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51 results for “biophysical modelling”
Data from: Reconstruction of larval origins based on genetic relatedness and biophysical modeling
The assessment of the mechanisms and patterns of larval connectivity between geographically separated populations leads to a better understanding of benthic marine population dynamics, especially in commercially valuable species. This study investigated for the first time the fine-scale temporal genetic variability of new settlers and their origins in a benthic marine organism with one of the longest pelagic larval phases, the Caribbean spiny lobster (Panulirus argus). We genotyped newly settled postlarvae in the Florida Keys and adults of spiny lobster from the Florida Keys and throughout the Caribbean Sea. We identified strong larval connectivity between Dominican Republic, Belize, Nicaragua, the Florida Keys, and West-Florida. The larval dispersal modeling suggests that Florida's lobster population could receive recruits from within and from other areas outside its state and national maritime boundaries. The genetic analyses refine the oceanographic model indicating that the connectivity patterns described could also result from unknown parental populations sourcing adults and postlarvae in different spawning seasons to the Florida Keys. We discuss the importance of small temporal scales to identify patterns in larval export. Our findings are significant on two levels. From the larval dispersal perspective, genetic results and biophysical modeling identify patterns of gene flow enhancing persistence of local populations. From an economic and fishery perspective, P. argus is the most important commercial species in the Caribbean and our results inform how considering larval source and sink dynamics across international boundaries could improve management plans at local, national, and regional levels.
Data from: Biophysical model of water economy can explain geographic gradients in body size in anurans
Geographical gradients of body size express climate-driven constraints on animals, but whether they exist and what cause them in ectotherms remains contentious. For amphibians, the water conservation hypothesis posits that larger bodies reduce evaporative water loss (EWL) along dehydrating gradients. To address this hypothesis mechanistically, we build upon well-established biophysical equations of water exchange in anurans to propose a state-transition model that predicts an increase of either body size or resistance to EWL as alternative specialization along dehydrating gradients. The model predicts that species whose water economy is more sensitive to variation in body size than to variation in resistance to EWL should increase in size with response to increasing potential evapotranspiration (PET). To evaluate the model predictions, we combine physiological measurements of resistance to EWL with geographic data of body size for four different anuran species. Only one species, Dendropsophus minutus, was predicted to exhibit a positive body size–PET relationship. Results were as predicted for all cases, with one species – Boana faber – showing a negative relationship. Based on a mathematical model verified empirically, we show that clines of body size among anurans depend on the current values of those traits and emerges as an advantage for water conservation. Our model offers a mechanistic and compelling explanation for the cause and the variation of gradients of body size in anurans.
Data from: Biophysical modeling of the temporal niche: from first principles to the evolution of activity patterns
Most mammals can be characterized as nocturnal or diurnal. However infrequently, species may overcome evolutionary constraints and alter their activity patterns. We modeled the fundamental temporal niche of a diurnal desert rodent, the golden spiny mouse, Acomys russatus. This species can shift into nocturnal activity in the absence of its congener, the common spiny mouse, A. cahirinus, suggesting that it was competitively driven into diurnality, and that this shift in a small desert rodent may involve physiological costs. Therefore, we compared metabolic costs of diurnal vs. nocturnal activity using a biophysical model to evaluate the preferred temporal niche of this species. The model predicted that energy expenditure during foraging is almost always lower during the day except during mid-day in summer at the less sheltered microhabitat. We also found that a shift in summer to foraging in less sheltered microhabitats in response to predation pressure and food availability involves a significant physiological cost moderated by midday reduction in activity. Thus adaptation to diurnality may reflect the 'ghost of competition past'; Climate-driven diurnality is an alternative but less likely hypothesis. While climate is considered to play a major role in the physiology and evolution of mammals, this is the first study to model its effect on the evolution of activity patterns of mammals.
