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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

A biophysical model of synaptic tagging-and-capture based on actin dynamics

According to the synaptic tagging-and-capture hypothesis, long-term synaptic plasticity requires postsynaptic sites to establish a synaptic tag, enabling them to capture plasticity-related products synthesized elsewhere in the neuron. Although electrophysiological studies have provided evidence for the existence of synaptic tags, it remains largely unresolved which biophysical processes or synaptic molecules or properties implement them. In this study, we examine the hypothesis that a mismatch between dendritic spine volume and the size of the postsynaptic density (PSD) inheres the essential characteristics of a synaptic tag. To test this hypothesis, we developed a computational model that integrates established principles of calcium-dependent synaptic plasticity with the complex biochemical dynamics of actin, which is a key structural protein determining dendritic spine geometry. Using this model, we demonstrate the plausibility of our hypothesis by showing that the model can reproduce and explain a broad range of experimental findings across diverse synaptic plasticity protocols at the level of individual synapses as well as heterosynaptic plasticity protocols involving two synapses. Furthermore, the model predicts that the repeated induction of plasticity within a one-hour time window results in a nonlinear accumulation of synaptic changes, reminiscent of the spacing effect observed in psychological studies of learning and memory. These results offer a concise mechanistic framework for understanding critical synaptic processes and suggest how temporal disparities in structural and biochemical dynamics can form a memory trace that could act as a synaptic tag.

neuroscience↗

bioRxiv2025

Mechanotransduction and Biophysical Regulation of Fibroblast Plasticity Under Prolonged Shear Stress

Complex interactions between mechanical stimuli and biochemical signaling shape the evolving fibrotic milieu. Fibroblasts contribute to this process, transitioning into myofibroblasts on stiffer substrates, and under low shear in 3D environments. The time-dependent effects of prolonged physiological shear stress on fibroblasts however remain poorly understood. We demonstrate that sustained shear stress (1.2 Pa) over 12 hours induces a temporal cascade of signaling and biophysical changes, driving fibroblast trans-differentiation into myofibroblasts. Cells adopt a spindle-shaped morphology under shear, actively remodeling through actin and -SMA to form elongated stress fibers. Myofibroblasts exhibit increased vinculin and zyxin expression in mature adhesions that remain elevated post-shear. Traction stresses progressively increased over the shear duration and persisted for 12 hours after the removal of the stimulus, indicating mechanical memory. Post-shear, deadhesion strengths increased significantly, accompanied by shifts in gene expressions governing extracellular matrix homeostasis. Prolonged shear enhanced MSD, directionality, and migration speed, all crucial for effective tissue repair. Finally, we demonstrate temporal changes in the biochemical gene expressions mediating the shear-induced remodeling of the extracellular matrix, cytoskeleton, and focal adhesions during the myofibroblast transition. These findings provide insights into the mechanobiological factors driving fibroblast-to-myofibroblast transitions which may inform the development of mechano-therapeutic strategies.

bioengineering↗

bioRxiv2025

Biophysical characterization of Eag chaperones suggests the mechanism of effector transmembrane domain release

The type VI secretion system (T6SS) is a dynamic protein nanomachine found in Gram- negative bacteria that secretes toxic effectors into prey-cells. For secretion, effectors require chaperones or adaptors for proper loading onto the T6SS. Effector associated genes (Eags) are a family of T6SS chaperones that stabilize N-terminal transmembrane domains (TMDs) found in thousands of effectors. Eags are essential for secretion and inhibit effector TMDs from prematurely adopting a membrane-penetrative conformation. However, the mechanism of TMD release from its cognate Eag chaperone is unknown. Here, we take a biochemical and biophysical approach to probe the mechanism of TMD binding and dissociation from Eag chaperones. Using steady-state fluorescence, stopped-flow measurements, and bacterial competition assays, we compare the thermodynamics, kinetics, and in vivo chaperone function of wild-type and point-variant Eag-TMD complexes. Additionally, we solve an X-ray crystal structure of an Eag-TMD point-variant complex that captures an intermediate state of TMD release. Our data reveals the molecular features and specific residue contacts necessary for TMD binding and demonstrates the Eag conformational change required to initiate rapid release of the TMD. Overall, our work details the stability of Eag-TMD complexes and the energetic pathway for the dissociation of effector TMDs from their Eag chaperones.

