating gene knockout information, promoter characteristics, and other biological attributes. Extensive experimental results demonstrate superior performance compared with state-of-the-art baselines on the GRN reconstruction task, efficiently leveraging biological knowledge and comprehensively understanding the gene regulatory relationships. MTLGRN also pioneered attempts to simulate gene knockouts on bulk data by incorporating gene knockout information.

Jellyfish-Inspired Visual and Sensory Bubbling Robots with Automatic 3D Morphable Films for Underwater Environmental Interactions.

Coelenterates, such as Atolla jellyfish, are capable of integrating color, communication, and motion in a sophisticated manner, thereby enabling them to function as intelligent biological systems that can adapt to the challenges of the underwater environment. Extensive efforts have been dedicated to exploiting underwater visual, sensory, actuating, or combined systems. However, current biomimetic soft systems are still limited by the lack of comprehensive, integrated functional skins that can automatically deform, dynamically sense, and further send color signals when diving into underwater conditions. Here, we propose the synthetic soft skins composed of assembled entangled carbon nanotube networks and fluorescent unit-embedded elastomers which can be applied in a suspended form to allow autonomic 3D deformation, real-time perception, and dynamic fluorescence color transformation. The capabilities of the sensory and color display thresholds were controlled through the entanglement density of carbon nanotubes and the suspended area. As a demonstration, the soft thin skin was integrated into an artificial jellyfish robot, enabling the realization of a closed-loop feedback system for dynamic sensory processing, signal processing, and further 3D morphing-induced fluorescent color change, demonstrating significant potentials in underwater visual display, danger warning, and environmental exploration.

The BioRECIPE Knowledge Representation Format.

The BioRECIPE (Biological system Representation for Evaluation, Curation, Interoperability, Preserving, and Execution) knowledge representation format was introduced to standardize and facilitate human-machine interaction while creating, verifying, evaluating, curating, and expanding executable models of intra- and intercellular signaling. This format allows a human user to easily preview and modify any model component, while it is at the same time readable by machines and can be processed by a suite of model development and analysis tools. The BioRECIPE format is compatible with multiple representation formats, natural language processing tools, modeling tools, and databases that are used by the systems and synthetic biology communities.

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data.

Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer linkage prediction, and enhancer-gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relations associated with pseudotime, as well as key TF-enhancer-gene relations and TF cooperation underlying tumor cells.

Nonadiabatic Exciton Dynamics and Energy Gradients in the Framework of FMO-LC-TDDFTB.

We introduce a novel methodology for simulating the excited-state dynamics of extensive molecular aggregates in the framework of the long-range corrected time-dependent density-functional tight-binding fragment molecular orbital method (FMO-LC-TDDFTB) combined with the mean-field Ehrenfest method. The electronic structure of the system is described in a quasi-diabatic basis composed of locally excited and charge-transfer states of all fragments. In order to carry out nonadiabatic molecular dynamics simulations, we derive and implement the excited-state gradients of the locally excited and charge-transfer states. Subsequently, the accuracy of the analytical excited-state gradients is evaluated. The applicability to the simulation of exciton transport in organic semiconductors is illustrated on a large cluster of anthracene molecules. Additionally, nonadiabatic molecular dynamics simulations of a model system of benzothieno-benzothiophene molecules highlight the method's utility in studying charge-transfer dynamics in organic materials. Our new methodology will facilitate the investigation of excitonic transfer in extensive biological systems, nanomaterials, and other complex molecular systems consisting of thousands of atoms.

Analyzing antimicrobial activity of ZnO/FTO, Sn-Cu-doped ZnO/FTO thin films: Production and characterizations.

