Biophysical Model Research Articles

Explainable machine learning for predicting stomatal conductance across multiple plant functional types

Stomatal conductance (gs) is a key leaf-level function controlling water, carbon, and energy exchange between vegetation and the surrounding environment. Conventionally, semi-empirical models have been used to model gs, but these models require re-parameterization as ecosystems undergo phenological changes over the growing season. In contrast, machine learning (ML) models offer a potential path to overcome this problem but are less interpretable than process-based models. This study explores ML as an approach to develop flexible and robust models of gs for a range of plant functional types (PFTs), including C3 crops, C3 grasses, shrubs, and tree species across different continents. An explainable machine-learning approach (eXML) was used here to provide novel interpretations and insights into the ML model formulations and relative predictor importance. We contrast the performance of three ML architectures: extreme gradient boosting, random forests, and neural networks. Models were developed and examined using many combinations of environmental and physiological predictors. The results demonstrated that ML models significantly outperform conventional semi-empirical models in predicting gs responses to the environment, while not requiring re-parameterization as is required in the semi-empirical paradigm. Particular focus is placed on models formulated around predictor sets that are: (a) relevant to gs estimation in modern terrestrial biophysical simulation models, and (b) composed of variables describing environmental and physiological drivers that can be remotely sensed non-invasively. “Generalized” models developed using data from all four PFTs demonstrated strong predictive performance using only three predictor variables, capturing 63–80 % of the variability in stomatal conductance across all ML architectures. Four predictor variables resulted in models capturing 79–83 % of gs variability, and models developed using all five predictor variables examined here were able to capture as much as 87 % of gs variability across all PFTs. Uncertainty in gs predictions was quantified using quantile regression. Shapley additive explanations was applied to unravel instance-based positive and negative contributions of environmental and physiological predictors to gs modeling, while illustrating that the models are consistent with the underlying ecophysiology. This work demonstrates the power of ML to introduce a new paradigm in the simulation of highly dynamic ecophysiological processes critical to environmental prediction.

Mutational biases favor complexity increases in protein interaction networks after gene duplication

Biological systems can gain complexity over time. While some of these transitions are likely driven by natural selection, the extent to which they occur without providing an adaptive benefit is unknown. At the molecular level, one example is heteromeric complexes replacing homomeric ones following gene duplication. Here, we build a biophysical model and simulate the evolution of homodimers and heterodimers following gene duplication using distributions of mutational effects inferred from available protein structures. We keep the specific activity of each dimer identical, so their concentrations drift neutrally without new functions. We show that for more than 60% of tested dimer structures, the relative concentration of the heteromer increases over time due to mutational biases that favor the heterodimer. However, allowing mutational effects on synthesis rates and differences in the specific activity of homo- and heterodimers can limit or reverse the observed bias toward heterodimers. Our results show that the accumulation of more complex protein quaternary structures is likely under neutral evolution, and that natural selection would be needed to reverse this tendency.

Modification of brain conductivity in human focal epilepsy: A model-based estimation from stereoelectroencephalography.

We have developed a novel method for estimating brain tissue electrical conductivity using low-intensity pulse stereoelectroencephalography (SEEG) stimulation coupled with biophysical modeling. We evaluated the hypothesis that brain conductivity is correlated with the degree of epileptogenicity in patients with drug-resistant focal epilepsy. We used bipolar low-intensity biphasic pulse stimulation (.2 mA) followed by a postprocessing pipeline for estimating brain conductivity. This processing is based on biophysical modeling of the electrical potential induced in brain tissue between the stimulated contacts in response to pulse stimulation. We estimated the degree of epileptogenicity using a semi-automatic method quantifying the dynamic of fast discharge at seizure onset: the epileptogenicity index (EI). We also investigated how the location of stimulation within specific anatomical brain regions or within lesional tissue impacts brain conductivity. We performed 1034 stimulations of 511 bipolar channels in 16 patients. We found that brain conductivity was lower in the epileptogenic zone (EZ; unpaired median difference = .064, p < .001) and inversely correlated with the epileptogenic index value (p < .001, Spearman rho = -.32). Conductivity values were also influenced by anatomical site, location within lesion, and delay between SEEG electrode implantation and stimulation, and had significant interpatient variability. Mixed model multivariate analysis showed that conductivity is significantly associated with EI (F = 13.45, p < .001), anatomical regions (F = 5.586, p < .001), delay since implantation (F = 14.71, p = .003), and age at SEEG (F = 6.591, p = .027), but not with the type of lesion (F = .372, p = .773) or the delay since last seizure (F = 1.592, p = .235). We provide a novel model-based method for estimating brain conductivity from SEEG low-intensity pulse stimulations. The brain tissue conductivity is lower in EZ as compared to non-EZ. Conductivity also varies significantly across anatomical brain regions. Involved pathophysiological processes may include changes in the extracellular space (especially volume or tortuosity) in epileptic tissue.