Connectivity Matrices from biophysical modelling studies for A. millepora coral larvae in the Great Barrier Reef (Australia); present day and future scenarios
<p>These data contain connectivity matrices from biophysical modelling simulations of the dispersal of <em>Acropora millepora</em> coral larvae in the southern Great Barrier Reef (Australia), under present-day and future climate scenarios. The connectivity matrices represent modelled strength of larval transfer from one reef to another, and were obtained using a coupled reef-scale, high-resolution, depth-integrated finite element hydrodynamic model (SLIM) of water currents in the Great Barrier Reef, and Individual-Based particle tracking module. Biological parameters to model larval acquisition and loss of competency and mortality were based on the results of experiments detailed in the related journal article. We include connectivity matrices for 2 different water temperature scenarios representing current and future climates, for three different recent spawning seasons (2008, 2009, 2010), and also for scenarios where low-frequency currents through the domain are modulated to mimic the likely effects from future changes to large-scale circulation extracted from CMIP5 global climate models.</p> <p>We also include files summarising the relative changes, per reef, to certain key connectivity metrics between the 2 temperature scenarios (dispersal distance, local retention, number of incoming connections, "source index", and present day "source index" - all metrics are defined in the related journal article and Methods section of this metadata), averaged over all 3 spawning seasons modelled, as plotted in Figs 1a-e in the related journal article. Additionally, we include a separate file summarising changes to reef recovery times following a disturbance to coral cover between the 2 temperature scenarios, per reef, as modelled using the meta-population model described in the related journal article, and as shown in Fig 2 of the article.</p>
Data from: Resolving the mesoscopic missing link: biophysical modeling of EEG from cortical columns in primates
<p class="MsoNormal"><span>Event-related potentials (ERP) are among the most widely measured indices for studying human <span>cognition. While their timing and magnitude provide valuable insights, their usefulness is limited by our understanding of their neural generators at the circuit level. Inverse source localization offers insights into such generators, but their solutions are not unique. To address this problem, scientists have assumed the source space generating such signals comprises a set of discrete equivalent current dipoles, representing the activity of small cortical regions. Based on this notion, theoretical studies have employed forward modeling of scalp potentials to understand how changes in circuit-level dynamics translate into macroscopic ERPs. However, experimental validation is lacking because it requires <em>in vivo</em> measurements of intracranial brain sources. Laminar local field potentials (LFP) offer a mechanism for estimating intracranial current sources. Yet, a theoretical link between LFPs and intracranial brain sources is missing. Here, we present a forward modeling approach for estimating mesoscopic intracranial brain sources from LFPs and predict their contribution to macroscopic ERPs. We evaluate the accuracy of this LFP-based representation of brain sources utilizing synthetic laminar neurophysiological measurements and then demonstrate the power of the approach <em>in vivo</em> to clarify the source of a representative cognitive ERP component. To that end, </span>LFP was measured across the cortical layers of visual area V4 in macaque monkeys performing an attention demanding task. <span>We show that area V4 generates dipoles through layer-specific transsynaptic currents that biophysically recapitulate the ERP component through the detailed forward modeling. The constraints imposed on EEG production by this method also revealed an important dissociation between computational and biophysical contributors. As such, this approach </span>represents an important bridge between laminar microcircuitry, through the mesoscopic activity of cortical columns to the patterns of EEG we measure at the scalp. </span></p>
Live imaging and biophysical modeling support a button-based mechanism of somatic homolog pairing in Drosophila
<p></p><p>3D eukaryotic genome organization provides the structural basis for gene regulation. In Drosophila melanogaster, genome folding is characterized by somatic homolog pairing, where homologous chromosomes are intimately paired from end to end; however, how homologs identify one another and pair has remained mysterious. Recently, this process has been proposed to be driven by specifically interacting 'buttons' encoded along chromosomes. Here, we turned this hypothesis into a quantitative biophysical model to demonstrate that a button-based mechanism can lead to chromosome-wide pairing. We tested our model using live-imaging measurements of chromosomal loci tagged with the MS2 and PP7 nascent RNA labeling systems. We show solid agreement between model predictions and experiments in the pairing dynamics of individual homologous loci. Our results strongly support a button-based mechanism of somatic homolog pairing in Drosophila and provide a theoretical framework for revealing the molecular identity and regulation of buttons.</p><p></p>
Biophysical models unravel associations between glucocorticoids and thermoregulatory costs across avian species