In the developing field of nanotechnology, ZnO (zinc oxide) based semiconductor samples have emerged as the foremost choice due to their immense potential for advancing the development of cutting-edge nanodevices. Due to its excellent chemical stability, low cost, and non-toxicity to biological systems, it is also utilized in various investigations. In this study, the successive ionic layer adsorption and reaction (SILAR) method was used to generate FTO (fluorine-doped tin oxide)/ZnO, and tin (Sn)-copper (Cu)-doped ZnO thin films at varying concentrations on FTO substrates. After being stacked 40 times in varying concentrations on the FTO substrate, FTO/ZnO thin films and Sn-Cu-doped thin films were annealed at 300°C. Using Scanning Electron Microscopy (SEM) Energy Dispersive Spectroscopy-(EDS), the agar diffusion test, and the viability cell counting method, the minimum inhibitory concentration, structural properties, surface morphology, antibacterial properties, bacterial adhesion, and survival organism count of FTO/ZnO thin films and Sn-Cu-doped thin films were investigated. Both doped and FTO/ZnO films with varying Sn-Cu concentrations expanded harmonically on the FTO substrate, according to the SEM-EDS investigation. The doping concentration affected their morphological properties, causing changes depending on the doping level. Antibacterial activity was observed in the powder metals, but no antibacterial activity was found in the thin film form. The highest adhesion rate of bacterial organisms on the produced samples was observed when the FTO/ZnO/Sn-Cu doping rate was 1%. In addition, the lowest adhesion rate was observed when the FTO/ZnO/Sn-Cu additive ratio was 3%. RESEARCH HIGHLIGHTS: ZnO based semiconductors highlight significant potential in advancing nanodevice technology due to their chemical stability, cost-effectiveness, and biocompatibility. Employing the SILAR method, the study innovatively fabricates FTO/ZnO and Sn-Cu-doped ZnO thin films on FTO substrates, exploring a novel approach in semiconductor manufacturing. Post annealing at 300°C, the research examines the structural and surface morphological changes in the films, contributing to the understanding of semiconductor behavior under varying conditions. The study delves into the antibacterial properties of ZnO thin films, offering insights into the potential biomedical applications of these materials. SEM-EDS analysis reveals that doping concentrations crucially influence the morphological properties of ZnO thin films, shedding light on the optimization of semiconductor performance. Findings indicate a specific doping rate (1% Sn-Cu) enhances bacterial adhesion, while a 3% additive ratio minimizes it, suggesting implications for biomedical device engineering and antibacterial surface design.

Commands of Synthetic Biology to Modernize and Re-design the Biological Systems

The scope of synthetic biology continues to expand and has encompassed a huge number of biological features. Its scope starts from scratch, enabling the de novo synthesis of biological systems. It has re-designed the biological systems and empowered the production of synthetic genes, RNA, DNA and proteins by undertaking the control of pathways involved in genetic regulation. It has increased the production of nano-scale RNA architectures and synthetic biological circuits which either have therapeutic or other productive uses. Furthermore, advancements in synthetic biology have enabled the generation of diversity through methods such as epPCR and site-directed mutagenesis, allowing for the creation of complex genetic variations. Additionally, synthetic biology intersects with computer engineering to design functional biological devices and circuits, utilizing computational analysis to guide the design process. Moreover, ethical and regulatory considerations are paramount in synthetic biology, with careful examination required to address dual-use concerns, environmental impacts, and issues of social justice and equitable access to benefits. As synthetic biology continues to advance, it presents opportunities to address pressing challenges in fields ranging from medicine and agriculture to environmental conservation and beyond. Thus, the fusion of synthetic biology with other scientific disciplines holds promise for transformative innovation and societal benefit. The present discussion enlightened the core of generating complex biological systems and has given a brief overview on the fusion of synthetic biology with other fields of science.Keywords: Biological Systems; Genetic Regulation; Synthetic biology circuits

Biological Upcycling of Plastics Waste.

