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bioRxiv2025

Epistasis in Allosteric Proteins: Can Biophysical Models Provide a Better Framework for Prediction and Understanding?

The prediction of epistasis, or the interaction between mutations, is a complex challenge impacting protein science, healthcare, and biotechnology. For allosteric proteins, the prediction of epistatic effects is further complicated by the intricate networks of conformational states and binding interactions inherent to their function. Here, we explore these issues by systematically comparing biophysical and phenomenological models to analyze mutational effects and epistasis for the lac repressor protein, LacI. Using an extensive dataset consisting of dose-response measurements for 164 LacI variants, we find that while the phenomenological Hill model provides slightly better predictive accuracy, the biophysical model fits the data more parsimoniously, with significantly less epistasis in its parameters. Our results highlight the importance of the multi-state, multi-dimensional nature of allosteric function and the potential benefits of using biophysical models for the analysis of mutational effects and epistasis.

biophysics↗

bioRxiv2025

Ceramide Connects TNF-α Signaling to Organelle Biophysical Remodeling

Ceramides are essential bioactive lipids involved in cell physiology and stress responses, yet the mechanisms linking their biophysical effects on membranes to downstream cellular outcomes remain incompletely understood. Here, we demonstrate that endogenous ceramide generation in response to TNF--induced stress results in the rapid remodeling of the plasma membrane, triggering a cascade of cellular events. Ceramide accumulation increases membrane order and drives the formation of internalizing vesicles enriched in ceramides, which traffic through the endolysosomal system. These vesicles act as carriers that transmit membrane remodeling signals to intracellular compartments, altering their biophysical properties and function. In parallel, ceramide production reprograms lipid droplet metabolism, decreasing polarity and modifying gene expression related to lipid storage. Our findings provide direct evidence that ceramides function as biophysical integrators of the TNF- stress response, coupling membrane order, vesicle trafficking, and lipid metabolic adaptation. This work reveals how localized lipid remodeling at the plasma membrane propagates intracellularly to support cellular adaptation and homeostasis under inflammatory stress.

biophysics↗

bioRxiv2018

Beyond stability constraints: a biophysical model of enzyme evolution with selection for stability and activity

Proteins trace trajectories in sequence space as their amino acids become substituted by other amino acids. The number of substitutions per unit time, the rate of evolution, varies among sites because of biophysical constraints. Several properties that characterize sites local environments have been proposed as biophysical determinants of site-specific evolutionary rates. Thus, rate increases with increasing solvent exposure, increasing flexibility, and decreasing local packing density. For enzymes, rate increases also with increasing distance from the proteins active residues, presumably due to functional constraints. The dependence of rates on solvent accessibility, packing density, and flexibility has been mechanistically explained in terms of selection for stability. However, as I show here, a stability-based model fails to reproduce the observed rate-distance dependence, overestimating rates close to the active residues and underestimating rates of distant sites. Here, I pose a new biophysical model of enzyme evolution with selection for stability and activity (MSA) and compare it with a stability-based counterpart (MS). Testing these models on a structurally and functionally diverse dataset of monomeric enzymes, I found that MSA fits observed rates better than MS for most proteins. While both models reproduce the observed dependence of rates on solvent accessibility, packing, and flexibility, MSA fits these dependencies somewhat better. Importantly, while MS fails to reproduce the dependence of rates on distance from the active residues, MSA accounts for the rate-distance dependence quantitatively. Thus, the variation of evolutionary rate among enzyme sites is mechanistically underpinned by natural selection for both stability and activity.

