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Biology subjects

Shen, Y.

Publications and source records attributed to Shen, Y..

At least 91 records · Page 5Linked to original sources

Three transcription factors coordinate the archaeal cell cycle progression through a regulatory braking point mechanism

Archaea of the order Sulfolobales execute a well-structured cell cycle program similar to that of eukaryotic cells. However, unlike eukaryotes, archaea lack cyclins and cyclin-dependent kinases and thus the mechanism of cell cycle regulation remained enigmatic. Here, we show that three essential ribbon-helix-helix domain transcription factors, namely, aCcr1, aCcr2, and aCcr3, play pivotal roles in controlling the cell cycle progression in the thermoacidophilic archaeon Saccharolobus islandicus. Coordinated expression of aCcr1 during M-G1 phase, aCcr3 during G1-S phase, and aCcr2 throughout the cell cycle ensures the timely transcription of the key genes that define the cell cycle phases. These include genes involved in chromosome dimer resolution, chromosome segregation, cell division, chromatin organization, DNA replication and repair, protein phosphorylation and degradation and metabolisms of amino acids, tRNA and carbohydrates. The synergy between aCcr1, aCcr2, and aCcr3 is achieved through their differencial affinities for the promoters and the levels of protein expression. We propose that the global regulation of the Sulfolobales cell cycle may be achieved not through transcriptional activation, but rather by repression of the key genes during strategic moments of the cell cycle. We propose a braking point model for the cell cycle control in Sulfolobales, which may represent a simple evolutionary intermediate on the way to the more complex cell cycle regulation in eukaryotes.

microbiology↗

Effective sequence-to-expression prediction for membrane proteins using machine learning and computational protein design

The recombinant expression of integral membrane proteins is notoriously challenging. One way to address this challenge is via computational genotype-to-phenotype models that determine how particular sequence features correlate with protein expression levels. However, the potential of such approaches is yet to be fully realised, at least partly because so few expression datasets are available. Here, we study the sequence-to-expression relationships of a library of 12,248 variants of a specific membrane protein derived from combinatorial computational design. The major advantage of this approach lies in the controlled sequence diversity explored in design, making this new dataset directly compatible with lightweight off-the-shelf bioinformatic tools. The expression phenotype of the entire library is assessed in the widely-used recombinant host Escherichia coli. We employed a relatively small dataset of [~]2000 phenotyped sequences to train a sequence-to-expression predictor using supervised machine learning, which achieved high classification accuracy on held-out test sequences. This model was then used to infer the expression of >10,000 unmeasured sequences, and validation of the top predictions of both high and low expressers achieved 100% success rate. Using tools from explainable AI, we identified specific sequence positions and substitutions that are most important in dictating cellular expression levels. This analysis was validated by model-guided protein engineering that achieved an 8-fold increase in the purification yield of a poorly-expressing variant. Our results show that, at least for this controlled dataset, straightforward and interpretable machine learning can reveal the intrinsic sequence code for membrane protein expression.

bioengineering↗

Deep Multi-modal Species Occupancy Modeling

Effective conservation and restoration of species is an increasingly urgent priority. To design management strategies that improve species success, we need a solid understanding of the habitat characteristics that support it. Occupancy models are statistical tools that ecologists use to model these relationships from data. Yet, current models represent habitats with coarse-scale environmental variables that fail to capture important microhabitat features. We show that these limitations can be addressed by incorporating AI-derived, multimodal habitat representations from overhead satellite imagery and ground-level camera-trap imagery. Across geography and species, these representations yield more accurate out-of-sample predictions than models based on conventional covariates alone, and combining satellite and ground-level views provides complementary gains. To translate improved prediction into actionable ecological insight, we further introduce a method that makes black-box AI-derived habitat representations interpretable by summarizing key factors contributing to occupancy probability into text-based descriptions. We then generate a per-site score for each description, which can replace black-box features to transparently link discovered habitat elements to species occurrence while maintaining predictive performance. Our approach provides a path toward microhabitat-aware and interpretable species-habitat models that support restoration planning and management decisions. We implement our method in an open-source Python package bridging AI and statistical ecology.

