Biophysical Model Research Articles

AIUPred: combining energy estimation with deep learning for the enhanced prediction of protein disorder.

Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these proteins are a challenge to study experimentally, computational methods play important roles in their characterization. One of the commonly used tools is the IUPred web server which provides prediction of disordered regions and their binding sites. IUPred is rooted in a simple biophysical model and uses a limited number of parameters largely derived on globular protein structures only. This enabled an incredibly fast and robust prediction method, however, its limitations have also become apparent in light of recent breakthrough methods using deep learning techniques. Here, we present AIUPred, a novel version of IUPred which incorporates deep learning techniques into the energy estimation framework. It achieves improved performance while keeping the robustness of the original method. Based on the evaluation of recent benchmark datasets, AIUPred scored amongst the top three single sequence based methods. With a new web server we offer fast and reliable visual analysis for users as well as options to analyze whole genomes in mere seconds with the downloadable package. AIUPred is available at https://aiupred.elte.hu.

Allosteric Cholesterol Site in Glycine Receptors Characterized through Molecular Simulations.

Glycine receptors are pentameric ligand-gated ion channels that conduct chloride ions across postsynaptic membranes to facilitate fast inhibitory neurotransmission. In addition to gating by the glycine agonist, interactions with lipids and other compounds in the surrounding membrane environment modulate their function, but molecular details of these interactions remain unclear, in particular, for cholesterol. Here, we report coarse-grained simulations in a model neuronal membrane for three zebrafish glycine receptor structures representing apparent resting, open, and desensitized states. We then converted the systems to all-atom models to examine detailed lipid interactions. Cholesterol bound to the receptor at an outer-leaflet intersubunit site, with a preference for the open and desensitized versus resting states, indicating that it can bias receptor function. Finally, we used short atomistic simulations and iterative amino acid perturbations to identify residues that may mediate allosteric gating transitions. Frequent cholesterol contacts in atomistic simulations clustered with residues identified by perturbation analysis and overlapped with mutations influencing channel function and pathology. Cholesterol binding at this site was also observed in a recently reported pig heteromeric glycine receptor. These results indicate state-dependent lipid interactions relevant to allosteric transitions of glycine receptors, including specific amino acid contacts applicable to biophysical modeling and pharmaceutical design.

Biophysics of frequency-dependent variation in paresthesia and pain relief during spinal cord stimulation.

The neurophysiological effects of spinal cord stimulation (SCS) for chronic pain are poorly understood, resulting in inefficient failure-prone programming protocols and inadequate pain relief. Nonetheless, novel stimulation patterns are regularly introduced and adopted clinically. Traditionally, paresthetic sensation is considered necessary for pain relief, although novel paradigms provide analgesia without paresthesia. However, like pain relief, the neurophysiological underpinnings of SCS-induced paresthesia are unknown. Here, we paired biophysical modeling with clinical paresthesia thresholds (of both sexes) to investigate how stimulation frequency affects the neural response to SCS relevant to paresthesia and analgesia. Specifically, we modeled the dorsal column (DC) axonal response, dorsal column nucleus (DCN) synaptic transmission, conduction failure within DC fiber collaterals, and dorsal horn network output. Importantly, we found that high-frequency stimulation reduces DC fiber activation thresholds, which in turn accurately predicts clinical paresthesia perception thresholds. Furthermore, we show that high-frequency SCS produces asynchronous DC fiber spiking and ultimately asynchronous DCN output, offering a plausible biophysical basis for why high-frequency SCS is less comfortable and produces qualitatively different sensation than low-frequency stimulation. Finally, we demonstrate that model dorsal horn network output is sensitive to SCS-inherent variations in spike timing, which could contribute to heterogeneous pain relief across patients. Importantly, we show that model DC fiber collaterals cannot reliably follow high-frequency stimulation, strongly affecting network output and typically producing anti-nociceptive effects at high frequencies. Altogether, these findings clarify how SCS affects the nervous system and provide insight into the biophysics of paresthesia generation and pain relief.Significance Statement The effects of spinal cord stimulation (SCS) on the nervous system are poorly understood, resulting in inadequate clinical success rates. Here, we use a biophysical modeling approach to investigate the neural response to SCS. We demonstrate that low- and high-frequency stimulation produce contrasting responses in the dorsal columns, brainstem, and dorsal horn. Importantly, our modeling approach was able to accurately predict clinical paresthesia thresholds as a function of frequency, as well as provide plausible biophysical explanations for frequency-dependent effects on paresthesia quality and pain relief. Overall, our results greatly enhance our understanding of the neural response to SCS, thereby offering context for interpreting clinical observations and crucial insight for development of future SCS systems.

Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions.

Many imaging techniques for biological systems-like fixation of cells coupled with fluorescence microscopy-provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. These snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution.

AAPM Task Group Report 267: A joint AAPM GEC-ESTRO report on biophysical models and tools for the planning and evaluation of brachytherapy.

Brachytherapy utilizes a multitude of radioactive sources and treatment techniques that often exhibit widely different spatial and temporal dose delivery patterns. Biophysical models, capable of modeling the key interacting effects of dose delivery patterns with the underlying cellular processes of the irradiated tissues, can be a potentially useful tool for elucidating the radiobiological effects of complex brachytherapy dose delivery patterns and for comparing their relative clinical effectiveness. While the biophysical models have been used largely in research settings by experts, it has also been used increasingly by clinical medical physicists over the last two decades. A good understanding of the potentials and limitations of the biophysical models and their intended use is critically important in the widespread use of these models. To facilitate meaningful and consistent use of biophysical models in brachytherapy, Task Group 267 (TG-267) was formed jointly with the American Association of Physics in Medicine (AAPM) and The Groupe Européen de Curiethérapie and the European Society for Radiotherapy & Oncology (GEC-ESTRO) to review the existing biophysical models, model parameters, and their use in selected brachytherapy modalities and to develop practice guidelines for clinical medical physicists regarding the selection, use, and interpretation of biophysical models. The report provides an overview of the clinical background and the rationale for the development of biophysical models in radiation oncology and, particularly, in brachytherapy; a summary of the results of literature review of the existing biophysical models that have been used in brachytherapy; a focused discussion of the applications of relevant biophysical models for five selected brachytherapy modalities; and the task group recommendations on the use, reporting, and implementation of biophysical models for brachytherapy treatment planning and evaluation. The report concludes with discussions on the challenges and opportunities in using biophysical models for brachytherapy and with an outlook for future developments.

Using biophysical modelling and marine connectivity to assess the risk of natural dispersal of non-indigenous species to comply with the Ballast Water Management Convention

AbstractThe introduction of Marine Non-Indigenous Species (NIS) poses a significant threat to global marine biodiversity and ecosystems. To mitigate this risk, the Ballast Water Management Convention (BWMC) was adopted by the UN International Maritime Organisation (IMO), setting strict criteria for discharges of ballast water. However, the BWMC permits exemptions for shipping routes operating within a geographical area, known as a Same-Risk-Area (SRA). An SRA can be established in areas where a risk assessment (RA) can conclude that the spread of NIS via ballast water is low relative to the predicted natural dispersal. Despite the BWMC's requirement for RAs to be based on modelling of the natural dispersal of NIS, no standard procedures have been established. This paper presents a methodology utilizing biophysical modelling and marine connectivity analyses to conduct SRA RA and delineation. Focusing on the Kattegat and Øresund connecting the North Sea and Baltic Sea, we examine two SRA candidates spanning Danish and Swedish waters. We provide an example on how to conduct an RA including an RA summary, and addressing findings, challenges, and prospects. Our study aims to advance the development and adoption of consistent, transparent, and scientifically robust SRA assessments for effective ballast water management.