biochemistry↗

bioRxiv2025

Predicting specificity of TCR-pMHC interactions using machine learning and biophysical models

Understanding the mechanism of T-cell activation and T-cell receptor (TCR) discrimination of MHC-presented epitope peptides (pMHCs) remains an open problem. Machine learning (ML)-based prediction of TCR specificity has gained considerable recent attention. However, the capacity of current models to generalize to peptides unseen during training is currently unknown. Here, we use a proprietary cancer-patient data set that profiles TCR binding to novel regions of peptide space to show that peptide generalization remains an unsolved problem. Specifically, we show that while ML methods have demonstrable utility in predicting TCR specificity for known peptides, they fail to generalize to novel peptides. We also show that physics-based methods utilizing classical energy functions outperform ML methods when predicting TCR binding to novel peptides but underperform them on known peptides. In light of these observations, we develop a new ML model that leverages general knowledge acquired by protein foundation models to achieve better or comparable performance than either ML or biophysical methods on both in- and out-of-distribution TCR-pMHC specificity prediction. We furthermore analyze model performance as a function of distance of TCR sequence specificity between the training and test sets to quantitatively characterize the generalization potential of any given TCR-pMHC model. Our analysis sheds light on the status of modeling TCR-pMHC interactions and suggests new paths forward for continued method development and data acquisition.

immunology↗

bioRxiv2025

Optimizing Biophysical Large-Scale Brain Circuit Models With Deep Neural Networks

Biophysical modeling provides mechanistic insights into brain function, spanning single-neuron dynamics to large-scale circuit models. These models are governed by biologically meaningful parameters, many of which can be experimentally measured. Some parameters are unknown, and optimizing them improves fit to experimental data, enhancing biological plausibility. However, existing methods require repeated, computationally expensive numerical integration of differential equations, limiting scalability to population-level datasets. Here, we introduce DELSSOME (DEep Learning for Surrogate Statistics Optimization in MEan field modeling), a framework that bypasses numerical integration by directly predicting whether parameter sets produce realistic brain dynamics. Across three large-scale circuit models, DELSSOME achieves a 1500-8000x speedup over numerical integration in predicting model realism. When embedded within an evolutionary optimization strategy, DELSSOME enables 50-100x faster parameter estimation without sacrificing agreement with numerical integration. Because of computational constraints, most studies simulate large-scale circuit models only at the group level. DELSSOME enables efficient individual-level optimization of the feedback inhibition control model. By collating 12,005 individuals across 14 datasets, we derive - for the first time - normative trajectories of cortical E/I ratio across the lifespan, revealing new insights into sex differences and network-specific patterns. This acceleration enables population-scale mechanistic modeling and unlocks new opportunities for understanding brain function.

neuroscience↗

bioRxiv2025

Biophysical mechanisms of default mode network function and dysfunction

The default mode network (DMN) plays a fundamental role in internally focused cognition, and its disruption is implicated in numerous brain disorders. While neuroimaging has revealed DMN suppression by salient stimuli, the cellular mechanisms orchestrating this process remain unknown. Using whole-brain computational modeling informed by neuronal biophysics and retrograde tracer-derived directional mouse brain connectomics, we demonstrate that stimulation of the insula node of the salience network suppresses DMN activity, whereas cingulate cortex stimulation produces antagonistic effects, enhancing retrosplenial cortex activity. Prelimbic cortex stimulation showed intermediate patterns, partially replicating insula-mediated suppression while failing to suppress cingulate regions, suggesting its role as a functional bridge between networks. Systematic brain-wide analysis confirmed the insulas unique pattern of simulated DMN suppression. Comprehensive parameter space exploration demonstrated that DMN emergence as a functionally segregated network is robust across wide ranges of excitatory-inhibitory balance regimes and cholinergic modulation. However, outside these boundaries, DMN integrity breaks down through three distinct failure modes: loss of responsiveness, reversal of suppression to enhancement, and network fragmentation. The retrosplenial cortex emerged as a particularly vulnerable regulatory hub whose excitatory-inhibitory disruption reversed normal suppression patterns across the DMN, while prelimbic cortex demonstrated remarkable robustness. Brain-wide analysis also identified a functionally segregated frontal network displaying antagonistic dynamics with the DMN. Our findings provide mechanistic insights into DMN robustness and vulnerability, establishing a framework that links cellular excitatory-inhibitory balance to large-scale network dynamics. This model could explain how region-specific disruptions can produce the heterogeneous patterns of DMN dysfunction observed across brain disorders. Significance StatementTo respond to important external events, the brain must suppress internal thought processes implicating the default mode network. This suppression fails in psychiatric conditions, that also involve imbalances between excitatory and inhibitory neurons. However, the connection between cellular imbalances and default-mode-network dysfunction has remained unclear. We used brain-wide computer simulations incorporating neuronal properties to understand how imbalance at the cellular scale disrupts network function. Our simulations reveal the precise excitatory-inhibitory balance needed for normal suppression. Additionally, we identified distinct failure modes and discovered that certain brain hubs are more vulnerable than others to disruption. Our findings reveal how cellular alterations scale up to cause network-level dysfunction, potentially guiding treatments targeting specific brain regions in individual patients.