biophysics↗

bioRxiv2019

An Open Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries

Advances in imaging methods such as electron microscopy, tomography, and other modalities are enabling high-resolution reconstructions of cellular and organelle geometries. Such advances pave the way for using these geometries for biophysical and mathematical modeling once these data can be represented as a geometric mesh, which, when carefully conditioned, enables the discretization and solution of partial differential equations. In this study, we outline the steps for a naive user to approach GAMer 2, a mesh generation code written in C++ designed to convert structural datasets to realistic geometric meshes, while preserving the underlying shapes. We present two example cases, 1) mesh generation at the subcellular scale as informed by electron tomography, and 2) meshing a protein with structure from x-ray crystallography. We further demonstrate that the meshes generated by GAMer are suitable for use with numerical methods. Together, this collection of libraries and tools simplifies the process of constructing realistic geometric meshes from structural biology data. SIGNIFICANCEAs biophysical structure determination methods improve, the rate of new structural data is increasing. New methods that allow the interpretation, analysis, and reuse of such structural information will thus take on commensurate importance. In particular, geometric meshes, such as those commonly used in graphics and mathematics, can enable a myriad of mathematical analysis. In this work, we describe GAMer 2, a mesh generation library designed for biological datasets. Using GAMer 2 and associated tools PyGAMer and BlendGAMer, biologists can robustly generate computer and algorithm friendly geometric mesh representations informed by structural biology data. We expect that GAMer 2 will be a valuable tool to bring realistic geometries to biophysical models.

biophysics↗

bioRxiv2026

The biophysical properties of the bacterial nucleoid are dynamic, heterogeneous, and responsive to perturbations of cellular processes

Biophysical properties play central roles in cellular function by controlling the diffusion and spatial organization of biomolecules. Because bacteria lack a nuclear membrane, biophysical measurements are often averaged over the entire cell. However, the nucleoid environment is distinct from that of the surrounding cytoplasm, and averaging also ignores local characteristics within the nucleoid. Here, we developed a microrheology framework to quantitatively characterize the bacterial nucleoid and investigate the interplay of its physical properties with cellular processes. We combined single-particle tracking of a genetically encoded protein probe and three-dimensional (3D) Brownian dynamics simulations to separate the nucleoid from the cytoplasm and specifically measure nucleoid accessibility and viscosity. We found that the nucleoid viscosity is 2.5-fold higher than the cytoplasmic viscosity, and that both viscosity and accessibility change systematically across growth phases and the cell cycle. Inhibiting transcription or translation produces opposite changes in nucleoid viscosity in exponential versus stationary phase cells, indicating that the regulation of nucleoid viscosity is sensitive to the underlying biomolecular composition, crowding, and spatial organization. Using Hi-C assays, we further show that changes in nucleoid viscosity may occur without detectable alterations in genome organization, which suggests that nucleoid mechanics provide an independent regulatory mechanism. Spatially, viscosity differences are more pronounced between the nucleoid core and periphery than between genomic locations, and the periphery-core contrast correlates with the coupling of transcription, translation, and membrane insertion. Together, these results indicate that the bacterial nucleoid is a dynamic, heterogeneous viscoelastic environment in which actively regulated biophysical properties may help coordinate multiple cellular processes and provide a physical layer of control that complements canonical biochemical regulation.

biophysics↗

bioRxiv2026

Exposure to the antimicrobial peptides LL-37 and ATRA-1 induces a lipidome response in Staphylococcus aureus that alters membrane biophysical properties