Optimising seafood nutritional value and environmental sustainability in aquaculture through a novel integrated modelling tool applicable to IMTA and monoculture

Aquaculture policies that decrease the reliance on wild-caught fish and promote integrated multi-trophic aquaculture (IMTA) both aim to lessen the environmental footprint but often overlook economic-environmental trade-offs and neglect social, health, and broad-scale sustainability considerations. Here we present a new bio-physical model (FYNE) that bridges some of these gaps, by linking farm operational choices, such as feed choices and farm siting, with the nutritional value of the farm-gate seafood products and the resulting environmental eutrophication footprint at the farm level. The FYNE model operates both in conditions of monoculture or of IMTA production, and estimates feed composition effects on growth and omega-3 fatty acid content of Atlantic salmon (Salmo salar) and blue mussel (Mytilus edulis), and nitrogen and carbon concentrations, the latter being a measure of the environmental eutrophication footprint of the farm. The model here developed integrates for the first time (i) an ecosystem model of an IMTA and (ii) a fatty acid dynamics model, with new models for salmon feed digestibility and mussel ingestion. The outputs span (i) production analysis for simulation of biomass; (ii) farm mass-balance analysis on deposition analysis and environmental eutrophication footprint; and (iii) product nutritional quality, in terms of fatty acid content. An application of the model to an IMTA farm in Scotland (UK) demonstrates how fatty acid and environmental changes can be modelled in tandem and could be used to support the industry to optimize the nutritional value of cultured species and manage aquatic environmental eutrophication footprint at farm level, informing feed and siting decisions. The model effectively predicts changes in omega-3 fatty acids, particularly DHA and EPA, in response to variations in feed composition. Notably, our results confirm that salmon waste has limited influence on mussel growth due to the spatial separation within the IMTA system, which emphasizes the need for strategic siting to enhance system productivity through effective waste recycling. Additionally, results show that while increasing fishmeal in diets enhances environmental outcomes, it reduces levels of critical fatty acids like DHA and EPA, underscoring the need for balanced feed formulations. The method developed in this study described a novel integrated model (FYNE) that extend previous modelling work by incorporating the influence of farmed species feed composition on fatty acid dynamics as well as the farming environmental factors.

Abstract PO1-01-05: Spatial PK/PD Model with HER2 Expression for Predicting Individual Tumor Response to T-DM1

Abstract Introduction: As new antibody drug conjugates are developed, the question of which specific patients will respond to these therapies remains. This work develops a spatial PK/PD model for the antibody drug conjugate trastuzumab emtansine, T-DM1, and explores the relationship between HER2 (target) expression and other individual features of the tumor microenvironment using a computational approach. Methods: We developed a spatial PK/PD model for T-DM1 based upon an allometrically-scaled mouse model (Jumbe, Nelson L., et al., 2010) and the reported outcomes from the KRISTINE trial, which investigated T-DM1 plus pertuzumab in the neoadjuvant early-stage breast cancer setting (Hurvitz, Sara A., et al., 2018). The T-DM1 spatial PK/PD model was then integrated into our previously described 4D biophysical simulation model (Howard, Frederick M., et al., 2022). The biophysical model leverages baseline breast MRI and clinicopathologic features and predicts the individual response to a treatment regimen. Using digital twin tumors from our TumorBank to simulate the KRISTINE trial cohort (n=40), the PD model was parameterized to achieve the trial response rate. To orthogonally validate the spatial PK/PD model, volumetric response and pCR were predicted in an independent cohort of ISPY2 trial patients (n=44) who received T-DM1 followed by dose-dense doxorubicin and cyclophosphamide. We then assessed the added value of the target expression data by developing logistic regression models to predict which patients would achieve pCR based upon HER2 expression alone or HER2 expression combined with the final tumor volume simulated using the spatial PK/PD model. Results: A logistic regression model based upon HER2 expression from transcriptomic data is a strong predictor of response to T-DM1 with an odds ratio of 9.6 and an area under the ROC curve (AUC) of 0.81. The tumorHER2 model, which combines the HER2 expression with the spatial PK/PD model, improves the predictive value of the model with an odds ratio of 34.0 and an AUC of 0.88. Conclusion: The spatial PK/PD improves prognostic predictions compared to HER2 gene expression data alone. In leveraging the SBS biophysical simulation platform, the spatial PK/PD model captures the individual variability in tumor growth rate, tumor perfusion, and drug disposition, all of which influence the treatment efficacy. The rate of target expression will highly influence response to an ADC, but the biophysical model demonstrates that features of the spatial tumor microenvironment also influence response. Table 1. Performance comparison between the SBS tumorHER2model and SBSHER2 Signature. Citation Format: Nicole Hobbs, Bradley Feiger, John Cole, Tricia Carrigan, Joseph Peterson, John Pfeiffer, Daniel Cook. Spatial PK/PD Model with HER2 Expression for Predicting Individual Tumor Response to T-DM1 [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-01-05.