neuroscience↗

bioRxiv2025

Biophysical Modeling Uncovers Transcription Factor and Nucleosome Binding on Single DNA Molecules

Gene regulation in eukaryotes emerges from a dynamic interplay between transcription factors (TFs), nucleosomes, and RNA Polymerase II (Pol II), whose competitive and cooperative binding shapes DNA accessibility and transcriptional output. Single-molecule footprinting (SMF) and long-read chromatin accessibility assays such as Fiber-seq now capture these interactions at nucleotide resolution on individual DNA molecules. However, existing computational tools remain insufficient to decode complex binding events from sparse methylation data. Here, we introduce HiddenFoot, a probabilistic modeling framework based on statistical mechanics that quantitatively infers TF, nucleosome, and Pol II occupancy profiles on single DNA molecules by systematically evaluating all thermodynamically plausible binding configurations. Applying HiddenFoot to SMF and Fiber-seq data from mouse, Drosophila, and human cells, we recovered known TF footprints, precisely resolved Pol II pausing, and identified extensive heterogeneity in nucleosome positioning driven by TF binding. HiddenFoot further distinguishes direct TF-TF cooperativity from nucleosome-mediated co-dependency by estimating pairwise interaction energies and comparing to null models under equilibrium. By integrating biophysical modeling with high-resolution single-molecule data, HiddenFoot offers a general, interpretable framework for dissecting regulatory logic in native chromatin with base-pair precision. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=149 SRC="FIGDIR/small/653852v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@46f3b5org.highwire.dtl.DTLVardef@2a2cbdorg.highwire.dtl.DTLVardef@df42f2org.highwire.dtl.DTLVardef@1a4205d_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

TREM2 Activation by First-in-Class Direct Small Molecule Agonists: DEL Screening, Optimization, Biophysical Validation, and Functional Characterization

Triggering receptor expressed on myeloid cells 2 (TREM2) is a key regulator of microglial function, and its loss-of-function variants are linked to Alzheimers disease (AD) and neurodegenerative disorders. While TREM2 activation is a promising therapeutic strategy, no small molecule agonists acting via direct TREM2 binding have been reported to date. Here, we describe the discovery of first-in-class, direct small molecule TREM2 agonists identified through DNA-encoded library (DEL) screening. The DEL hit (4a) demonstrated TREM2 binding affinity, as validated by three biophysical screening platforms (TRIC, MST, and SPR), induced Syk phosphorylation, and enhanced microglial phagocytosis. Preliminary optimization yielded 4i, which maintained TREM2 engagement with improved selectivity over TREM1 and no cytotoxicity. Molecular dynamics simulations revealed that 4a stabilizes a transient binding pocket on TREM2, suggesting a novel mechanism for receptor activation. These findings provide the first proof-of-concept for direct pharmacological TREM2 agonism, offering a foundation for developing therapeutics against AD and related disorders. Table of Contents graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/655617v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@14a3d24org.highwire.dtl.DTLVardef@f423deorg.highwire.dtl.DTLVardef@5ee108org.highwire.dtl.DTLVardef@107e6df_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Changes in biophysical characteristics of YghA from E. coli due to variation in pH

