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bioRxiv2025

Critical Environmental Limits: Assessing the Limitations of Core Temperature Inflection Point (CTIP) and Biophysical Modeling

The Core Temperature Inflection Point (CTIP) method and biophysical modeling are widely used to determine critical environmental limits (CEL), yet their validity under prolonged heat exposure remains untested. This study evaluated their predictive accuracy by exposing 36 healthy young adults (20 males, 16 females; age: 20.9-22.4 yr) to five counterbalanced 8-hour heat trials in a controlled chamber (36{degrees}C/74.5% RH, 40{degrees}C/55.0% RH, 44{degrees}C/29.2% RH, 47{degrees}C/35.6% RH, 50{degrees}C/24.5% RH). These conditions were selected based on prior CTIP and biophysical model predictions of CELs. Participants engaged in sedentary office tasks (1.29- 1.67 METs), wore standardized summer clothing (0.39-0.40 clo), and had ad libitum access to an electrolyte drink, with a 500-kcal sandwich provided at midday. Rectal temperature (Trec) was continuously monitored. Contrary to CTIP and biophysical model predictions, all five conditions remained compensable (Trec rise rate [≤] 0.1{degrees}C/h), with mean peak Trec staying well below heatstroke thresholds (38.2 {+/-} 0.4{degrees}C). At 44{degrees}C/29.2% RH, females had significantly lower Trec than males (p < 0.05), but steady-state Trec responses were similar between sexes (all p > 0.10). Collectively, CTIP and biophysical models substantially underestimated CELs, leading to overpredicted heat risk across all trials. These findings challenge the reliability of current predictive methods, suggesting human tolerance may exceed existing estimates. Refining these models is essential for improving heat risk assessment during real-world heatwaves and informing public and occupational health guidelines in a warming climate. NEW & NOTEWORTHYThis study demonstrates that healthy young adults maintained heat balance over 8 hours across five extreme heat conditions (36-50{degrees}C), despite the Core Temperature Inflection Point (CTIP) method and biophysical modeling predicting uncompensability. These findings challenge the reliability of these tools for assessing prolonged heat exposure, highlighting the need for refined models to accurately predict critical environmental limits in real-world scenarios, such as extreme heatwaves.

physiology↗

bioRxiv2018

Asymmetric dispersal is a critical element of concordance between biophysical dispersal models and spatial genetic structure in Great Barrier Reef corals

AimWidespread coral bleaching, crown-of-thorns seastar outbreaks, and tropical storms all threaten foundational coral species of the Great Barrier Reef, with impacts differing over time and space. Yet, dispersal via larval propagules could aid reef recovery by supplying new settlers and enabling the spread of adaptive variation among regions. Documenting and predicting spatial connections arising from planktonic larval dispersal in marine species, however, remains a formidable challenge.\n\nLocationThe Great Barrier Reef, Australia\n\nMethodsContemporary biophysical larval dispersal models were used to predict longdistance multigenerational connections for two common and foundational coral species (Acropora tenuis and Acropora millepora). Spatially extensive genetic surveys allowed us to infer signatures of asymmetric dispersal for these species and evaluate concordance against expectations from biophysical models using coalescent genetic simulations, directions of inferred gene flow, and spatial eigenvector modelling.\n\nResultsAt long distances, biophysical models predicted a preponderance of north to south connections and genetic results matched these expectations: coalescent genetic simulations rejected an alternative scenario of historical isolation; the strongest signals of inferred gene flow were from north to south; and asymmetric eigenvectors derived from north to south connections in the biophysical models were significantly better predictors of spatial genetic patterns than eigenvectors derived from symmetric null spatial models.\n\nMain conclusionsResults are consistent with biophysical dispersal models yielding approximate summaries of past multigenerational gene flow conditioned upon directionality of connections. For A. tenuis and A. millepora, northern and central reefs have been important sources to downstream southern reefs over the recent evolutionary past and should continue to provide southward gene flow. Endemic genetic diversity of southern reefs suggests substantial local recruitment and lack of long distance gene flow from south to north.

ecology↗

bioRxiv2026

Multiaxial Biophysical Control of Oncogenic Phase Separation by Indoleamines: A Proof-of-Concept Synthesis of Landscape-Level Regulation