Plastic wastes accumulate in the environment, impacting wildlife and human health and representing a significant pool of inexpensive waste carbon that could form feedstock for the sustainable production of commodity chemicals, monomers, and specialty chemicals. Current mechanical recycling technologies are not economically attractive due to the lower-quality plastics that are produced in each iteration. Thus, the development of a plastics economy requires a solution that can deconstruct plastics and generate value from the deconstruction products. Biological systems can provide such value by allowing for the processing of mixed plastics waste streams via enzymatic specificity and using engineered metabolic pathways to produce upcycling targets. We focus on the use of biological systems for waste plastics deconstruction and upcycling. We highlight documented and predicted mechanisms through which plastics are biologically deconstructed and assimilated and provide examples of upcycled products from biological systems. Additionally, we detail current challenges in the field, including the discovery and development of microorganisms and enzymes for deconstructing non-polyethylene terephthalate plastics, the selection of appropriate target molecules to incentivize development of a plastic bioeconomy, and the selection of microbial chassis for the valorization of deconstruction products.

Synthesis and Development of Inverse-Type Nucleoside Diarylethene Photoswitches.

Nucleosidic diarylethenes (DAEs) have evolved from an emerging class of photochromes into a well-established option for integrating photochromic functionalities into biological systems. However, a comprehensive understanding of how chemical structure influences their photochromic properties remains essential. While structural features, such as an inverse connection between the aryl residues and the ethene bridge, are well-documented for classical DAEs, their application to nucleosidic DAEs has been underexplored. In this study, we address this gap by developing three distinct types of inverse nucleosidic DAEs - semi-inverse thiophenes, semi-inverse uridines and inverse uridines. We successfully synthesized these compounds and conducted comprehensive analyses of their photostationary states, thermal stability, reversibility, and reaction quantum yields. Additionally, we conducted an in-depth comparison of their photochromic properties with those of their normal-type counterparts. Among the synthesized compounds, seven semi-inverse thiophenes exhibited the most promising characteristics. Notably, these compounds demonstrated excellent fatigue resistance, with up to 96% retention of photochromic activity over 40 switching cycles, surpassing the performance of all comparable nucleosidic DAEs reported to date. These findings hold significant promise for future applications in various fields.

Electromagnetic Fields Trigger Cell Death in Glioblastoma Cells through Increasing miR-126-5p and Intracellular Ca2+ Levels.

Electromagnetic fields create potential negative implications on biological systems, including modifications to DNA structure, nuclear condensation, cellular ion transport, and intracellular Ca2+ accumulation. To explore these effects on cancer cells, we exposed prostate, glioblastoma and cervix cancer cell lines to electromagnetic fields of wireless and assessed its anti-proliferative effects. PC3, A172, and HeLa cancer cells were cultured and exposed to electromagnetic fields for 24, 48, and 72 h. We used the MTT assay to detect cell viability and proliferation, Annexin V staining to determine apoptotic cells, and confocal microscopy to measure apoptosis-mediated intracellular calcium signals. Additionally, we performed profiling for apoptosis-related miRNAs. The results indicated that the electromagnetic field triggers apoptosis in the glioblastoma cell line A172 by increasing level of miR-129-5p, a known tumor suppressor. In contrast, the cervix cancer cell line and the prostate cancer cell line remained largely unaffected. In summary, our investigation underscores that electromagnetic fields at a 2.4 GHz frequency may adversely affect certain cancer cell lines, notably triggering apoptosis in the glioblastoma cancer cell line.