A nonlinear meccano for Alzheimer's emergence by amyloid β-mediated glutamatergic hyperactivity

The pathophysiological process of Alzheimer's disease (AD) is believed to begin many years before the formal diagnosis of AD dementia. This protracted preclinical phase offers a crucial window for potential therapeutic interventions, yet its comprehensive characterization remains elusive. Accumulating evidence suggests that amyloid-β (Aβ) may mediate neuronal hyperactivity in circuit dysfunction in the early stages of AD. At the same time, neural activity can also facilitate Aβ accumulation through intricate feed-forward interactions, complicating elucidating the conditions governing Aβ-dependent hyperactivity and its diagnostic utility. In this study, we use biophysical modeling to shed light on such conditions. Our analysis reveals that the inherently nonlinear nature of the underlying molecular interactions can give rise to the emergence of various modes of hyperactivity. This diversity in the mechanisms of hyperactivity may ultimately account for a spectrum of AD manifestations.

Theoretical evaluation of the impact of diverse treatment conditions by calculation of the tumor control probability (TCP) of simulated cervical cancer Hyperthermia-Radiotherapy (HT-RT) treatments in-silico

Introduction Hyperthermia (HT) induces various cellular biological processes, such as repair impairment and direct HT cell killing. In this context, in-silico biophysical models that translate deviations in the treatment conditions into clinical outcome variations may be used to study the extent of such processes and their influence on combined hyperthermia plus radiotherapy (HT + RT) treatments under varying conditions. Methods An extended linear-quadratic model calibrated for SiHa and HeLa cell lines (cervical cancer) was used to theoretically study the impact of varying HT treatment conditions on radiosensitization and direct HT cell killing effect. Simulated patients were generated to compute the Tumor Control Probability (TCP) under different HT conditions (number of HT sessions, temperature and time interval), which were randomly selected within margins based on reported patient data. Results Under the studied conditions, model-based simulations suggested a treatment improvement with a total CEM43 thermal dose of approximately 10 min. Additionally, for a given thermal dose, TCP increased with the number of HT sessions. Furthermore, in the simulations, we showed that the TCP dependence on the temperature/time interval is more correlated with the mean value than with the minimum/maximum value and that comparing the treatment outcome with the mean temperature can be an excellent strategy for studying the time interval effect. Conclusion The use of thermoradiobiological models allows us to theoretically study the impact of varying thermal conditions on HT + RT treatment outcomes. This approach can be used to optimize HT treatments, design clinical trials, and interpret patient data.

Mapping tissue microstructure of brain white matter in vivo in health and disease using diffusion MRI

Abstract Diffusion magnetic resonance imaging offers unique in vivo sensitivity to tissue microstructure in brain white matter, which undergoes significant changes during development and is compromised in virtually every neurological disorder. Yet, the challenge is to develop biomarkers that are specific to micrometer-scale cellular features in a human MRI scan of a few minutes. Here, we quantify the sensitivity and specificity of a multicompartment diffusion modeling framework to the density, orientation, and integrity of axons. We demonstrate that using a machine learning-based estimator, our biophysical model captures the morphological changes of axons in early development, acute ischemia, and multiple sclerosis (total N = 821). The methodology of microstructure mapping is widely applicable in clinical settings and in large imaging consortium data to study development, aging, and pathology.

Modelling in Support of Better Agricultural and Food Policies: the JRC's Integrated Agro‐economic Modelling platform (iMAP)

SummaryResearch in the area of agro‐economic modelling has been one of the key contributions of the Joint Research Centre (JRC) to the development of better agri‐food policies in the EU. This article provides an overview of the development of the JRC's integrated agro‐economic modelling platform since its establishment in 2005. We highlight the main contributions to policy analysis and describe in detail two key areas, namely the provision of medium‐term projections for agricultural markets and the analysis of various CAP proposals. Furthermore, we describe the gradual shift to expand the capabilities of individual models within the platform to encompass aspects beyond traditional agro‐economic aspects and incorporate environmental considerations. Model integration remains a focus both within the platform at different levels and scales, and in conjunction with biophysical models. This, together with incorporating knowledge from other fields of economic analysis is fundamental to capture changing policy objectives.