ABSTRACTO_ST_ABSBackgroundC_ST_ABSPhase-separated oncogenic condensates act as biophysical sanctuaries, stabilizing transcriptional and survival programs to drive cancer progression. While individual proteins within these assemblies are well-documented, a universal regulator capable of orchestrating the integrated biophysical axes governing cellular phase behavior has remained elusive. We propose a paradigm of landscape-level regulation mediated by the indoleamine scaffold. MethodsA systematic synthesis and integrative bioinformatics analysis were performed to identify the intersection between melatonin-responsive genes and the phase-separation proteome. A gene set of 121 phase separation-related molecules was validated via PhaSepDB and intersected with 134 melatonin-modulated genes identified across diverse cancer models. Clinical relevance was assessed using TCGA survival datasets. ResultsWe identified a conserved network of 26 core genes--including AR, BCL2, CGAS, CTNNB1, EP300, EZH2, EGFR, IKBKG (NEMO), KEAP1, KDM1A (LSD1), LEF1, MYC, NANOG, PRNP, SMAD3, SOX9, SQSTM1, TFEB, TFAM, TP53, TWIST1, USP10, WWTR1 (TAZ), VIM, YAP1, and YTHDF3--at the intersection of melatonin signaling and PS architecture. Network analysis revealed high-confidence interactions predominantly localized to the nucleoplasm and stress granules. Melatonin significantly suppressed the majority of these oncogenic drivers, targeting pathways associated with EMT, glycolysis, and hypoxia. Furthermore, higher expression of this melatonin-responsive phase separation-network correlated with poorer overall survival in breast and gastric cancers. ConclusionsThis proof-of-concept synthesis positions melatonin as a remarkably versatile regulator of phase-separated oncogenic networks, capable of simultaneously modulating the integrated biophysical axes that govern cellular phase behavior. We propose that melatonin utilizes a tri-lever framework--comprising redox switching, structural intercalation, and dielectric tuning--to recalibrate the cellular solvation and fluidity landscape. By enforcing fundamental physicochemical constraints rather than targeting discrete signaling nodes, melatonin destabilizes the biophysical sanctuaries that shield oncogenic programs. This landscape-level regulation offers a strategic platform for disrupting condensate-driven malignancy, providing a biophysical logic that circumvents the diverse evasion mechanisms of the malignant cell.

cancer biology↗

bioRxiv2024

VISION -- an open-source software for automated multi-dimensional image analysis of cellular biophysics

Environment-sensitive probes are frequently used in spectral/multi-channel microscopy to study alterations in cell homeostasis. However, the few open-source packages available for processing of spectral images are limited in scope. Here, we present VISION, a stand-alone software based on Phyton for spectral analysis with improved applicability. In addition to classical intensity-based analysis, our software can batch-process multidimensional images with an advanced single-cell segmentation capability and apply user-defined mathematical operations on spectra to calculate biophysical and metabolic parameters of single cells. VISION allows for 3D and temporal mapping of properties such as membrane fluidity and mitochondrial potential. We demonstrate the broad applicability of VISION by applying it to study the effect of various drugs on cellular biophysical properties; the correlation between membrane fluidity and mitochondrial potential; protein distribution in cell-cell contacts; and properties of nanodomains in cell-derived vesicles. Together with the code, we provide a graphical user interface for facile adoption. We anticipate that VISION will find a broad range of applications in different fields of biology, spanning from molecular and tissue biology to immunology and biophysics. Summary statementVISION, an open-source software, enables high throughput and correlative analysis of cellular biophysical properties.

biophysics↗

bioRxiv2025

Giant KASH proteins and ribosomes synergistically establish cytoplasmic biophysical properties in vivo

Understanding how cells control their biophysical properties during development remains a fundamental challenge. While cytoplasmic macromolecular crowding affects multiple cellular processes in single cells, its regulation in living animals remains poorly understood. Using genetically encoded multimeric nanoparticles for in vivo rheology, we discovered that C. elegans tissues maintain distinct cytoplasmic biophysical properties that differ from those observed across diverse systems, including bacteria, yeast species, and cultured mammalian cells. We identified two conserved mechanisms controlling cytoplasmic macromolecular diffusion: ribosome concentration, a known regulator of cytoplasmic crowding, works in concert with a previously unknown function for the giant KASH protein ANC-1 scaffolding the endoplasmic reticulum. These findings reveal mechanisms by which tissues establish and maintain distinct cytoplasmic biophysical properties, with implications for understanding cellular organization across species. One-Sentence SummaryLiving tissues maintain unique intracellular biophysical properties under the control of cytoplasmic constraints and crowding.