IN SILICO TRIAL APPROACHES BETWEEN PHYTOCHEMICAL COMPOSITION OF VERBENA OFFICINALIS AND LIVER CANCER TARGETS

Objective: The abundance of bioactive metabolites in Verbena officinalis explains the biological benefits and folkloric use of the plant. Liver cancer is an extremely heterogeneous malignant disease compared to other defined tumors. To explore the potential therapeutic value of bioactive metabolites in Verbena officinalis, this study aimed to filter secondary metabolites, conduct ADME-Tox assessments, perform drug similarity tests, and analyze with molecular dynamic simulations. The objective was to evaluate how potential drug candidates derived from Verbena officinalis behave in biological systems and assess their potential toxicity risks. Material and Method: Ligands selected from the ADME assay were utilized in in silico molecular docking studies against Glucose-6-phosphate dehydrogenase enzyme in the oxidative part of the pentose phosphate pathway, which is crucial for liver diseases. These studies were conducted using Autodock Vina embedded in Chimera 1.16. Molecular dynamics simulations were performed with the AMBER16. Result and Discussion: When the ADME test results were evaluated, 88 secondary metabolites were identified as ligands. Among all the ligands evaluated against Glucose-6-phosphate dehydrogenase enzyme, which is the key enzyme of the pentose phosphate pathway, quercetin flavonoid was determined to be the most active ligand with a docking score of -8.1 kcal/mol and binding energy of -118.51 kcal/mol. A molecular dynamics simulation performed for 300 nanoseconds confirmed that quercetin can remain stable in its microenvironment. The activity of this metabolite is worthy of further testing in vitro and in vivo as it may highlight a therapeutic modality within the pentose phosphate pathway.

Novel synthesis of thermoresponsive single-walled carbon nanotubes/poly(N-isopropylacrylamide) hybrids

Poly(N-isopropylacrylamide) (PNIPAM) is one of the most well studied thermoresponsive polymers and its microgels undergo a reversible volume phase transition (VPT) at temperatures close to that of the human body. Coupling this property with the unique optical properties of single-walled carbon nanotubes (SWCNT) in the near infrared (NIR) leads to interesting nanohybrids that could be used as multi-responsive sensors/actuators for biological applications.We show the synthesis of thermoresponsive SWCNT/PNIPAM hybrids using two different non-covalent strategies, preserving the nanotube optical properties such as fluorescence. The first strategy involves the dispersion of the SWCNT in water with sodium dodecyl sulfate (SDS), and subsequent in situ synthesis of PNIPAM microgels inside the SDS coatings around the nanotubes. The second strategy starts with modifying the nanotubes with a pyrene derivative, which in turn is used as the starting point for the in situ polymerization of the PNIPAM microgels, thus ensuring that the polymer grows around the nanotubes. In both cases, we obtain hybrids showing a phase transition at temperatures close to that of the human body, with the absorption and fluorescence spectra of the hybrids in the NIR changing in response to the changing dielectric environment. These systems could be used as actuators/sensors in biological systems.

Gradient Hierarchically Porous Ionic-Junction Fibers of Wet-Spun Carboxymethyl Cellulose Coagulated with Copper Sulfate.

Polyelectrolyte-based ionic-junction fibers newly serve as signal transmission and translation media between electronic devices and biological systems, facilitating ion transport within organic matrices. In this work, we fabricated gel filaments of carboxymethyl cellulose (CMC) chelated with Cu(II) ions through wet-spinning, using a saturated coagulant of CuSO4. Interestingly, the as-spun fibers exhibited dramatic 3D porous frameworks that varied with the temperature and precursor concentration. At 20 °C, the Cu(II) chelation networks favored the formation of well-organized cellular chambers or corrugated channels, displaying dense stacking patterns. However, critical transitions from cellular chambers to corrugated channels occurred at precursor dope concentrations of approximately 2 and 7 wt %, with the porous structure diminishing beyond 8 wt %. We have proposed schematic diagrams to mimic the 3D pore structure, dense porous stacking, and formation mechanism, according to electronic micrographs. Our investigations revealed that the distinct ion-junction channels or chambers are under the control of axial drawing extension as well as the outside-inside penetration of Cu(II) ions into the dope and inside-outside diffusion of water into coagulants. Therefore, controlling the metal chelation-water diffusion process at specific temperatures and concentrations will offer valuable insights for tailoring ionic-junction soft filaments with gradient hierarchically porous structures and shape memory properties.

Droplet Electrophoresis with Internal Free Ions: Effect of Permittivity Changes in the Electric Double Layer.