Data-driven control of oscillator networks with population-level measurement.

Controlling complex networks of nonlinear limit-cycle oscillators is an important problem pertinent to various applications in engineering and natural sciences. While in recent years the control of oscillator populations with comprehensive biophysical models or simplified models, e.g., phase models, has seen notable advances, learning appropriate controls directly from data without prior model assumptions or pre-existing data remains a challenging and less developed area of research. In this paper, we address this problem by leveraging the network's current dynamics to iteratively learn an appropriate control online without constructing a global model of the system. We illustrate through a range of numerical simulations that the proposed technique can effectively regulate synchrony in various oscillator networks after a small number of trials using only one input and one noisy population-level output measurement. We provide a theoretical analysis of our approach, illustrate its robustness to system variations, and compare its performance with existing model-based and data-driven approaches.

Efficient Estimation of Directed Connectivity in Nonlinear and Nonstationary Spiking Neuron Networks.

Studying directed connectivity within spiking neuron networks can help understand neural mechanisms. Existing methods assume linear time-invariant neural dynamics with a fixed time lag in information transmission, while spiking networks usually involve complex dynamics that are nonlinear and nonstationary, and have varying time lags. We develop a Gated Recurrent Unit (GRU)-Point Process (PP) method to estimate directed connectivity within spiking networks. We use a GRU to describe the dependency of the target neuron's current firing rate on the source neurons' past spiking events and a PP to relate the target neuron's firing rate to its current 0-1 spiking event. The GRU model uses recurrent states and gate/activation functions to deal with varying time lags, nonlinearity, and nonstationarity in a parameter-efficient manner. We estimate the model using maximum likelihood and compute directed information as our measure of directed connectivity. We conduct simulations using artificial spiking networks and a biophysical model of Parkinson's disease to show that GRU-PP systematically addresses varying time lags, nonlinearity, and nonstationarity, and estimates directed connectivity with high accuracy and data efficiency. We also use a non-human-primate dataset to show that GRU-PP correctly identifies the biophysically-plausible stronger PMd-to-M1 connectivity than M1-to-PMd connectivity during reaching. In all experiments, the GRU-PP consistently outperforms state-of-the-art methods. The GRU-PP method efficiently estimates directed connectivity in varying time lag, nonlinear, and nonstationary spiking neuron networks. The proposed method can serve as a directed connectivity analysis tool for investigating complex spiking neuron network dynamics.

Assessing carbon storage and sequestration benefits of urban greening in Nador City, Morocco, utilizing GIS and the InVEST model

In the 21st century, tackling climate change is an important challenge, and the absorption of CO2 via urban greening employing photosynthesis offers a potential answer to the climate issue. Within this work, the city of Nador is chosen as a case study. The initiative in question proposes an enhanced technique focused on estimating the advantages of urban greening in terms of carbon storage and sequestration. This technique combines the use of Geographic Information Systems (GIS) tools and the InVEST model, enabling the study of land use and land cover (LULC) scenarios within the location. These scenarios contain numerous alternatives, ranging from the adoption of intense and extensive green roofs to the establishment of gardens and woodland areas. The carbon storage modeling technique depends on the exploitation of land use maps, reinforced by a bibliographic examination of the key carbon reservoirs. The analysis of results from biophysical modeling indicates a considerable increase of over 45.36 % compared to the existing condition when the option of extensive green roofs is chosen. In the setting of extensive green roofs, this rise reaches a rate of 64.20 %. Furthermore, the restoration of the Gourougou forest north of the city leads to an increase of 70.30 %. The adoption of diverse combinations, such as the integration of green roofs with urban vegetation, greatly helps to further enhancing carbon storage. From a financial perspective, the additional value can range from $2,012,825.16 to $3,601,025.53, depending on the chosen scenario. This financial assessment highlights the possible advantages that might be converted into carbon credits in return for emission reduction, partially offsetting the expenses associated with the adoption of each greening solution. Ultimately, these findings give a chance for urban planners and green architecture professionals to perform in-depth planning analyses.

Biophysical Modeling of Synaptic Plasticity.