YghA from E. coli has been reported to be involved in conferring tolerance against furan aldehyde inhibitors which are commonly generated in thermo-acidic pretreatment of lignocellulosic biomass. In this study we report the biophysical characterization of YghA from E. coli as a function of variation in pH. Fluorescence intensity of YghA increased by 2.5-fold in acidic pH as compared to circumneutral pH. In presence of the hydrophilic 8-anilinonaphthalene-1-sulfonic acid (ANS) dye, a 68 - 79.4-fold increase in fluorescence intensity at acidic pH was observed as compared to circumneutral pH, while at basic pH the increase was only 1 to 2-fold. Secondary structure analysis by circular dichroism signal at 222 and 208 nm suggests that the secondary structure of YghA is primarily composed of alpha-helix at pH 7 and the secondary structure is abruptly lost at pH 3 and lower. In agreement with these observations, MD simulations predicted greater structural variations at low pH when compared to neutral or high pH. Interface energy calculations using computational docking protocols suggested that YghA forms relatively more stable complex with NADH. The in silico pulling assay results also show that NADH is more preferred compared to NADPH. Molecular dynamics simulations also indicate that YghA is structurally unstable at acidic pH with significant variations in the root mean square deviation values of the tetramer backbone. Residues GLU 84 at pH 7 together with PRO 24 and LEU 236 at pH 1 were identified as flexible residues and are promising target for future mutagenesis studies targeted towards improving structural stability of YghA.

biochemistry↗

bioRxiv2025

Intrinsic mechanisms contributing to the biophysical signature of mouse gamma motoneurons

Precise motor control relies on continuous sensory feedback from muscles, a process in which gamma motoneurons play a key role. These specialized spinal neurons innervate intrafusal muscle fibres, modulating their sensitivity to stretch and maintaining proprioceptive signalling during movement. Gamma motoneurons are characterized by a distinct biophysical profile, including low recruitment thresholds and high firing rates that enable rapid activation of intrafusal fibres at contraction onset. Despite their importance, the intrinsic mechanisms that underlie these properties remain poorly understood. In this study, we analysed published and unpublished data to identify a population of low-threshold, high-gain motoneurons with features consistent with gamma motoneurons, emerging during the third postnatal week in mice. Their low recruitment threshold was linked to lower membrane capacitance, higher input resistance, a more hyperpolarized activation of persistent inward currents (PICs), and a narrower axon initial segment. In contrast, higher firing rates were associated not with PIC amplitude but with shorter action potential durations and smaller medium afterhyperpolarizations. Notably, 92% of putative gamma motoneurons exhibited a sodium pump-mediated ultra-slow afterhyperpolarization (usAHP), which was absent in slow alpha motoneurons. This difference could not be attributed to h-current activity or expression of the alpha 3 subunit of the sodium-potassium ATPase. These findings reveal key intrinsic properties that support the unique excitability of gamma motoneurons, offering new insight into their contribution to motor control. This work provides a foundation for future studies into their development, regulation, and involvement in neuromuscular disorders. Key Points[bullet] A distinct cluster of motoneurons with low recruitment current and high firing gain, characteristic of gamma motoneurons, emerges in the third week of postnatal development. [bullet]Gamma motoneurons have a low recruitment current due to lower capacitance, higher input resistance, and a more hyperpolarized activation voltage for persistent inward currents. [bullet]Their high firing rates are not driven by differences in persistent inward current amplitude but are instead attributed to shorter duration action potentials and smaller amplitude medium afterhyperpolarizations. [bullet]A narrower axon initial segment in gamma motoneurons may contribute to their increased excitability compared to alpha motoneurons. [bullet]Gamma motoneurons present with a higher prevalence of ultra slow afterhyperpolarization than slow alpha motoneurons that cannot be accounted for by differences in h-current or expression of alpha 3 subunits of the sodium potassium ATPase pump.

neuroscience↗

bioRxiv2025

Impacts of changing ocean circulation, temperature, and food supply on larval recruitment of purple sea urchins in Southern California: A biophysical modeling study