Modeling attenuation of photosynthetically active radiation across the optical gradient in the Laurentian Great Lakes with application to Lake Erie

Representation of subsurface photosynthetically active radiation (PAR) in biophysical models of the Laurentian Great Lakes (LGL) is imperative to their utility as tools for research and management. Here we consolidated measured vertical profiles of subsurface PAR with concurrent water quality (WQ) data from four LGL. We estimated the diffuse attenuation coefficient of PAR (Kd(PAR)) by fitting an exponential function to measured PAR over depth, and evaluated 68 regressions predicting Kd(PAR) as a function of water quality variables (Kd-WQ regressions). We compare four of the top cross-lake calibrated regressions against two published regressions trained on western Lake Erie (WLE) data. Then, as a case study, we demonstrate the utility of our cross-lake calibrated Kd-WQ regressions with a simplified biophysical model of Lake Erie consisting of the Finite Volume Community Ocean Model with submodules for simulating suspended sediment and dissolved organic carbon (FVCOM-SS-DOC). Twenty-five Kd-WQ regressions were identified as candidates for use in biophysical models based on their skill determined via cross-validation. WLE-trained Kd-WQ regressions were less able to simulate Kd(PAR) and PAR in more transparent waters compared to cross-lake calibrated Kd-WQ regressions, which translated to considerable differences in primary production estimates for the central and eastern basins when using WQ data simulated by FVCOM-SS-DOC. A cross-lake calibrated Kd-WQ regression was installed into FVCOM-SS-DOC, which then simulated spatial patterns of suspended sediments and Kd(PAR). These calibrated Kd-WQ regressions can be used in a variety of biophysical models across optically-distinct waters of the LGL to support adaptive management of nutrient inputs and fisheries

Numerics with coordinate transforms for efficient Brownian dynamics simulations

Many stochastic processes in the physical and biological sciences can be modelled as Brownian dynamics with multiplicative noise. However, numerical integrators for these processes can lose accuracy or even fail to converge when the diffusion term is configuration-dependent. One remedy is to construct a transform to a constant-diffusion process and sample the transformed process instead. In this work, we explain how coordinate-based and time-rescaling-based transforms can be used either individually or in combination to map a general class of variable-diffusion Brownian motion processes into constant-diffusion ones. The transforms are invertible, thus allowing recovery of the original dynamics. We motivate our methodology using examples in one dimension before then considering multivariate diffusion processes. We illustrate the benefits of the transforms through numerical simulations, demonstrating how the right combination of integrator and transform can improve computational efficiency and the order of convergence to the invariant distribution. Notably, the transforms that we derive are applicable to a class of multibody, anisotropic Stokes-Einstein diffusion that has applications in biophysical modelling.

The full spectrum of SLC22 OCT1 mutations illuminates the bridge between drug transporter biophysics and pharmacogenomics

Mutations in transporters can impact an individual's response to drugs and cause many diseases. Few variants in transporters have been evaluated for their functional impact. Here, we combine saturation mutagenesis and multi-phenotypic screening to dissect the impact of 11,213 missense single-amino-acid deletions, and synonymous variants across the 554 residues of OCT1, a key liver xenobiotic transporter. By quantifying in parallel expression and substrate uptake, we find that most variants exert their primary effect on protein abundance, a phenotype not commonly measured alongside function. Using our mutagenesis results combined with structure prediction and molecular dynamic simulations, we develop accurate structure-function models of the entire transport cycle, providing biophysical characterization of all known and possible human OCT1 polymorphisms. This work provides a complete functional map of OCT1 variants along with a framework for integrating functional genomics, biophysical modeling, and human genetics to predict variant effects on disease and drug efficacy.