Dendritic spines are small, bulbous compartments that function as postsynaptic sites and undergo intense biochemical and biophysical activity. The role of the myriad signaling pathways that are implicated in synaptic plasticity is well studied. A recent abundance of quantitative experimental data has made the events associated with synaptic plasticity amenable to quantitative biophysical modeling. Spines are also fascinating biophysical computational units because spine geometry, signal transduction, and mechanics work in a complex feedback loop to tune synaptic plasticity. In this sense, ideas from modeling cell motility can inspire us to develop multiscale approaches for predictive modeling of synaptic plasticity. In this article, we review the key steps in postsynaptic plasticity with a specific focus on the impact of spine geometry on signaling, cytoskeleton rearrangement, and membrane mechanics. We summarize the main experimental observations and highlight how theory and computation can aid our understanding of these complex processes.

Biophysical Modeling of Actin-Mediated Structural Plasticity Reveals Mechanical Adaptation in Dendritic Spines.

Synaptic plasticity is important for learning and memory formation; it describes the strengthening or weakening of connections between synapses. The postsynaptic part of excitatory synapses resides in dendritic spines, which are small protrusions on the dendrites. One of the key features of synaptic plasticity is its correlation with the size of these spines. A long-lasting synaptic strength increase [long-term potentiation (LTP)] is only possible through the reconfiguration of the actin spine cytoskeleton. Here, we develop an experimentally informed three-dimensional computational model in a moving boundary framework to investigate this reconfiguration. Our model describes the reactions between actin and actin-binding proteins leading to the cytoskeleton remodeling and their effect on the spine membrane shape to examine the spine enlargement upon LTP. Moreover, we find that the incorporation of perisynaptic elements enhances spine enlargement upon LTP, exhibiting the importance of accounting for these elements when studying structural LTP. Our model shows adaptation to repeated stimuli resulting from the interactions between spine proteins and mechanical forces.

A linearized modeling framework for the frequency selectivity in neurons postsynaptic to vibration receptors.

Vibration is an indispensable part of the tactile perception, which is encoded to oscillatory synaptic currents by receptors and transferred to neurons in the brain. The A2 and B1 neurons in the drosophila brain postsynaptic to the vibration receptors exhibit selective preferences for oscillatory synaptic currents with different frequencies, which is caused by the specific voltage-gated Na+ and K+ currents that both oppose the variations in membrane potential. To understand the peculiar role of the Na+ and K+ currents in shaping the filtering property of A2 and B1 neurons, we develop a linearized modeling framework that allows to systematically change the activation properties of these ionic channels. A data-driven conductance-based biophysical model is used to reproduce the frequency filtering of oscillatory synaptic inputs. Then, this data-driven model is linearized at the resting potential and its frequency response is calculated based on the transfer function, which is described by the magnitude-frequency curve. When we regulate the activation properties of the Na+ and K+ channels by changing the biophysical parameters, the dominant pole of the transfer function is found to be highly correlated with the fluctuation of the active current, which represents the strength of suppression of slow voltage variation. Meanwhile, the dominant pole also shapes the magnitude-frequency curve and further qualitatively determines the filtering property of the model. The transfer function provides a parsimonious description of how the biophysical parameters in Na+ and K+ channels change the inhibition of slow variations in membrane potential by Na+ and K+ currents, and further illustrates the relationship between the filtering properties and the activation properties of Na+ and K+ channels. This computational framework with the data-driven conductance-based biophysical model and its linearized model contributes to understanding the transmission and filtering of vibration stimulus in the tactile system.

A neurophysiological basis for aperiodic EEG and the background spectral trend

Electroencephalograms (EEGs) display a mixture of rhythmic and broadband fluctuations, the latter manifesting as an apparent 1/f spectral trend. While network oscillations are known to generate rhythmic EEG, the neural basis of broadband EEG remains unexplained. Here, we use biophysical modelling to show that aperiodic neural activity can generate detectable scalp potentials and shape broadband EEG features, but that these aperiodic signals do not significantly perturb brain rhythm quantification. Further model analysis demonstrated that rhythmic EEG signals are profoundly corrupted by shifts in synapse properties. To examine this scenario, we recorded EEGs of human subjects being administered propofol, a general anesthetic and GABA receptor agonist. Drug administration caused broadband EEG changes that quantitatively matched propofol’s known effects on GABA receptors. We used our model to correct for these confounding broadband changes, which revealed that delta power, uniquely, increased within seconds of individuals losing consciousness. Altogether, this work details how EEG signals are shaped by neurophysiological factors other than brain rhythms and elucidates how these signals can undermine traditional EEG interpretation.