ecology↗

The complex microscopic dynamics of cells in high-density epithelial tissues

Epithelial tissues line the surfaces of vital organs and are often densely packed in a disordered, mechanically arrested state, referred to as jammed or glassy state. While collective migration at low-density regimes has been extensively studied, the microscopic dynamics within such jammed epithelia remains poorly understood. Here, we reveal that contrary to expectations from thermal systems with glassy dynamics, jammed epithelial monolayers do not exhibit cage effects that reflect the temporary spatial trapping of the particles. Instead, cells display sub-diffusive creep and Fickian yet non-Gaussian dynamics, accompanied by compressed exponential relaxation, features that reveal stress-driven fluidity. We show that cell divisions and extrusions transiently do enhance local motion, they are insufficient to fluidize the tissue globally. Fast-moving cells form collective, anisotropic clusters, and these dynamic heterogeneities correlate with local structural entropy and low-frequency vibrational modes. These findings challenge the conventional view that jammed tissues are static and inert structures, uncovering a hidden fluidity that can be expected to play a critical role in morphogenesis, wound healing, and early tumor progression.

biophysics↗

An RNA polymerase III tissue and tumor atlas uncovers context-specific activities linked to 3D epigenome regulatory mechanisms

RNA polymerase III (Pol III) produces a plethora of small noncoding RNA species involved in diverse cellular processes, from transcription regulation and splicing to RNA stability, translation, and proteostasis. Though Pol III activity is broadly coupled with cellular demands for protein synthesis and growth, a more precise understanding of gene-level dynamics and context-specific expression patterns remains missing, in part due to challenges related to sequencing and mapping Pol III-derived small ncRNAs. Here, we establish a predictive multi-tissue map of human Pol III activity across 19 tissues and 23 primary cancer subtypes by comprehensively profiling the chromatin accessibility of canonical Pol III-transcribed gene classes. Our framework relies on the unique relationship between gene accessibility and Pol III transcription, inferring activity through uniform binary classification of ATAC-seq enrichment at Pol III-transcribed genes. By characterizing multi-context gene uniformity, we provide a definition of the core Pol III transcriptome, broadly active across specialized tissues, and catalog genes with varied levels of context specificity. Our genomic Pol III atlas uncovers variable levels of activity across tissues, including sharp contraction of the Pol III transcriptome in heart and brain tissues and frequent expansion across diverse cancers. We show that both tissue- and tumor-specific genes are significantly enriched within lamina-associated domains (LADs), and that aberrant expression of nuclear lamin proteins is sufficient to induce Pol III-emergent patterns at tumor-specific genes. Together, these findings link Pol III dynamics to subnuclear compartmentalization and provide a resource for better understanding Pol III expansion and small RNA biogenesis in cancer.

genomics↗

Leveraging Large Language Models for Redundancy-Aware Pathway Analysis and Deep Biological Interpretation

Extracting coherent, biologically meaningful insights from vast, complex multi-omics data remains challenging. Currently, pathway enrichment analysis serves as a cornerstone for the functional interpretation of such data. However, conventional approaches often suffer from extensive functional redundancy caused by shared molecular components and overlapping pathway definitions across databases. This redundancy can obscure key biological signals and compromise the interpretability of pathway enrichment results. Here, we present MAPA (Functional Module Identification and Annotation for Pathway Analysis Results Using Large Language Models [LLM]), an open-source computational framework that resolves redundancy and enhances pathway analysis result interpretation. MAPA computes functional similarity between pathways using LLM-based text embeddings, enabling comparison across different databases. It constructs pathway similarity networks and identifies functional modules via community detection algorithms. Crucially, MAPA employs LLMs for automated functional annotation, integrating Retrieval-Augmented Generation (RAG) to generate comprehensive and real-time biological summaries and reduce hallucinations. Benchmarking demonstrated MAPAs superior performance: the biotext embedding similarity showed a large effect size (Cliffs {delta} = 0.96) compared with the Jaccard index ({delta} = 0.73), and module identification achieved high accuracy (Adjusted Rand Index [ARI] = 0.95) versus existing methods (ARI = 0.23-0.33). Human expert evaluation confirmed that MAPAs annotations match expert-quality interpretations. Finally, a multi-omics aging case study illustrates that MAPA uncovers coherent functional modules and generates insights extending beyond conventional pathway analyses. Collectively, MAPA represents a significant advance in redundancy-aware pathway analysis, transforming pathway enrichment results from fragmented lists into biologically coherent narratives. By leveraging the capabilities of LLMs, MAPA offers researchers a robust, scalable tool for deriving deep mechanistic insights from complex and vast multi-omics datasets, marking a new direction for AI-driven bioinformatics.