biophysics↗

bioRxiv2025

Biophysical fitness landscape design traps viral evolution

AO_SCPLOWBSTRACTC_SCPLOWEvolutionary adaptation is often visualized as a populations stochastic climb toward the top of a fitness landscape. While there exist approaches to design or synthetically evolve proteins into desired structures, there is a lack of methodology for designing, tuning, and quantitatively reshaping the fitness landscapes themselves on which protein evolution takes place. Here, we introduce foundational principles of fitness landscape design (FLD) to customize the structural peaks and valleys of biophysical fitness landscapes with quantitative accuracy, offering robust control of long-term evolutionary outcomes. Our FLD algorithms use stochastic optimization of a chemically derived biophysical fitness model to consistently discover optimal antibody ensembles which force a target protein to evolve according to a user-specified target fitness landscape. We then apply FLD to suppress the fitnesses of two SARS-CoV-2 genotype neutral networks and to discover proactive vaccines that preemptively restrict escape variant fitness trajectories before they arise. SO_SCPLOWIGNIFICANCEC_SCPLOW SO_SCPLOWTATEMENTC_SCPLOWRapidly evolving viruses mutate to escape antibodies generated by the human immune system, leading to periodic waves of infection and death. Modern vaccine design approaches that focus on currently prevalent strains can be vulnerable to emerging escape mutations. An ideal strategy for proactive vaccine design requires not only immediate effectiveness, but also control over the viral fitness landscape to ensure optimal suppression of escape variants. Here, we introduce biophysics-based computational algorithms to discover optimal antibody ensembles that quantitatively reshape the viral fitness landscape to induce long-term suppression of fitness trajectories. These protocols, called biophysical fitness landscape design, open the door to improved pandemic preparedness via proactive vaccine, antibody, and peptide design, thinking several steps ahead of pathogen evolution.

biophysics↗

bioRxiv2019

A biophysical model of Epithelial-Mesenchymal Transition uncovers the frequency and size distribution of Circulating Tumor Cell clusters across cancer types

The gain of cellular motility via the epithelial-mesenchymal transition (EMT) is considered crucial in the metastatic cascade. Cells undergoing EMT to varying extents are launched into the bloodstream as single circulating tumor cells (CTCs) or multi-cellular clusters. The frequency and size distributions of these multi-cellular clusters has been recently measured, but the underlying mechanisms enabling these different modes of migration remain poorly understood. We present a biophysical model that couples the epithelial-mesenchymal phenotypic transition and cell migration to explain these different modes of cancer cell migration. With this reduced physical model, we identify a transition from individual migration to clustered cell migration that is regulated by the rate of EMT and the degree of cooperativity between cells during migration. This single cell to clustered migration transition can robustly recapitulate cluster size distributions observed experimentally across several cancer types, thus suggesting the existence of common features in the mechanisms of cell migration during metastasis. Furthermore, we identify three main mechanisms that can facilitate the formation and dissemination of large clusters: first, mechanisms that prevent a complete EMT and instead increase the population of hybrid Epithelial/Mesenchymal (E/M) cells; second, multiple intermediate E/M states that give rise to heterogeneous clusters formed by cells with different epithelial-mesenchymal traits; and third, non-cell-autonomous induction of EMT via cell-to-cell signaling that gives rise to spatial correlations among cells in a tissue. Overall, this biophysical model represents a first step toward bridging the gap between the molecular and biophysical understanding of EMT and various modes of cancer cell migration, and highlights that a complete EMT might not be required for metastasis. Popular summaryThe Epithelial-Mesenchymal Transition (EMT) has been identified as the first step that enables cancer metastases; through this process, cancer cells gain the motility necessary to migrate and invade. Cancer cells that undergo EMT can enter the circulatory system both as single cells or as multi-cellular clusters. While single cells are generally more frequent in human cancers, clusters are more prevalent in aggressive cancers that metastasize more. Although the molecular mechanisms of EMT are relatively conserved across cancers, how different cancers exhibit such tremendous variability in terms of cell migration remains unclear. We develop a biophysical model to investigate how EMT regulation at a single cell level can give rise to single cell and clustered cell migration. This model quantitatively reproduces size distributions of circulating tumor cell clusters reported in human circulation and mouse models, therefore identifying a unifying set of principles governing cell migration across different cancer types. Moreover, a model where cells only undergo a partial EMT to a hybrid epithelial/mesenchymal state can recapitulate different features observed in collective cancer cell migration, including the frequency of large clusters and flat distributions that cannot be captured by a model of complete EMT. Besides partial EMT, we propose additional mechanisms that can facilitate the formation of large tumor cell clusters, including multiple hybrid epithelial/mesenchymal cell states and signaling between cells that enables noncell autonomous EMT induction. Therefore, our general picture suggests universal traits in the migration of cancer cells and challenges the necessity of a complete EMT for cancer metastasis.