Changes in ocean conditions among years and across decades can alter populations in marine ecosystems. This study evaluates the impact of changing ocean conditions on larval recruitment of purple sea urchins in the Southern California Bight using 3D biophysical modeling, surface chlorophyll, and recruitment data. The influence of circulation, temperature, and food supply on larval recruitment is quantified using five modeled variables, larval dispersal distance, larval source location, larval food supply, and temperature exposure for larvae and adults, which are derived for 18 years and five larval recruitment sites. Sensitivity testing of the variables to different plankton larval durations (PLDs), larval behaviors, and nearshore retention is performed. All variables are found to be relatively insensitive to changes in PLD greater than 26 days. Larval dispersal distance and source location, representing changes in circulation, are found to be more sensitive to larval behavior and nearshore retention than larval food supply and temperature exposure for larvae and adults. All variables are statistically compared to recruitment field data. Temperature exposure for adults during the fall reproductive season is found to be a strong driver of larval recruitment while temperature exposure for larvae during the spring recruitment season is not. Food supply is not found to be a driver of larval recruitment. Circulation is found to be a driver of larval recruitment if larvae have behavior that reduces their dispersal distance, allowing them to come from source sites near to the recruitment sites. Overall, we hypothesize that larval behavior which reduces dispersal improves recruitment and that the timing of recruitment and reproduction can predict the impact of temperature on recruitment.

ecology↗

bioRxiv2025

Distant Site Mutations in Clinical TEM Beta-Lactamase Variants Enhance Non-Covalent Binding to Ceftazidime: Insights from Spectroscopic and Biophysical Investigations.

{beta}-lactamases retain the central armamentarium against the {beta}-lactams, resulting in surge of antibiotic resistance, primarily due to the hydrolysis of the amide bond of the four-membered ring. This study aimed to investigate the binding interactions of ceftazidime (CAZ) to TEM {beta}- lactamase variants with distant site mutations isolated from clinical setting to explore the cause of their selection and dissemination due to indiscriminate use of {beta}-lactams. Absorbance and fluorescence spectroscopy along with biophysical experimentation indicated facilitated binding of CAZ to the active site of the mutants than the wild type. The CAZ-TEM {beta}-lactamase mutant interactions were predominantly hydrophobic compared to H-bonding and van der Waals forces in the CAZ-wild type complex. Additionally structural alteration to justify more rigid binding of CAZ to the mutants in contrast to the wild type enzyme was established in silico. Therefore, acquisition of distant site mutations with respect to the active site of the {beta}-lactamase variants that rendered conformational flexibility to accommodate CAZ was evidenced. This study provided an insight to the bioactive interaction of CAZ with TEM {beta}-lactamase variants that probably facilitated their selection from clinical settings in response to rampant usage of {beta}-lactams.

microbiology↗

bioRxiv2025

Shaker it OFF: Biophysical Characterization of an Inactivating Potassium Conductance Mediating Object Segmentation in a Collision-Detecting Neuron

Homologous ion channels are expressed in sensory neurons across species where they shape responses to varied sensory stimuli, on a wide range of timescales. In grasshoppers, visual detection of looming stimuli which simulate an approaching predator involves the sub-cellular expression pattern of a newly characterized Shaker channel in a single identified neuron, discovered using a newly sequenced genome and single cell RNA sequencing. This channel shapes selectivity for approaching threats and was characterized with electrophysiological recordings, RNA interference, pharmacology and biophysical compartmental simulations. Our results explain how a slowly activating and inactivating potassium conductance common in neuronal dendrites contributes to visual object segmentation and implements a complex neural computation within a single neuron.

neuroscience↗

bioRxiv2025

An ion channel omnimodel for standardized biophysical neuron modelling

Biophysical neuron modeling is an indispensable tool in neuroscience research, with the combination of diverse ion channel kinetics and morphologies being used to explain various single-neuron properties and responses. Despite this, there is no standard way of formulating ion channel models, making it challenging to relate models to one another and experimental data. Here, we revive the idea of a standard model for ion channels based on the Hodgkin-Huxley formulation, and apply it to a recently curated database of ion channel models. We demonstrate that this standard formulation, which we refer to as an omnimodel, accurately fits the majority of voltage-gated models in the database (over 3,000 models). It produces similar, if not identical, responses to voltage-clamp protocols in simulations where the ion channel omnimodels were used. Importantly, the standard formalism enables easy comparison of models based on parameter settings. It can also be used to make new observations about the space of ion channel kinetics found in neurons. Furthermore, it facilitates the inference of ion channel parameters from the responses to standard protocols. We provide an interactive platform to compare and select channel models, and encourage the community to use this standard formulation whenever possible, to facilitate understanding and comparison among models.