bioinformatics↗

DeepBioSim: Efficient and Versatile Methods for Microbiome Data Simulation with Minimal Statistical Assumptions

BackgroundThe human microbiome profoundly influences health and disease. Robust computational and statistical tools for identifying causal microbe-disease links are therefore critical to uncovering the mechanistic basis of these associations. Yet benchmarking such tools remains difficult: microbiome datasets are sparse, high-dimensional, and contain complex dependencies, and no gold-standard reference set exists. Realistic simulated data with embedded ground truth are essential for fair evaluation of analytical tools. Current simulators often impose strong assumptions, require hard-to-obtain auxiliary information, or fail to scale to large, high-dimensional datasets. ResultsWe introduce DeepBioSim, a DEEP-learning framework for BIOlogical SIMulation of microbiome data. DeepBioSim uses variational autoencoders (VAEs) to generate realistic microbiome datasets by sampling directly from the latent distribution of metagenomic or metatranscriptomic count data. ConclusionsThe approach is fast, accurate, and scalable, generating highly realistic synthetic microbiome datasets without extensive hyper-parameter tuning or phylogenetic input. Tests on human RNA-seq data confirm versatility of DeepBioSim, showing it can also reliably simulate single-organism omics profiles.

bioinformatics↗

Mechanistic Modelling of Recessive Disease through Allelic Integration of Variant Effects

Interpreting the effects of genetic variants remains a major challenge in recessive diseases, where clinical outcomes often depend on interactions between alleles. Multiplex assays of variant effects (MAVEs) measure variant function at scale, but nonlinear relationships with biochemical activity complicate the interpretation of MAVE scores. Here, we describe a broadly applicable approach to estimate enzymatic activities for thousands of genetic variants using a pair of fitness assays conducted at different expression levels, by modelling the nonlinear relationship between activity and fitness. Activity scores from two alleles are then combined into a single pathogenicity metric that captures their joint effect. We applied this approach to adenylosuccinate lyase (ADSL), a purine biosynthesis enzyme mutated in the autosomal recessive disorder ADSL deficiency. Using a yeast-based MAVE, we quantified the functional impact of over 8,000 coding variants. Our framework distinguished pathogenic from benign alleles based on estimated activity, and the integrated pathogenicity score correlated strongly with biochemical measurements from patient-derived cells, outperforming existing computational predictors. This dual innovation--the mechanistic transformation of MAVE data and allelic integration--offers a generalizable strategy for probing enzyme function and interpreting genetic variation in recessive disorders.

genetics↗

Caffeic acid promoted deep vein thrombosis resolution in mice by suppressed macrophage M1 polarization through Keap1/Nrf2 pathwayv

BackgroundDeep vein thrombosis (DVT) carries significant health risks, with macrophages playing a key role in thrombus resolution. Caffeic acid (CA) has been shown to inhibit thrombosis, but its role in DVT resolution remains unclear. PurposeThis study aimed to investigates the effects and mechanisms of CA in accelerating DVT resolution. MethodsA stasis-type DVT model was established in mice via inferior vena cava ligation. The effects of CA were analyzed. Thrombus resolution was assessed using histological staining, gelatin zymography, immunoblotting, and immunofluorescence. Macrophage depletion was conducted with chlorophosphate liposomes, and bone marrow-derived macrophages (BMDMs) were used for polarization studies. RNA sequencing identified potential molecular pathways. Nrf2 gene-deficient mice were used to verify the role of Nrf2 in deep vein thrombus resolution. ResultsCaffeic acid reduced thrombus size, enhanced collagenolysis via increased MMP-2 activity, promoted neovascularization, increased macrophage infiltration, and suppressed M1 polarization and inflammation. CA activated the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway by inhibiting Keap1, leading to enhanced antioxidant responses in both BMDMs and thrombus tissue. Macrophage depletion negated CAs benefits, confirming the central role macrophages. ConclusionCA promoted early thrombus resolution via macrophage recruitment, M1 polarization suppression, and Nrf2 activation, highlighting its potential as a preventive strategy for DVT.