Staphylococcus aureus (S. aureus) is an opportunistic pathogen that is a global health concern for its ability to cause a wide spectrum of clinical infections. Due to the emergence of resistance to commonly used antibiotics, there has been interest in exploring the use of antimicrobial peptides to treat S. aureus infections. However, changes in the lipid composition of the lipid bilayer membrane can alter the activity of peptides, and S. aureus is able to induce variations in lipid composition in response to environmental stress. Here, we explore how the main lipid components in S. aureus are altered when exposed to LL-37, a human cathelicidin involved in primary immune response, and ATRA-1, a short antimicrobial peptide derived from the snake Naja atra venom. A lipidomic study is conducted through HPLC-MS-MS (LC-ESI-MS/MS) to quantify phosphatidylglycerol, cardiolipin, lysyl-phosphatidylglycerol, monogalacto- and digalacto-diacylglycerol, and carotenoids. In addition, menaquinones, responsible for electron transport during oxidative phosphorylation, were also quantified. Biophysical properties such as membrane electric surface potential and lipid packing were assessed. We find that lipid adaptation is specific to the type of antimicrobial peptide, where ATRA-1 mainly induces changes in the electric surface potential through variations in Lysyl-PG, while exposure to LL-37 changes carotenoid levels, inducing an increase in membrane rigidity as measured by FTIR. In addition, both peptides induce a reduction in menaquinone and DGDG levels. These findings highlight the role of membrane lipid remodeling as a peptide-specific response mechanism in S. aureus, with implications for the development of AMP-based therapies. HighlightsO_LIStaphylococcus aureus responds through shifts in lipid composition and membrane biophysical properties to exposure to the antimicrobial peptides LL-37 and ATRA-1. C_LIO_LIBoth LL-37 and ATRA-1 lead to shifts in the glycolipids MGDG and DGDG; two lipids involved in regulating negative membrane curvature stress and responsible for shifting resistance to antimicrobial peptide activity in Staphylococcus aureus. C_LIO_LILL-37 treatment leads to an overall reduction in carotenoid content in Staphylococcus aureus, including the carotenoid end-product staphyloxanthin and the precursor 4,4-diaponeurosporenoic acid. Both lipids regulate membrane biophysical properties and protect Staphylococcus aureus from oxidative stress. C_LIO_LIBoth LL-37 and ATRA-1 lead to a reduction in menaquinone levels, which are involved in the electron transport chain during oxidative phosphorylation. Reduction in these menaquinones have been associated to the formation of small colony variants that are often observed in chronic Staphylococcus aureus infections. C_LI

biophysics↗

bioRxiv2026

Processing, analysing and modelling kinetic data in the era of high-throughput single-molecule biophysics

Biomolecular reactions are often composed of multiple stochastic, reversible and branched transition paths over intermediates, leading to rich dynamics. Single-molecule biophysics has revolutionized our view of biology by revealing the heterogeneity in realized paths and pointing to the importance of rare events. The recent development of high-throughput single-molecule biophysics techniques now allow to quantitatively study this heterogeneity and characterize even the rarest kinetic events. Processing, analyzing and modelling high-throughput single-molecule data has been the focus of several reports, but are often difficult to implement for non-experts. Here, we provide a guide to extract the most from transitions in single-molecule biophysics data using a first-passage time framework and maximum likelihood estimation. We specifically focused on parameter sweeps in systems with one or two characteristic timescales, and show how they can be analyzed in terms of a minimal kinetic model and its dependence on enzyme/substrate concentration, force and temperature. We introduce a general framework to perform data-driven modelling on single- and two-state models and illustrate it with concrete examples. We also provide programs with graphical user interfaces to perform such analysis on raw data, in the hope that it will empower experimental single-molecule biophysicists to extract the most out of their data.

biophysics↗

bioRxiv2020

Effective Biophysical Modeling of Cell Free Transcription and Translation Processes

Transcription and translation are at the heart of metabolism and signal transduction. In this study, we developed an effective biophysical modeling approach to simulate transcription and translation processes. We tested this approach by simulating the dynamics of two cell free synthetic circuits. First, we considered a simple circuit in which sigma factor 70 induced the expression of green fluorescent protein. This relatively simple case was then followed by a more complex negative feedback circuit in which two control genes were coupled to the expression of a third reporter gene, green fluorescent protein. While many of the model parameters were estimated from previous biophysical literature, the remaining unknown model parameters for each circuit were estimated from messenger RNA (mRNA) and protein measurements using multi-objective optimization. In particular, either the literature parameter estimates were used directly in the model simulations, or characteristic literature values were used to establish feasible ranges for the multiobjective parameter search. Next, global sensitivity analysis was used to determine the influence of individual model parameters on the expression dynamics. Taken together, the effective biophysical modeling approach captured the expression dynamics, including the transcription dynamics, for the two synthetic cell free circuits. While we considered only two circuits here, this approach could potentially be extended to simulate other genetic circuits in both cell free and whole cell biomolecular applications. The model code, parameters, and analysis scripts are available for download under an MIT software license from the Varnerlab GitHub repository.