biophysics↗

bioRxiv2026

Discovering Plastic-Binding Peptides with Favorable Affinity, Water Solubility, and Binding Specificity Through Deep Learning and Biophysical Modeling

Microplastic (MP) pollution, which is present in the ecosystem in vast quantities, adversely affects human health and the environment, making it imperative to develop methods for its mitigation. The challenge of detecting or capturing MPs could potentially be addressed using plastic-binding peptides (PBPs). The ideal PBP for MP remediation would not only bind strongly to plastic, but also have other properties such as high solubility in water or great binding specificity to a certain plastic. However, the scarcity or absence of known PBPs for common plastics along with the lack of methods that can discover PBPs with all of the desired properties precludes the development of peptide-based MP remediation strategies. In this study, we discovered short linear PBPs with high predicted water solubility and binding specificity by employing an in-silico discovery pipeline that combines deep learning and biophysical modeling. First, a long short-term memory (LSTM) network was trained on biophysical modeling data to predict peptide affinity to plastic. High affinity peptides were generated by pairing the trained LSTM with a Monte Carlo tree search (MCTS) algorithm. Molecular dynamics (MD) simulations showed that the PBPs discovered for polyethylene, the most common plastic, had 15% lower binding free energy than PBPs obtained using biophysical modeling alone. PBPs with both high affinity and high predicted solubility in water were found by including the CamSol solubility score in the MCTS peptide scoring function, increasing the average solubility score from 0.2 to 0.9, while only minimally decreasing affinity for polyethylene. The framework also discovered peptides with high binding specificity between polystyrene and polyethylene, two major constituents of MP pollution, using a competitive MCTS approach that optimized the difference in affinity between the two plastics. MD simulations showed that competitive MCTS increased the binding specificity of PBPs for polystyrene and identified peptides with relatively great preference for either of the two plastics. The framework can readily be applied to design PBPs for other types of plastic. Overall, the high-affinity PBPs with desirable properties discovered by marrying artificial intelligence and biophysics can be valuable for remediating MP pollution and protecting the health of humans and the environment.

biophysics↗

bioRxiv2026

A Multiscale Signaling--Biophysical Framework Reveals Mechanisms of Macrophage-Mediated RBC Clearance in Sickle Cell and Gaucher Disease