Spectral neural approximations for models of transcriptional dynamics

The advent of high-throughput transcriptomics provides an opportunity to advance mechanistic understanding of transcriptional processes and their connections to cellular function at an unprecedented, genome-wide scale. These transcriptional systems, which involve discrete stochastic events, are naturally modeled using chemical master equations (CMEs), which can be solved for probability distributions to fit biophysical rates that govern system dynamics. While CME models have been used as standards in fluorescence transcriptomics for decades to analyze single-species RNA distributions, there are often no closed-form solutions to CMEs that model multiple species, such as nascent and mature RNA transcript counts. This has prevented the application of standard likelihood-based statistical methods for analyzing high-throughput, multi-species transcriptomic datasets using biophysical models. Inspired by recent work in machine learning to learn solutions to complex dynamical systems, we leverage neural networks and statistical understanding of system distributions to produce accurate approximations to a steady-state bivariate distribution for a model of the RNA life cycle that includes nascent and mature molecules. The steady-state distribution to this simple model has no closed-form solution and requires intensive numerical solving techniques: our approach reduces likelihood evaluation time by several orders of magnitude. We demonstrate two approaches, whereby solutions are approximated by 1) learning the weights of kernel distributions with constrained parameters or 2) learning both weights and scaling factors for parameters of kernel distributions. We show that our strategies, denoted by kernel weight regression and parameter-scaled kernel weight regression, respectively, enable broad exploration of parameter space and can be used in existing likelihood frameworks to infer transcriptional burst sizes, RNA splicing rates, and mRNA degradation rates from experimental transcriptomic data.

Biophysical Modeling of Mangrove Seedling Establishment and Survival Across an Elevation Gradient With Forest Zones

Biophysical Modeling of Mangrove Seedling Establishment and Survival Across an Elevation Gradient With Forest Zones

Proteolytic stability and aggregation in a key metabolic enzyme of bacteria

Proteins that are kinetically stable are thought to be less prone to both aggregation and proteolysis. We demonstrate that the classical lac system of Escherichia coli can be leveraged as a model system to study this relation. β-galactosidase (LacZ) plays a critical role in lactose metabolism and is an extremely stable protein that can persist in growing cells for multiple generations after expression has stopped. By attaching degradation tags to the LacZ protein, we find that LacZ can be transiently degraded during lac operon expression but once expression has stopped functional LacZ is protected from degradation. We reversibly destabilize its tetrameric assembly using α-complementation, and show that unassembled LacZ monomers and dimers can either be degraded or lead to formation of aggregates within cells, while the tetrameric state protects against proteolysis and aggregation. We show that the presence of aggregates is associated with cell death, and that these proteotoxic stress phenotypes can be alleviated by attaching an ssrA tag to LacZ monomers which leads to their degradation. We unify our findings using a biophysical model that enables the interplay of protein assembly, degradation, and aggregation to be studied quantitatively in vivo. This work may yield approaches to reversing and preventing protein-misfolding disease states, while elucidating the functions of proteolytic stability in constant and fluctuating environments.

Brain elastography in aging relates to fluid/solid trendlines.