systems biology↗

bioRxiv2021

A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex

A fundamental challenge for the theoretical study of neuronal networks is to make the link between complex biophysical models based directly on experimental data, to progressively simpler mathematical models that allow the derivation of general operating principles. We present a strategy that successively maps a relatively detailed biophysical population model, comprising conductance-based Hodgkin-Huxley type neuron models with connectivity rules derived from anatomical data, to various representations with fewer parameters, finishing with a firing rate network model that permits analysis. We apply this methodology to primary visual cortex of higher mammals, focusing on the functional property of stimulus orientation selectivity of receptive fields of individual neurons. The mapping produces compact expressions for the parameters of the abstract model that clearly identify the impact of specific electrophysiological and anatomical parameters on the analytical results, in particular as manifested by specific functional signatures of visual cortex, including input-output sharpening, conductance invariance, virtual rotation and the tilt after effect. Importantly, qualitative differences between model behaviours point out consequences of various simplifications. The strategy may be applied to other neuronal systems with appropriate modifications. Author summaryA hierarchy of theoretical approaches to study a neuronal network depends on a tradeoff between biological fidelity and mathematical tractibility. Biophysically-detailed models consider cellular mechanisms and anatomically defined synaptic circuits, but are often too complex to reveal insights into fundamental principles. In contrast, increasingly abstract reduced models facilitate analytical insights. To better ground the latter to the underlying biology, we describe a systematic procedure to move across the model hierarchy that allows understanding how changes in biological parameters - physiological, pathophysiological, or because of new data - impact the behaviour of the network. We apply this approach to mammalian primary visual cortex, and examine how the different models in the hierarchy reproduce functional signatures of this area, in particular the tuning of neurons to the orientation of a visual stimulus. Our work provides a navigation of the complex parameter space of neural network models faithful to biology, as well as highlighting how simplifications made for mathematical convenience can fundamentally change their behaviour.

neuroscience↗

bioRxiv2021

Probing molecular specificity with deep sequencing and biophysically interpretable machine learning

Quantifying sequence-specific protein-ligand interactions is critical for understanding and exploiting numerous cellular processes, including gene regulation and signal transduction. Next-generation sequencing (NGS) based assays are increasingly being used to profile these interactions with high-throughput. However, these assays do not provide the biophysical parameters that have long been used to uncover the quantitative rules underlying sequence recognition. We developed a highly flexible machine learning framework, called ProBound, to define sequence recognition in terms of biophysical parameters based on NGS data. ProBound quantifies transcription factor (TF) behavior with models that accurately predict binding affinity over a range exceeding that of previous resources, captures the impact of DNA modifications and conformational flexibility of multi-TF complexes, and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with a new assay called Kd-seq, it determines the absolute affinity of protein-ligand interactions. It can also profile the kinetics of kinase-substrate interactions. By constructing a biophysically robust foundation for profiling sequence recognition, ProBound opens up new avenues for decoding biological networks and rationally engineering protein-ligand interactions.

genomics↗

bioRxiv2021

Biophysical network models of phase-synchronization in MEG resting-state

Magnetoencephalography (MEG) is used extensively to study functional connectivity (FC) networks of phase-synchronization, but the relationship of these networks to their biophysical substrates is poorly understood. Biophysical Network Models (BNMs) have been used to produce networks corresponding to MEG-derived networks of phase-synchronization, but the roles of inter-regional conduction delays, the structural connectome and dynamics of model of individual brain regions, in obtaining this correspondence remain unknown. In this study, we investigated the roles of conduction delays, the structural connectome, and dynamics of models of individual regions, in obtaining a correspondence between model-generated and MEG-derived networks between left-hemispheric regions. To do this, we compared three BNMs, respectively comprising Wilson-Cowan oscillators interacting with diffusion Magnetic Resonance Imaging (MRI)-based patterns of structural connections through zero delays, constant delays and distance-dependent delays respectively. For the BNM whose networks corresponded most closely to the MEG-derived network, we used comparisons against null models to identify specific features of each model component, e.g. the pattern of connections in the structure connectome, that contributed to the observed correspondence. The Wilson-Cowan zero delays model produced networks with a closer correspondence to the MEG-derived network than those produced by the constant delays model, and the same as those produced by the distance-dependent delays model. Hence, there is no evidence that including conduction delays improves the correspondence between model-generated and MEG-derived networks. Given this, we chose the Wilson-Cowan zero delays model for further investigation. Comparing the Wilson-Cowan zero delays model against null models revealed that both the pattern of structural connections and Wilson-Cowan oscillatory dynamics contribute to the correspondence between model-generated and MEG-derived networks. Our investigations yield insight into the roles of conduction delays, the structural connectome and dynamics of models of individual brain regions, in obtaining a correspondence between model-generated and MEG-derived networks. These findings result in a parsimonious BNM that produces networks corresponding closely to MEG-derived networks of phase-synchronization. HighlightsO_LISimple biophysical model produces close match ({rho}=0.49) between model and MEG networks C_LIO_LINo evidence for conduction delays improving match between model and MEG networks C_LIO_LIPattern of structural connections contributes to match between model and MEG networks C_LIO_LIWilson-Cowan oscillatory dynamics contribute to match between model and MEG networks C_LI