Red blood cell (RBC) clearance by macrophages maintains blood homeostasis and is dysregulated in the hemolytic disorder sickle cell disease (SCD) and the lysosomal storage disorder Gaucher disease (GD), where biophysical and biochemical alterations promote premature phagocytosis. We develop a multiscale hybrid modeling framework integrating signaling dynamics, biophysical simulations, and machine learning to investigate the mechanisms governing RBC phagocytosis in these diseases. Our approach couples a systems biology model of macrophage-RBC signaling with Dissipative Particle Dynamics (DPD) simulations of molecular diffusion and membrane interactions, and leverages Physics-Informed Neural Networks (PINNs) for robust parameter inference. The DPD framework provides mechanistic insight into antibody diffusion, receptor engagement, and membrane-level interactions during macrophage-RBC contact, generating spatially resolved trajectories of CD47-SIRP signaling and antibody-receptor binding that serve as intermediate observables constraining the signaling model. The model accurately captures differential phagocytic responses between healthy and altered RBCs, revealing diminished inhibitory signaling and changes in SHP1-mediated pathways in both SCD and GD. Identifiability analysis combining Fisher Information Matrix diagnostics and profile likelihood confirms that parameters governing the CD47-SIRP-SHP1 axis are among the most robustly recoverable, and simulations of therapeutic perturbations with anti-SIRP antibodies demonstrate modulation of engulfment outcomes. We further employ Physics-Informed Kolmogorov-Arnold Networks (PIKANs) as an alternative to standard PINNs, demonstrating improved robustness under noise and sampling variability. More broadly, our multiscale platform linking biophysical simulation with systems-level inference is generalizable, offering mechanistic insights and computational tools for therapeutic exploration in diseases involving dysregulated phagocytosis. Significance statementRed blood cells are normally removed from circulation by macrophages through tightly regulated molecular signals. In diseases such as sickle cell disease and Gaucher disease, this clearance process becomes abnormal, contributing to anemia and other complications. However, the mechanisms linking the physical properties of red blood cells to immune signaling remain poorly understood. Here we develop a multiscale computational framework that combines particle-based biophysical simulations, systems biology models, and physics-informed machine learning. This approach provides a quantitative framework to interpret how changes in red blood cell mechanics and surface signaling disrupt the CD47-SIRP inhibitory pathway that normally prevents phagocytosis. The framework provides a predictive platform for studying immune clearance and may help guide therapeutic strategies targeting red blood cell-macrophage interactions.

biophysics↗

bioRxiv2026

A Bottom-Up Approach to Fungal Plasma Membrane Model: Lipid Mixture Design and Biophysical-Mechanical Characterization

The rising incidence of invasive fungal diseases emphasizes the need for novel therapeutic strategies, including membrane-targeting antifungal agents, which require representative lipid models for detailed molecular-level studies. In this work, we propose a consensus quinary fungal plasma membrane model based on lipidomic literature data, specifically PC:PE:PI:PA:PS phospholipid model with ratio of 44:29:13:8:6. Using a bottom-up approach, we characterized the biophysical properties of this system - with particular emphasis on mechanical parameters such as bending rigidity and area compressibility - by combining molecular dynamics simulations with experimental flicker-noise and ATR-FTIR spectroscopies. Furthermore, we investigated the effect of two key non-phospholipid components: ergosterol and triacylglycerols. Biophysical analysis revealed that DPPI and its specific interactions with DSPS induced the most substantial deviations in baseline membrane parameters, particularly area per lipid, membrane thickness, and area compressibility, while DSPS influenced bending rigidity change and DLiPA primarily affected lipid packing defects. In addition, ergosterol and TGs were found to influence all of the investigated parameters to different degree. Notably, the overall biophysical profile of the proposed FPMM closely mimicked that of natural vesicles derived from yeast lipid extracts, establishing this model may provide a reliable platform for studying fungal membrane biophysics and lipid-targeting interactions.

biophysics↗

bioRxiv2020

Advanced Biophysical Model to Capture Channel Variability for EQS Capacitive HBC

Human Body Communication (HBC) has come up as a promising alternative to traditional radio frequency (RF) Wireless Body Area Network (WBAN) technologies. This is essentially due to HBC providing a broadband communication channel with enhanced signal security in the physical layer due to lower radiation from the human body as compared to its RF counterparts. An in-depth understanding of the mechanism for the channel loss variability and associated biophysical model needs to be developed before EQS-HBC can be used more frequently in WBAN consumer and medical applications. Biophysical models characterizing the human body as a communication channel didnt exist in literature for a long time. Recent developments have shown models that capture the channel response for fixed transmitter and receiver positions on the human body. These biophysical models do not capture the variability in the HBC channel for varying positions of the devices with respect to the human body. In this study, we provide a detailed analysis of the change in path loss in a capacitive-HBC channel in the electroquasistatic (EQS) domain. Causes of channel loss variability namely: inter-device coupling and effects of fringe fields due to bodys shadowing effects are investigated. FEM based simulation results are used to analyze the channel response of human body for different positions and sizes of the device which are further verified using measurement results to validate the developed biophysical model. Using the bio-physical model, we develop a closed form equation for the path loss in a capacitive HBC channel which is then analyzed as a function of the geometric properties of the device and the position with respect to the human body which will help pave the path towards future EQS-HBC WBAN design.