Interactions between calcium-induced arrhythmia triggers and the electrophysiological-anatomical substrate underlying the induction of atrial fibrillation.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is sustained by spontaneous focal excitations and re-entry. Spontaneous electrical firing in the pulmonary vein (PV) sleeves is implicated in AF generation. The aim of this simulation study was to identify the mechanisms determining the localisation of AF triggers in the PVs and their contribution to the genesis of AF. A novel biophysical model of the canine atria was used that integrates stochastic, spontaneous subcellular Ca2+ release events (SCRE) with regional electrophysiological heterogeneity in ionic properties and a detailed three-dimensional model of atrial anatomy, microarchitecture and patchy fibrosis. Simulations highlighted the importance of the smaller inward rectifier potassium current (IK1 ) in PV cells compared to the surrounding atria, which enabled SCRE more readily to result in delayed-afterdepolarisations that induced triggered activity. There was a leftward shift in the dependence of the probability of triggered activity on sarcoplasmic reticulum Ca2+ load. This feature was accentuated in 3D tissue compared to single cells (Δ half-maximal [Ca2+ ]SR =58μM vs. 22μM). In 3D atria incorporating electrical heterogeneity, excitations preferentially emerged from the PV region. These triggered focal excitations resulted in transient re-entry in the left atrium. Addition of fibrotic patches promoted localised emergence of focal excitations and wavebreaks that had a more substantial impact on generating AF-like patterns than the PVs. Thus, a reduced IK1 , less negative resting membrane potential, and fibrosis-induced changes of the electrotonic load all contribute to the emergence of complex excitation patterns from spontaneous focal triggers. KEY POINTS: Focal excitations in the atria are most commonly associated with the pulmonary veins, but the mechanisms for this localisation are yet to be elucidated. We applied a multi-scale computational modelling approach to elucidate the mechanisms underlying such localisations. Myocytes in the pulmonary vein region of the atria have a less negative resting membrane potential and reduced time-independent potassium current; we demonstrate that both of these factors promote triggered activity in single cells and tissues. The less negative resting membrane potential also contributes to heterogeneous inactivation of the fast sodium current, which can enable re-entrant-like excitation patterns to emerge without traditional conduction block.

An Explanatory Model of Red Lentil Seed Coat Colour to Manage Degradation in Quality during Storage

This study presents an explanatory biophysical model developed and validated to simulate seed coat colour traits including CIE L*, a*, and b* changes over time for stored lentil cultivars PBA Hallmark, PBA Hurricane, PBA Bolt, and PBA Jumbo2 under diverse storage conditions. The model showed robust performance for all cultivars, with R2 values ≥ 0.89 and RMSE values ≤ 0.0019 for all seed coat colour traits. Laboratory validation at 35 °C demonstrated a high agreement (Lin’s Concordance Correlation Coefficient, CCC ≥ 0.82) between simulated and observed values of all colour traits for PBA Jumbo2 and strong agreement (CCC ≥ 0.81) for PBA Hallmark in brightness (CIE L*) and redness (CIE a*), but not in yellowness (CIE b*). At 15 °C, both cultivars exhibited moderate to weak agreement between simulated and observed values of all colour traits (CCC ≤ 0.47), as very little change was recorded in the observed values over the 360 days of storage. Bulk storage system validation for PBA Hallmark showed moderate performance (CCC ≥ 0.46) between simulated and observed values of all colour traits. Modelling to simulate changes in seed coat colour traits of lentils over time will equip growers and traders to make informed managerial decisions when storing lentils for long periods.

Advancing the optimization of urban–rural ecosystem service supply-demand mismatches and trade-offs

ContextIntensified human activities have disrupted landscape patterns, causing a reduction in the supply of ecosystem services (ESs) and an increase in demand, especially in urban agglomerations. This supply-demand imbalance will eventually lead to unsustainable landscapes and needs to be optimized.ObjectiveBased on ES supply-demand mismatch and trade-off relationships across urban–rural landscapes, this study explored which ESs need to be optimized and identified priority restoration regions of ESs that require optimization to promote landscape sustainability in Beijing-Tianjin-Hebei urban agglomeration.MethodsA methodological framework for ES supply-demand optimization in urban–rural landscapes was developed. urban–rural landscapes were identified using Iso cluster classification tool. ES supply was quantified using biophysical models and empirical formulas, and demand was quantified through consumption and expectations. Restoration Opportunities Optimization Tool was then adopted to identify priority regions.ResultsFrom 2000 to 2020, most of ES supply were lowest in urban areas and highest in rural areas, while demand exhibited the opposite. Although supply was increasing, it did not match demand. ES deficits were dominant in urban areas; both deficits and trade-offs were dominant in urban–rural fringe; and trade-offs were dominant in rural areas. There were 13,175 km2 of priority regions distributed in urban–rural landscapes, and their spatial heterogeneity was influenced by ES deficits and trade-offs.ConclusionDifferences in ESs supply-demand relationships affected the necessity of optimizing ESs zoning in urban–rural landscapes. Assigning weights reasonably according to trade-off curves to determine priority regions could facilitate both efficient use of resources and sustainable ES management for urban–rural regions.