neuroscience↗

bioRxiv2021

Experimental and Biophysical Modeling of Transcription and Translation Dynamics in Bacterial- and Mammalian-based Cell-Free Expression Systems

Cell-free expression (CFE) systems have been used extensively in system and synthetic biology as a promising platform for manufacturing proteins and chemicals. Currently, the most widely used CFE system is in vitro protein transcription and translation platform. As the rapidly increased applications and uses, it is crucial to have a standard biophysical model for quantitative studies of gene circuits, which will provide a fundamental understanding of basic working mechanisms of CFE systems. Current modeling approaches mainly focus on the characterization of E. coli-based CFE systems, a computational model that can be utilized to both bacterial- and mammalianbased CFE has not been investigated. Here, we developed a simple ODE (ordinary differential equation)-based biophysical model to simulate transcription and translation dynamics for both bacterial- and mammalian-based CFE systems. The key parameters were estimated and adjusted based on experimental results. We next tested four gene circuits to characterize kinetic dynamics of transcription and translation in E. coli- and HeLa-based CFE systems. The real-time transcription and translation were monitored using Broccoli aptamer, double stranded locked nucleic acid (dsLNA) probe and fluorescent protein. We demonstrated the difference of kinetic dynamics for transcription and translation in both systems, which will provide valuable information for quantitative genomic and proteomic studies. This simple biophysical model and the experimental data for both E. coli- and HeLa-based CFE will be useful for researchers that are interested in genetic engineering and CFE bio-manufacturing.

synthetic biology↗

bioRxiv2022

Resolving the mesoscopic missing link: Biophysical modeling of EEG from cortical columns in primates

Event-related potentials (ERP) are among the most widely measured indices for studying human 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 in vivo 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 in vivo to clarify the source of a representative cognitive ERP component. To that end, LFP was measured across the cortical layers of visual area V4 in macaque monkeys performing an attention demanding task. 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 represents an important bridge from the mesoscopic activity of cortical columns to the patterns of EEG we measure at the scalp. HighlightsO_LICognitive EEG production was accurately modeled from empirically measured cortical activity in awake macaques. C_LIO_LIV4 laminar activity plausibly generates the attention-related signal indexed by the EEG. C_LIO_LIModels demonstrate the importance of biophysical geometry in cognitive EEG production. C_LI

neuroscience↗

bioRxiv2022

Morphological profiling by high-throughput single-cell biophysical fractometry

Complex and irregular cell architecture is known to statistically exhibit fractal geometry, i.e., a pattern resembles a smaller part of itself. Although fractal variations in cells are proven to be closely associated with the disease-related phenotypes that are otherwise obscured in the standard cell-based assays, fractal analysis with single-cell precision remains largely unexplored. To close this gap, here we develop an image-based approach that quantifies a multitude of single-cell biophysical fractal-related properties at subcellular resolution. Taking together with its high-throughput single-cell imaging performance (~10,000 cells/sec), this technique, termed single-cell biophysical fractometry, offers sufficient statistical power for delineating the cellular heterogeneity, in the context of classification of lung-cancer cell subtypes and tracking of cell-cycle progression. Further correlative fractal analysis shows that single-cell biophysical fractometry can enrich the standard morphological profiling depth and spearhead systematic fractal analysis of how cell morphology encodes cellular health and pathological conditions.