bioengineering↗

bioRxiv2021

Blood Unit Segments Accurately Represent the Biophysical Properties of Red Blood Cells in Blood Bags but Not Hemolysis

BACKGROUNDThe biophysical properties of red blood cells (RBCs) provide potential biomarkers for the quality of donated blood. Blood unit segments provide a simple and nondestructive way to sample RBCs in clinical studies of transfusion efficacy, but it is not known whether RBCs sampled from segments accurately represent the biophysical properties of RBCs in blood bags. STUDY DESIGN AND METHODSRBCs were sampled from blood bags and segments every two weeks during 8 weeks of storage at 4 {degrees}C. RBC deformability was measured by deformabilitybased sorting using the microfluidic ratchet device in order to derive a rigidity score. Standard hematological parameters, including mean corpuscular volume (MCV), red cell distribution width (RDW), mean cell hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and hemolysis were measured at the same time points. RESULTSDeformability of RBCs stored in blood bags was retained over 4 weeks storage but a progressive loss of deformability was observed at weeks 6 and 8. This trend was mirrored in blood unit segments with a strong correlation to the blood bag data. Strong correlations were also observed between blood bag and segment for MCV, MCHC and MCH, but not for hemolysis. CONCLUSIONRBCs sampled from blood unit segments accurately represents the biophysical properties of RBCs in blood bags, but not hemolysis. Blood unit segments provide a simple and non-destructive sample for measuring RBC biophysical properties in clinical studies.

bioengineering↗

bioRxiv2021

Towards Biophysical Markers of Depression Vulnerability

A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. We constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.

neuroscience↗

bioRxiv2022

Neural mass modelling for the masses: Democratising access to whole-brain biophysical modelling with FastDMF

Different whole-brain models constrained by neuroimaging data have been developed during the last years to investigate causal hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) model is a particularly attractive model, combining a biophysically realistic single-neuron model that is scaled up via a mean-field approach and multimodal imaging data. Despite these favourable features, an important barrier for a widespread usage of the DMF model is that current implementations are computationally expensive - to the extent that the model often becomes unfeasible when no high-performance computing infrastructure is available. Furthermore, even when such computing structure is available, current implementations can only support simulations on brain parcellations that consider less than 100 brain regions. To remove these barriers, here we introduce a user-friendly and computationally-efficient implementation of the DMF model, which we call FastDMF, with the goal of making biophysical whole-brain modelling accessible to neuroscientists worldwide. By leveraging a suit of analytical and numerical advances - including a novel estimation of the feedback inhibition control parameter, and a Bayesian optimisation algorithm - the FastDMF circumvents various computational bottlenecks of previous implementations. An evaluation of the performance of the FastDMF showed that it can attain a significantly faster performance than previous implementations while reducing the memory consumption by several orders of magnitude. Thanks to these computational advances, FastDMF makes it possible to increase the number of simulated regions by one order of magnitude: we found good agreement between empirical and simulated functional MRI data parcellated at two different spatial scales (N=90 and N=1000 brain regions). These advances open the way to the widespread use of biophysically grounded whole-brain models for understanding the interplay among anatomy, function and brain dynamics in health and disease, and to provide mechanistic explanations of recent results obtained from empirical fine-grained neuroimaging data sets, such as turbulence or connectome harmonics. HighlightsO_LIWe present the FastDMF, a user-friendly and computationally efficient implementation of the Dynamic Mean Field model for simulations of whole-brain dynamics. C_LIO_LIUsing analytical and numerical tools, we develop a novel estimation of the feedback inhibition control based on the structural connectivity, bypassing an important computational bottleneck. C_LIO_LIThe FastDMF is coupled with a Bayesian Optimization algorithm significantly reducing the number of simulations required to fit the FastDMF to empirical neuroimaging data. C_LIO_LIOur advances open the possibility of simulating thousands of brain regions in a biophysically grounded whole-brain model. C_LI