neuroscience↗

bioRxiv2025

A Biophysical Model of Phagocytic Cup Dynamics: The Effect of Membrane Tension

Phagocytosis is a fundamental cellular process by which cells engulf external particles, controlled by receptor-ligand binding and actin-driven membrane dynamics. While a number of mathematical models have been developed to describe this process, they often overlook membrane tension, a key physical parameter known to influence membrane deformation and cytoskeletal behaviour. To address this gap, we present an enhanced mathematical model of receptor motion during phagocytosis that explicitly incorporates the role of membrane tension. Further, we introduce a signalling component that is coupled to receptor dynamics via the membrane tension. We find that including tension results in fundamentally different engulfment behaviour, which is slower than that predicted by models without tension. In particular, unlike in the previous version of this model, we show that tension can lead to stalled engulfment, an experimentally-observed phenomenon known as frustrated phagocytosis. We also find that signalling is able to modify engulfment behaviour, especially at later stages, and is able to alter cup growth to become linear in time without the need for receptor drift as introduced in previous models. These findings offer new insights into the role of membrane tension and biophysical regulation in phagocytosis, with implications for immune function, cell motility and targeted drug delivery.

cell biology↗

bioRxiv2017

Biophysical models reveal the relative importance of transporter proteins and impermeant anions in chloride homeostasis

Fast synaptic inhibition in the nervous system depends on the transmembrane flux of Cl- ions based on the neuronal Cl- driving force. Established theories regarding the determinants of Cl- driving force have recently been questioned. Here we present biophysical models of Cl- homeostasis using the pump-leak model. Using numerical and novel analytic solutions, we demonstrate that the Na+/K+-ATPase, ion conductances, impermeant anions, electrodiffusion, water fluxes and cation-chloride cotransporters (CCCs) play roles in setting the Cl- driving force. Our models, together with experimental validation, show that while impermeant anions can contribute to setting [Cl-]i in neurons, they have a negligible effect on the driving force for Cl- locally and cell-wide. In contrast, we demonstrate that CCCs are well-suited for modulating Cl- driving force and hence inhibitory signalling in neurons. Our findings reconcile recent experimental findings and provide a framework for understanding the interplay of different chloride regulatory processes in neurons.

neuroscience↗

bioRxiv2017

A biophysical dynamic module for the polarisation of auxin efflux carriers PIN-FORMED (PIN)

Intracellular polarisation of auxin efflux carriers is crucial for understanding how auxin gradients form in plants. The polarisation dynamics of auxin efflux carriers PIN-FORMED (PIN) depends on both biomechanical forces as well as chemical, molecular and genetic factors. Biomechanical forces have shown to affect the localisation of PIN transporters to the plasma membrane. We proposed a biophysical module of PIN polarisation that integrates biomechanical, molecular, and cellular processes as well as their non-linear interactions. The module was implemented as a discrete Boolean model and then approximated to a continuous dynamic system, in order to explore the relative contribution of the factors mediating PIN polarisation at the scale of single cell. Our models recovered qualitative behaviours that have been experimentally observed and enabled us to postulate that, in the context of PIN polarisation, the effects of the mechanical forces can predominate over the activity of molecular factors such as the GTPase ROP6 and the CRIB-motif CRIB-motif RIC1.

plant biology↗

bioRxiv2018

Biophysically Realistic Neuron Models for Simulation of Cortical Stimulation

1.ObjectiveWe implemented computational models of human and rat cortical neurons for simulating the neural response to cortical stimulation with electromagnetic fields.\n\nApproachWe adapted model neurons from the library of Blue Brain models to reflect biophysical and geometric properties of both adult rat and human cortical neurons and coupled the model neurons to exogenous electric fields (E-fields). The models included 3D reconstructed axonal and dendritic arbors, experimentally-validated electrophysiological behaviors, and multiple, morphological variants within cell types. Using these models, we characterized the single-cell responses to intracortical microstimulation (ICMS) and uniform E-field with dc as well as pulsed currents.\n\nMain resultsThe strength-duration and current-distance characteristics of the model neurons to ICMS agreed with published experimental results, as did the subthreshold polarization of cell bodies and axon terminals by uniform dc E-fields. For all forms of stimulation, the lowest threshold elements were terminals of the axon collaterals, and the dependence of threshold and polarization on spatial and temporal stimulation parameters was strongly affected by morphological features of the axonal arbor, including myelination, diameter, and branching.\n\nSignificanceThese results provide key insights into the mechanisms of cortical stimulation. The presented models can be used to study various cortical stimulation modalities while incorporating detailed spatial and temporal features of the applied E-field.

bioengineering↗