pharmacology and toxicology↗

HSD17B7 is required for Auditory Function by Regulating Cholesterol Synthesis in Sensory Hair Cells

Cholesterol homeostasis is fundamental to cellular function, and its disruption underlies a wide range of human diseases. However, the contribution of cholesterol biosynthesis to auditory physiology remains poorly understood. HSD17B7 (17{beta}-Hydroxysteroid dehydrogenase type 7) catalyzes the conversion of zymosterone to zymosterol, a key step in the post-lanosterol cholesterol biosynthetic pathway. Here, we found that Hsd17b7 is highly enriched in sensory hair cells of zebrafish and mice. The deficiency of Hsd17b7 deficiency reduced intracellular cholesterol levels in HEI-OC1 cells and zebrafish hair cells, thereby compromising MET and acoustic startle responses. A heterozygous nonsense variant (c.544G>T; p.E182*) in HSD17B7 was identified in an individual with bilateral profound hearing loss. mRNA of c.544G>T HSD17B7 failed to rescue the impaired MET and acoustic startle response of hsd17b7 mutants. Mechanistically, the mutation decreases mRNA abundance and significantly reduces protein. Moreover, expression of the p.E182* mutation disrupted the interaction between HSD17B7 and the ER retention receptor RER1, leading to aberrant subcellular localization and altered cholesterol distribution, thereby exacerbating HC dysfunction. Together, our findings suggest a conserved and essential role for HSD17B7-mediated cholesterol biosynthesis in sensory hair cell function and identify HSD17B7 as a candidate gene for sensorineural hearing loss.

developmental biology↗

Pluripotent stem cell-derived siTNK cells attack tumors via a synthetic CD8-CD28 TCR complex

Tumor-associated antigen-specific T cell receptor (TCR)-engineered T cells offer a promising strategy for cancer therapy. Natural killer (NK) cells exhibit broad anti-tumor activity with low side effects but lack the capacity to recognize intracellular antigens. Here, we found that the human pluripotent stem cell (hPSC)-derived iNK cells, unlike tissue-isolated NK cells, expressed all four CD3 subunits at the transcriptome level. We introduced a synthetic gene-expressing complex (SCOTR), encoding a tumor antigen- specific TCR, CD8 coreceptor, and CD28 costimulatory molecule, into hPSCs to generate SCOTR-hPSCs. The SCOTR-hPSCs gave rise to abundant synthetic TCR complex-expressing iNK (siTNK) cells via an organoid induction method. These siTNK cells showed HLA-dependent, antigen-specific cytotoxicity against tumor cells and significantly suppressed tumor growth in tumor xenograft animal models, while also preserving universal non-specific tumor-killing activity. Collectively, siTNK cells, as hPSC-derived hybrid cells with dual features of adaptive T and inherent NK cells, offer an artificial cell source for human immunotherapies.

immunology↗

Deciphering the Nrf2/ARE Mechanism: GanCaoXieXin Decoction Combats Oxidative Stress in Ulcerative Colitis Pathogenesis

BackgroundGanCaoXieXin (GCXX) decoction, a classic prescription, has shown clinical efficacy in treating ulcerative colitis (UC). However, its mechanism remains incompletely understood. ObjectiveThis study aims to explore how GCXX modulates the Nrf2/ARE signaling pathway to mitigate oxidative stress (OS)-induced damage and thereby ameliorate UC. Materials and methodsNetwork pharmacology and bioinformatics analyses identified key targets of GCXX in UC treatment. Ultra-performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry (UHPLC-MS/MS) analyzed GCXXs effective compounds. A 2.5% Dextran Sulfate Sodium Salt (DSS)-induced UC mouse model was used. Immunohistochemistry (IHC) assessed tight junction proteins. Enzyme-linked immunosorbent assay (ELISA) measured intestinal permeability and oxidative stress markers. Western blot (WB) analyzed Nrf2/ARE signaling proteins. In 800mol/L H2O2-induced oxidative stress (OS) state HT-29 cells, cell viability, apoptosis, oxidative stress indicators, and apoptosis-related proteins were evaluated. Immunofluorescence (IF) detected Nrf2/ARE signaling axis proteins. ResultsThe results of network pharmacology and bioinformatics analysis revealed that GCXX could intervene in UC by regulating OS-related pathways. GCXX contained antioxidant components like quercetin, berberine, and baicalin. In vivo, GCXX alleviated mucosal damage, reduced intestinal permeability, downregulated MDA, upregulated SOD, suppressed Keap1, and promoted tight junction and Nrf2/ARE pathway proteins. In vitro, GCXX increased cell survival, improved antioxidant capacity, reduced MDA and apoptosis in OS state cells. Immunofluorescence confirmed Nrf2/ARE pathway as crucial in GCXXs protective effects. ConclusionsGCXX elevates the expression level of Nrf2, HO-1 and NQO1, thus reducing entercell OS-induced damage, intestinal cell apoptosis, alleviating UC intestinal mucosal damaget.