The relatively new tools of brain elastography have established a general trendline for healthy, aging adult humans, whereby the brain's viscoelastic properties "soften" over many decades. Earlier studies of the aging brain have demonstrated a wide spectrum of changes in morphology and composition towards the later decades of lifespan. This leads to a major question of causal mechanisms: of the many changes documented in structure and composition of the aging brain, which ones drive the long term trendline for viscoelastic properties of grey matter and white matter? The issue is important for illuminating which factors brain elastography is sensitive to, defining its unique role for study of the brain and clinical diagnoses of neurological disease and injury. We address these issues by examining trendlines in aging from our elastography data, also utilizing data from an earlier landmark study of brain composition, and from a biophysics model that captures the multiscale biphasic (fluid/solid) structure of the brain. Taken together, these imply that long term changes in extracellular water in the glymphatic system of the brain along with a decline in the extracellular matrix have a profound effect on the measured viscoelastic properties. Specifically, the trendlines indicate that water tends to replace solid fraction as a function of age, then grey matter stiffness decreases inversely as water fraction squared, whereas white matter stiffness declines inversely as water fraction to the 2/3 power, a behavior consistent with the cylindrical shape of the axons. These unique behaviors point to elastography of the brain as an important macroscopic measure of underlying microscopic structural change, with direct implications for clinical studies of aging, disease, and injury.

RadPhysBio: A Radiobiological Database for the Prediction of Cell Survival upon Exposure to Ionizing Radiation.

Based on the need for radiobiological databases, in this work, we mined experimental ionizing radiation data of human cells treated with X-rays, γ-rays, carbon ions, protons and α-particles, by manually searching the relevant literature in PubMed from 1980 until 2024. In order to calculate normal and tumor cell survival α and β coefficients of the linear quadratic (LQ) established model, as well as the initial values of the double-strand breaks (DSBs) in DNA, we used WebPlotDigitizer and Python programming language. We also produced complex DNA damage results through the fast Monte Carlo code MCDS in order to complete any missing data. The calculated α/β values are in good agreement with those valued reported in the literature, where α shows a relatively good association with linear energy transfer (LET), but not β. In general, a positive correlation between DSBs and LET was observed as far as the experimental values are concerned. Furthermore, we developed a biophysical prediction model by using machine learning, which showed a good performance for α, while it underscored LET as the most important feature for its prediction. In this study, we designed and developed the novel radiobiological 'RadPhysBio' database for the prediction of irradiated cell survival (α and β coefficients of the LQ model). The incorporation of machine learning and repair models increases the applicability of our results and the spectrum of potential users.

Novel Electrophysiological Signatures of Learning and Forgetting in Human REM Sleep.

Despite the known behavioral benefits of rapid eye movement (REM) sleep, discrete neural oscillatory events in human scalp electroencephalography (EEG) linked with behavior have not been discovered. This knowledge gap hinders mechanistic understanding of the function of sleep, as well as the development of biophysical models and REM-based causal interventions. We designed a detection algorithm to identify bursts of activity in high-density, scalp EEG within theta (4-8 Hz) and alpha (8-13 Hz) bands during REM sleep. Across 38 nights of sleep, we characterized the burst events (i.e., count, duration, density, peak frequency, amplitude) in healthy, young male and female human participants (38; 21F) and investigated burst activity in relation to sleep-dependent memory tasks: hippocampal-dependent episodic verbal memory and non-hippocampal visual perceptual learning. We found greater burst count during the more REM-intensive second half of the night (p < .05), longer burst duration during the first half of the night (p < .05), but no differences across the night in density or power (p > .05). Moreover, increased alpha burst power was associated with increased overnight forgetting for episodic memory (p < .05). Furthermore, we show that increased REM theta burst activity in retinotopically specific regions was associated with better visual perceptual performance. Our work provides a critical bridge between discrete REM sleep events in human scalp EEG that support cognitive processes, and the identification of similar activity patterns in animals models that allow for further mechanistic characterization.Significance Statement Current understanding of sleep and its role cognitive processes is incomplete due to a lack of discrete electrophysiological events in human rapid eye movement (REM) sleep detectable via scalp EEG. Our work remedies this gap in knowledge by designing an open-source, computational approach to identify electrophysiological alpha and theta burst events in REM sleep. Additionally, we provide evidence that these burst events are functionally important for learning and memory. Defining burst events in human REM will contribute to the development of a comprehensive mechanistic model of how sleep as a whole, and REM specifically, facilitate cognitive processes, and provide a deeper understanding of the fundamental electrophysiological properties of REM sleep that are distinct from non-REM sleep.