Droplet electrophoresis (EP) is of interest in biological systems, microfluidics, and separation techniques. We investigate EP of an oil droplet that contains free ions and is stabilized in an electrolyte solution through an amphoteric surfactant. The presence of mobile ions within the droplet leads to the creation of a distinct nonzero space charge density inside the droplet and consequently, formation of an inner EDL inside the droplet in addition to the traditionally considered outside EDL. While we assume the permittivity inside the inner EDL to remain constant, we consider both the case of constant and variable permittivity in the outer EDL. Our findings demonstrate a change in the droplet direction of motion in the electric field when transitioning from acidic to alkaline pH, regardless of permittivity and ionic strength in both oil and electrolyte. We further find a significant reduction in the magnitude of droplet velocity in the case of a variable permittivity due to reduction of the local space charge density within the EDL surrounding the droplet. When decreasing the viscosity ratio of the oil to the electrolyte, in all cases we find a reduction in droplet velocity. This decline is attributed mostly to the formation and strength of a vortex around the droplet. We finally demonstrate that with constant permittivity in the outer EDL, the variation in κaouter has a more significant effect on the droplet's EP velocity than altering κainner. However, in cases where the body forces inside of the droplet dominate, minor changes in the outer electrolyte concentration have no influence on the droplet motion, which is relevant for biological colloids that can contain significant free internal charges. Our results are important for the manipulation of biological colloids, water and waste treatment such as lubricant removal from processing streams.

Status of LET assessment with active and passive detectors in ion beams

This review explores current experimental methods for determining the radiation quality in ion beams. In this context, radiation quality is commonly evaluated using the averaged linear energy transfer (LET), a metric employed to assess the response of both biological and physical systems. Dose and averaged LET can be experimentally determined with passive detectors through various techniques that have seen recent improvements. Another metric related to the LET is the mean lineal energy, which is measurable using microdosimetric detectors. This review focuses on the available possibilities for evaluating the radiation quality using three microdosimeters (mini-TEPC, Silicon Telescope, and SOI Microplus), three passive luminescence detectors (based on optical, thermo-, and radiophoto-luminescence), three track-based detectors (track-etched detector, Timepix, fluorescent nuclear track detector), and a chemical detector based on alanine. A comparison of detector properties is provided along with an overview of the underlying mechanisms enabling LET assessment or measurements of the mean lineal energy with each detector type. Finally, this review summarizes the current possibilities of LET determination with respect to the needs for quality assurance in particle therapy. Areas for future research and development are suggested.

Inferring dynamical models from time-series biological data using an interpretable machine learning method based on weighted expression trees

The growing time-series data make it possible to glimpse the hidden dynamics in various fields. However, developing a computational toolbox with high interpretability to unveil the interaction dynamics from data remains a crucial challenge. Here, we propose a new computational approach called automated dynamical model inference based on expression trees (ADMIET), in which the machine learning algorithm, the numerical integration of ordinary differential equations and the interpretability from prior knowledge are embedded into the symbolic learning scheme to establish a general framework for revealing the hidden dynamics in time-series data. ADMIET takes full advantage of both machine learning algorithm and expression tree. Firstly, we translate the prior knowledge into constraints on the structure of expression tree, reducing the search space and enhancing the interpretability. Secondly, we utilize the proposed adaptive penalty function to ensure the convergence of gradient descent algorithm and the selection of the symbols. Compared to gene expression programming, ADMIET exhibits its remarkable capability in function fitting with higher accuracy and broader applicability. Moreover, ADMIET can better fit parameters in nonlinear forms compared to regression methods. Furthermore, we apply ADMIET to two typical biological systems and one real data with different prior knowledge to infer the dynamical equations. The results indicate that ADMIET can not only discover the interaction relationships but also provide accurate estimates of the parameters in the equations. These results demonstrate ADMIET’s superiority in revealing interpretable dynamics from time-series biological data.

3D Imaging and Spectroscopy of Complex Biological Systems at the Sub-Cellular, Cellular and Multicellular Levels

3D Imaging and Spectroscopy of Complex Biological Systems at the Sub-Cellular, Cellular and Multicellular Levels