pharmacology and toxicology↗

Spliformer-v2 predicts multi-tissue RNA splicing and reveals functional genomic links with neurodegenerative diseases

Precise regulation of pre-mRNA splicing underlies transcriptomic diversity and is disrupted in aging and disease, yet tissue-specific splice-altering genetic variants remain poorly resolved. Here, we present Spliformer-V2, a SegmentNT-based deep learning model for predicting and interpreting variant effects on RNA splicing across human tissues. We generated a diploid sequence resolved RNA splice map from paired whole-genome-sequencing and RNA-seq data across 12 central nervous system (CNS) and 6 peripheral tissues for model development. Spliformer-V2 outperformed SpliceTransformer, Pangolin and AlphaGenome in predicting splice-site usage, identified tissue-specific splicing regulatory motifs, and revealed tissue vulnerability to pathogenic splice-altering variants. Analyses of loci associated with 8 neurological diseases prioritized CNS-specific mis-splice-vulnerable genes. In 1,405 amyotrophic lateral sclerosis (ALS) genomes, Spliformer-V2 nominated rare splice-altering variants enriched in PTPRN2, which showed reduced expression in TDP-43-depleted neurons. PTPRN2 overexpression rescued C9ORF72-patient derived motor neuron degeneration and modulated TDP-43 mislocalization, indicating it as a potential therapeutic modifier in ALS.

bioinformatics↗

A genome-wide MAGIC kit for recombinase-independent mosaic analysis in Drosophila

Mosaic analysis has been instrumental in advancing developmental and cell biology. Most current mosaic techniques rely on exogenous site-specific recombination sequences that need to be introduced into the genome, limiting their application. Mosaic analysis by gRNA-induced crossing-over (MAGIC) was recently developed in Drosophila to eliminate this requirement by inducing somatic recombination through CRISPR/Cas9-generated DNA double-strand breaks. However, MAGIC has not been widely adopted because gRNA-markers, a required component for this technique, are not yet available for most chromosomes. Here, we present a complete, genome-wide gRNA-marker kit that incorporates optimized designs for enhanced clone induction and more effective clone labeling in both positive MAGIC (pMAGIC) and negative MAGIC (nMAGIC). With this kit, we demonstrate clonal analysis in a broad range of Drosophila tissues, including cell types that have been difficult to analyze using recombinase-based systems. Notably, MAGIC enables clonal analysis of pericentromeric genes and deficiency chromosomes and in interspecific hybrid animals, opening new avenues for gene function study, rapid gene discovery, and understanding cellular basis of speciation. This MAGIC kit complements existing systems and makes mosaic analysis accessible to address a wider range of biological questions. IMPACT STATEMENTA comprehensive toolkit enables genome-wide, recombinase-independent mosaic analysis in Drosophila, permitting clonal analysis of pericentromeric genes, deficiency chromosomes, and interspecific hybrids previously inaccessible to standard methods.

genetics↗

Spectraplakin cooperates with noncentrosomal microtubule regulators to orient dendritic microtubules in Drosophila

The differential microtubule organization in axons and dendrites underlies neuronal polarity and developmental processes like neurite pruning. How neurons achieve their specific microtubule organization during development is an area of active research, and it has been especially hard to explain how dendritic microtubules are oriented with their plus ends towards the soma. Transient microtubule nucleation from tips of early growing dendrites has been detected in some systems and would explain how orientation is set up. In a survey for cytoskeletal regulators involved in dendrite pruning and microtubule organization in Drosophila, we found the spectraplakin Short stop (Shot), an actin/microtubule crosslinker. Loss of Shot causes microtubule orientation defects already during early dendrite development, when Shot is transiently recruited to tips of growing dendrites via its actin binding domain. Genetic and functional evidence suggest that Shots primary function in this process is to locally stabilize microtubules. We also provide evidence for a developmentally transient microtubule nucleation mechanism. Our data highlight the importance of transient and localized microtubule regulation for dendritic microtubule organization.