Estimating blue mussel (Mytilus edulis) connectivity and settlement capacity in mid-latitude fjord regions.

The mussel industry faces challenges such as low and inconsistent levels of larvae settlement and poor-quality spat, leading to variable production. However, mussel farming remains a vital sustainable and environmentally responsible method for producing protein, fostering ecological responsibility in the aquaculture sector. We investigate the population connectivity and larval dispersion of blue mussels (Mytilus edulis) in Scottish waters, as a case study, using a multidisciplinary approach that combined genetic data and particle modelling. This research allows us to develop a thorough understanding of blue mussel population dynamics in mid-latitude fjord regions, to infer gene-flow patterns, and to estimate population divergence. Our findings reveal a primary south-to-north particle transport direction and the presence of five genetic clusters. We discover a significant and continuous genetic material exchange among populations within the study area, with our biophysical model's outcomes aligning with our genetic observations. Additionally, our model reveals a robust connection between the southwest coast and the rest of the west coast. This study will guide the preservation of mussel farming regions, ensuring sustainable populations that contribute to marine ecosystem health and resilience.

Predicting the canopy conductance to water vapor of grapevines using a biophysical model in a hot and arid climate.

Canopy conductance is a crucial factor in modelling plant transpiration and is highly responsive to water stress. The objective of this study is to develop a straightforward method for estimating canopy conductance (gc) in grapevines. To predict gc, this study combines stomatal conductance to water vapor (gsw) measurements from grapevine leaves, scaled to represent the canopy size by the leaf area index (LAI), with atmospheric variables, such as net solar radiation (Rn) and air vapor pressure deficit (VPD). The developed model was then validated by comparing its predictions with gc values calculated using the inverse of the Penman Monteith equation. The proposed model demonstrates its effectiveness in estimating the gc, with the highest root-mean-squared-error (RMSE=1.45x10-4 m.s-1) being lower than the minimum gc measured in the field (gc obs=0.0005 m.s-1). The results of this study reveal the significant influence of both VPD and gsw on grapevine canopy conductance.

Abstract WP311: Novel Computational Framework for Modelling the Effects of Temperature on Survival Time in Ischemic Stroke

Introduction: The detrimental influence of cerebral hyperthermia is well known, with notably deleterious effects in the ischemic brain, where even mild increases (&lt;1°C) may rapidly potentiate ischemic injury. Experimental models of ischemic hyperthermia are undermined by the lack of pragmatic means for temporally- and spatially-resolved, direct cerebral thermometry. Computational models of cerebrovascular ischemia are well-suited to such exploration, and the use of digital phantoms for sophisticated biophysical modeling has been facilitated by emergent hardware, software, and computational solutions. We present a realistic computational environment for the simulation of thermal disturbance in cerebrovascular ischemia, permitting multiscale demonstration of hemodynamic, metabolic, and thermal aberrations in ischemic brain Methods: A multi-tissue biophysical numerical model of the human head and brain was extended to incorporate the dynamics of nutritive flow, metabolic need, tissue heating, and cytotoxicity. By restricting perfusion in a region of brain using a rudimentary representation of vascularization, we could quantify tissue heating arising from impaired radiative cooling, and time-dependent cell death. The procedure was iterated with an expected proportionality between temperature and metabolic rate Results: Fig 1 demonstrates simulated hypoperfusion affecting one hemisphere with resultant cerebral hyperthermia arising from impaired countercurrent heat exchange. Incorporating the effects of hyperthermia upon metabolic demand, the downward influence of heating on survivability is apparent, with ~30-minute acceleration to cytotoxicity. Conclusion: We present a combined model for tissue physiology and survival time following ischemic stroke using a multi-tissue model of whole-head bio-thermodynamics to examine effects of critical temperature disturbance on predicted tissue survival time.