<ol> <li>Glucocorticoids (GCs) are metabolic hormones that regulate physiological and behavioural responses to environmental change and mediate homeostasis maintenance in vertebrates. Despite the assumption that GCs covary with energy metabolism, we yet lack a mechanistic understanding of how environmental factors such as temperature modulate GC variation through their effect on organismal energy balance. In particular, the mechanisms linking temperature-dependent metabolic rate and GCs at broad spatial scales and across species remain poorly investigated.</li> <li>Here we used biophysical models to calculate thermoregulatory costs (i.e., the amount of heat required to keep body temperature in homeostasis) of free-living birds as a function of environmental conditions, body size, shape, and insulating layer of feathers. We then investigated the link between ambient temperature, cost of thermoregulation and baseline plasma GC concentrations in a comparative study including GC data from 94 bird species from HormoneBase.</li> <li>We found a significant, positive association between thermoregulatory costs and baseline GC concentrations. Interestingly, models including thermoregulatory costs better explained GC variation when compared to those including ambient temperature as a predictor variable. This result suggests that body size and shape fundamentally modulate energy requirements for thermoregulation and thereby GC concentrations in the wild.</li> <li>By providing a mechanistic description of the link between ambient temperature and thermoregulatory metabolism, biophysical models provide a tool to predict the impact of environmental conditions on energy metabolism. Our work demonstrates that differences in thermoregulation modulate variation in GC concentrations across a broad climatic gradient.</li> </ol>
Forest Types Show Divergent Biophysical Responses After Fire: Challenges to Ecological Modeling
<p>Datasets and scripts (MATLAB and R) used to document patterns of post-fire biophysical dynamics in seven forest types and 21 Level III ecoregions of the western United States.</p>
Soil and atmospheric drought explain the biophysical conductance responses in diagnostic and prognostic evaporation models over two contrasting European forest sites
<p>This contains the datasets and codes that were used to generate the results and discussions in the manuscript.</p>
Data from: Seascape genetics and biophysical connectivity modelling support conservation of the seagrass Zostera marina in the Skagerrak-Kattegat region of the eastern North Sea
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Data from: Reconstruction of larval origins based on genetic relatedness and biophysical modeling
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Live imaging and biophysical modeling support a button-based mechanism of somatic homolog pairing in Drosophila
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Connectivity Matrices from biophysical modelling studies for A. millepora coral larvae in the Great Barrier Reef (Australia); present day and future scenarios
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Biophysical models unravel associations between glucocorticoids and thermoregulatory costs across avian species
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Data from: Resolving the mesoscopic missing link: biophysical modeling of EEG from cortical columns in primates
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Data from: Biophysical modeling of the temporal niche: from first principles to the evolution of activity patterns
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Data from: Biophysical model of water economy can explain geographic gradients in body size in anurans
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Data from: Coalescent and biophysical models of stepping-stone gene flow in Neritid snails
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Curated set of cone bipolar cell morphologies for biophysical modelling
<p>This dataset contains 40 cone bipolar cell morphologies in the .swc file format (skeleton and radius) based on the e2006 EM dataset (Helmstaedter et al., 2013). All morphologies have been manually proof-read. Individual file names consist of the cell ID from the original EM dataset and the cell type following (Behrens et al., 2016).</p>
Data from: Biophysically inspired model for functionalized nanocarrier adhesion to cell surface: roles of protein expression and mechanical factors
In order to achieve selective targeting of affinity–ligand coated nanoparticles to the target tissue, it is essential to understand the key mechanisms that govern their capture by the target cell. Next-generation pharmacokinetic (PK) models that systematically account for proteomic and mechanical factors can accelerate the design, validation and translation of targeted nanocarriers (NCs) in the clinic. Towards this objective, we have developed a computational model to delineate the roles played by target protein expression and mechanical factors of the target cell membrane in determining the avidity of functionalized NCs to live cells. Model results show quantitative agreement with in vivo experiments when specific and non-specific contributions to NC binding are taken into account. The specific contributions are accounted for through extensive simulations of multivalent receptor–ligand interactions, membrane mechanics and entropic factors such as membrane undulations and receptor translation. The computed NC avidity is strongly dependent on ligand density, receptor expression, bending mechanics of the target cell membrane, as well as entropic factors associated with the membrane and the receptor motion. Our computational model can predict the in vivo targeting levels of the intracellular adhesion molecule-1 (ICAM1)-coated NCs targeted to the lung, heart, kidney, liver and spleen of mouse, when the contributions due to endothelial capture are accounted for. The effect of other cells (such as monocytes, etc.) do not improve the model predictions at steady state. We demonstrate the predictive utility of our model by predicting partitioning coefficients of functionalized NCs in mice and human tissues and report the statistical accuracy of our model predictions under different scenarios.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.