developmental biology↗

Accelerating Virtual Directed Evolution of Proteins via Reinforcement Learning

With the advancement of machine learning methods, the protein fitness landscape can be predicted, providing reliable guidance in the selection of advantageous mutations for the directed evolution of proteins. However, the potential multiple mutational variants derived from the simple combi-nation of a limited number of advantageous single mutations may not represent superior choices. Moreover, the exploration and selection of the astronomical number of multiple mutational variants remain a highly challenging task. In this study, we introduce a virtual directed evolution pipeline, RelaVDEP, for the rapid identification of mutational variants with explicit enhancement in the de-sired property of the target protein. By adapting and fine-tuning a pre-trained fitness predictor to improve sequence-based protein functional prediction and by designing a model-based reinforce-ment learning framework to efficiently explore the vast combinatorial space of protein mutations, this pipeline is capable of effectively accelerating the directed evolution process for a broad spec-trum of proteins with versatile functional profiles. According to a series of experimental validations, the diversified mutational variants identified by our method exhibit notable improvements in desir-able protein functional properties. In particular, by integrating RelaVDEP with active learning, we successfully optimized the sequence of a PETase, enhancing its catalytic activity through previously unknown mutations.

bioinformatics↗

Optimization of regulatory DNA with active learning

Many biotechnology applications rely on microbial strains engineered to express heterologous proteins at maximal yield. A common strategy for improving protein output is to design expression systems with optimized regulatory DNA elements. Recent advances in high-throughput experimentation have enabled the use of machine learning predictors in tandem with sequence optimizers to find regulatory sequences with improved phenotypes. Yet the narrow coverage of training data, limited model generalization, and highly nonconvex nature of genotype-phenotype landscapes can limit the use of traditional sequence optimization algorithms. Here, we explore the use of active learning as a strategy to improve expression levels through iterative rounds of measurements, model training, and sequence sampling-and-selection. We explore convergence and performance of the active learning loop using synthetic data and an experimentally characterized genotype-phenotype landscape of yeast promoter sequences. Our results show that active learning can outperform one-shot optimization approaches in complex landscapes with a high degree of epistasis. We demonstrate the ability of active learning to effectively optimize sequences using datasets from different experimental conditions, with potential for leveraging data across laboratories, strains or growth conditions. Our findings highlight active learning as an effective framework for DNA sequence design, offering a powerful strategy for phenotype optimization in biotechnology.

synthetic biology↗

A bottom-up approach to understanding ultrasound mediated blood-brain barrier opening using long and short pulses

Ultrasound-mediated blood-brain barrier (BBB) opening is promising for non-invasive, localized, and reversible drug delivery to the brain. However, the underlying mechanisms remain unclear, particularly at microscopic level in real time. Recently developed short-pulse ultrasound enhances safety by reducing side effects compared to conventional long-pulse ultrasound. Using vessel-mimicking microchannels cultured with brain endothelial monolayers, we directly observed bubble dynamics and cellular bioeffects under flow conditions. Intriguingly, cyclic jetting during stable cavitation occurs at long-pulse mode, accompanied by a few localized cell detachments and extensive sonoporation. In contrast, short-pulse ultrasound induced milder, uniform bubble dynamics, reversible sonoporation and calcium signaling, promoting safer BBB modulation. In vivo two-photon imaging and histology in mice confirmed these findings, showing that long-pulse ultrasound enabled higher drug delivery efficiency but caused localized endothelial damage, while short-pulse ultrasound facilitated uniform delivery and faster BBB recovery. The differential bubble dynamics and cellular responses correlated well between in vitro and in vivo models. This study establishes a cross-scale framework for real-time analysis of ultrasound-mediated BBB opening, revealing key biophysical factors governing safety and efficacy. The findings provide guidance for the optimization of ultrasound protocols for vascular drug delivery, with potential applications in treating brain tumors, neurodegenerative diseases, and other disorders.

biophysics↗