Combining population genomics and biophysical modelling to assess connectivity patterns in an Antarctic fish.

Connectivity is a fundamental process of population dynamics in marine ecosystems. In the last decade, with the emergence of new methods, combining different approaches to understand the patterns of connectivity among populations and their regulation has become increasingly feasible. The Western Antarctic Peninsula (WAP) is characterized by complex oceanographic dynamics, where local conditions could act as barriers to population connectivity. Here, the notothenioid fish Harpagifer antarcticus, a demersal species with a complex life cycle (adults with poor swim capabilities and pelagic larvae), was used to assess connectivity along the WAP by combining biophysical modelling and population genomics methods. Both approaches showed congruent patterns. Areas of larvae retention and low potential connectivity, observed in the biophysical model output, coincide with four genetic groups within the WAP: (1) South Shetland Islands, (2) Bransfield Strait, (3) the central and (4) the southern area of WAP (Marguerite Bay). These genetic groups exhibited limited gene flow between them, consistent with local oceanographic conditions, which would represent barriers to larval dispersal. The joint effect of geographic distance and larval dispersal by ocean currents had a greater influence on the observed population structure than each variable evaluated separately. The combined effect of geographic distance and a complex oceanographic dynamic would be generating limited levels of population connectivity in the fish H. antarcticus along the WAP. Based on this, population connectivity estimations and priority areas for conservation were discussed, considering the marine protected area proposed for this threatened region of the Southern Ocean.

Biophysics-inspired spike rate adaptation for computationally efficient phenomenological nerve modeling

This study introduces and evaluates the PHAST+ model, part of a computational framework designed to simulate the behavior of auditory nerve fibers in response to the electrical stimulation from a cochlear implant. PHAST+ incorporates a highly efficient method for calculating accommodation and adaptation, making it particularly suited for simulations over extended stimulus durations. The proposed method uses a leaky integrator inspired by classic biophysical nerve models. Through evaluation against single-fiber animal data, our findings demonstrate the model’s effectiveness across various stimuli, including short pulse trains with variable amplitudes and rates. Notably, the PHAST+ model performs better than its predecessor, PHAST (a phenomenological model by van Gendt et al.), particularly in simulations of prolonged neural responses. While PHAST+ is optimized primarily on spike rate decay, it shows good behavior on several other neural measures, such as vector strength and degree of adaptation. The future implications of this research are promising. PHAST+ drastically reduces the computational burden to allow the real-time simulation of neural behavior over extended periods, opening the door to future simulations of psychophysical experiments and multi-electrode stimuli for evaluating novel speech-coding strategies for cochlear implants.

SEEIIST – a new research facility for tumor treatment and biomedical investigations with proton and ion beams

Abstract Purpose A project for the establishment of an international intergovernmental scientific organization in the region of South East Europe (South East European Institute for Sustainable Technologies - SEEIIST) is currently in progress. SEEIIST is an intergovernmental effort supported by nine countries from the region (Albania, Bosnia and Herzegovina, Bulgaria, Greece, Kosovo (This designation is without prejudice to positions on status, and is in line with UNSC 1244 and the ICJ Opinion on the Kosovo Declaration of Independence.), Montenegro, North Macedonia, Serbia and Slovenia, while Switzerland is an observer). The main goals of the project are to establish a unique research infrastructure and to form a research nucleus in the region of South-East Europe, to boost the technology transfer from European laboratories like CERN and others, to promote collaboration between science, technology, and industry, to mitigate tensions between countries in the region, and to provide platforms for the development of education. Methods The research infrastructure will include a powerful next-generation accelerator that will provide proton and ion beams for tumor treatment and biomedical research. The center will comprise of three rooms for patient treatment and two large experimental halls. Results A wide pre-clinical and clinical research program in the fields of radiobiology, medical physics, medical imaging, biophysical modeling and planning of patient treatment, design and investigation of new materials, production of isotopes for diagnosis, and treatment of patients are envisaged. Conclusions This infrastructure is unique for Europe and will attract researchers from all European countries to perform their investigations.