neuroscience↗

bioRxiv2023

Skin microbiome attributes associate with biophysical skin aging

Two major arms of skin aging are changes in the skins biophysical conditions and alterations in the skin microbiome. This work partitioned both arms to study their interaction in detail. Leveraging the resolution provided by shotgun metagenomics, we explored how skin microbial species, strains, and gene content interact with the biophysical traits of the skin during aging. With a dataset well-controlled for confounding factors, we found that skin biophysical traits, especially the collagen diffusion coefficient, are associated with the composition and the functional potential of the skin microbiome, including the abundance of bacterial strains found in nosocomial infections and the abundance of antibiotic resistance genes. Our findings reveal important associations between skin biophysical features and aging-related changes in the skin microbiome and generate testable hypotheses for the mechanisms of such associations.

genomics↗

bioRxiv2023

Biophysical constraints on avian adaptation and diversification

The capacity of organisms to adapt to vacant niches or changing environments is limited by physical constraints on morphological evolution. Substantial progress has been made in identifying how these constraints shape the form and function of producers (plants), but our understanding of evolutionary limits in consumers (animals) remains highly limited, in part because the requisite data have not been available at sufficient scale. Using morphometric measurements for all birds, we demonstrate that observed variation is highly restricted--both for beak shape and overall body shape--to triangular regions of morphospace with clearly defined boundaries and vertices. By combining morphometric data with new information on physical functions of measured traits, we provide evidence that the extent of avian morphospace is constrained by biophysical trade-offs between three functional objectives (strength, reach and engulfment capacity) that characterize resource acquisition and processing by the beak, and three locomotory modalities (aerial, aquatic and terrestrial) that characterize avian lifestyles. Our results suggest that over avian evolutionary history, trajectories of morphological change trend towards the vertices, with birds evolving from a core of biophysical generalists to biophysical specialists, associated with faster macroevolutionary turnover of lineages at the periphery or morphospace. Our analyses reveal that the structure of avian morphological diversity follows relatively simple rules defined by biophysical constraints and trade-offs, shedding light on the process shaping modern animal diversity and responses to environmental change.

ecology↗

bioRxiv2023

An explainable model using Graph-Wavelet for predicting biophysical properties of proteins and measuring mutational effects

Proteins hold multispectral patterns of different kinds of physicochemical features of amino acids in their structures, which can help understand proteins behavior. Here, we propose a method based on the graph-wavelet transform of signals of features of amino acids in protein residue networks derived from their structures to achieve their abstract numerical representations. Such abstract representations of protein structures hand in hand with amino-acid features can be used for different purposes, such as modelling the biophysical property of proteins. Our method outperformed graph-Fourier and convolutional neural-network-based methods in predicting the biophysical properties of proteins. Even though our method does not predict deleterious mutations, it can summarize the effect of an amino acid based on its location and neighbourhood in protein-structure using graph-wavelet to estimate its influence on the biophysical property of proteins. Such an estimate of the influence of amino-acid has the potential to explain the mechanism of the effect of deleterious non-synonymous mutations. Thus, our approach can reveal patterns of distribution of amino-acid properties in the structure of the protein in the context of a biophysical property for better classification and more insightful understanding.

bioinformatics↗

bioRxiv2025

Molecular dynamics simulations of intrinsically disordered protein regions enable biophysical interpretation of variant effect predictors

Predictive models for missense variant pathogenicity offer little functional interpretation for intrinsically disordered regions, since they rely on conservation and coevolution across homologous sequences. To understand the extent to which biophysics modulates model performance compared to genomic conservation, we model biophysics of IDRs explicitly for improved interpretation of variant effects. We develop MDmis, a method that uses biophysical features extracted from molecular dynamics (MD) simulations of IDRs to predict pathogenicity. We find that pathogenic variants in Long IDRs manifest differently, with transient order and depleted solvent access, compared to those in Short IDRs. Using MD simulations of sequences with single missense variants, we identify stronger evidence for pathogenic effects in Long IDRs compared to Short IDRs. MDmis, when combined with conservation information, achieves strong predictive accuracy of pathogenicity of variants in Long IDRs. Overall, extracting information from MD simulations can help understand the drivers of predictive performance and elucidate biophysical behaviors affected by pathogenic variants.

bioinformatics↗