cell biology↗

bioRxiv2022

Bacterial capsular polysaccharides with antibiofilm activity share common biophysical and electrokinetic properties

Bacterial biofilms are surface-attached communities that are difficult to eradicate due to a high tolerance to antimicrobial agents. The use of non-biocidal surface-active compounds to prevent the initial adhesion and aggregation of bacterial pathogens is a promising alternative to antibiotic treatments and several antibiofilm compounds have been identified, including some capsular polysaccharides released by various bacteria. However, the lack of chemical and mechanistic understanding of the activity of these high-molecular-weight polymers limits their use for control of biofilm formation. Here, we screened a collection of 32 purified capsular polysaccharides and identified seven new compounds with non-biocidal activity against biofilms formed by Escherichia coli and/or Staphylococcus aureus. We analyzed the polysaccharide mobility under applied electric field conditions and showed that active and inactive polysaccharide polymers display distinct electrokinetic properties and that all active macromolecules shared high intrinsic viscosity features. Based on these characteristics, we identified two additional antibiofilm capsular polysaccharides with high density of electrostatic charges and their permeability to fluid flow. Our study therefore provides insights into key biophysical properties discriminating active from inactive polysaccharides. This characterization of a specific electrokinetic signature for polysaccharides displaying antibiofilm activity opens new perspectives to identify or engineer non-biocidal surface-active macromolecules to control biofilm formation in medical and industrial settings. Significance statementSome bacteria produce non-biocidal capsular polysaccharides that reduce the adhesion of bacterial pathogens to surfaces. Due to a lack of molecular and structural definition, the basis of their antiadhesion activity is unknown, thus hindering their prophylactic use for biofilm control. Here, we identified nine new active compounds and compared their composition, structure and biophysical properties with other inactive capsular polysaccharides. Despite the absence of specific molecular motif, we demonstrate that all active polysaccharides share common electrokinetic properties that distinguish them from inactive polymers. This characterization of the biophysical properties of antibiofilm bacterial polysaccharide provides key insights to engineer non-biocidal and bio-inspired surface-active compounds to control bacterial adhesion in medical and industrial settings.

microbiology↗

bioRxiv2023

Sodium channel subpopulations with distinct biophysical properties and subcellular localization enhance cardiac conduction

Sodium (Na+) current is responsible for the rapid depolarization of cardiac myocytes that triggers the cardiac action potential upstroke. Recent studies have illustrated the presence of multiple "pools" of Na+ channels with distinct biophysical properties and subcellular localization, including clustering of channels at the intercalated disk and along the lateral membrane. Computational studies predict that Na+ channel clusters at the intercalated disk can regulate cardiac conduction via modulation of the narrow intercellular cleft between electrically coupled myocytes. However, these studies have primarily focused on the redistribution of Na+ channels between intercalated disk and lateral membranes and not considered the distinct biophysical properties of the Na+ channel subpopulations. In this study, we simulate computational models of single cardiac cells and one-dimensional cardiac tissues to predict the functional consequence of distinct Na+ channel subpopulations. Single cell simulations predict that a subpopulation of Na+ channels with shifted steady-state activation and inactivation voltage-dependency promote an earlier action potential upstroke. In cardiac tissues that account for distinct subcellular spatial localization, simulations predict that "shifted" Na+ channels contribute to faster and more robust conduction, with regards to sensitivity to tissue structural changes (i.e., cleft width), gap junctional coupling, and rapid pacing rates. Simulations predict that the intercalated disk-localized shifted Na+ channels provide a disproportionally larger contribution to total Na+ charge, relative to lateral membrane-localized Na+ channels. Importantly, our work supports the hypothesis that Na+ channel redistribution may be a critical mechanism by which cells can rapidly respond to perturbations to support fast and robust conduction. SummarySodium channels are organized in multiple "pools" with distinct biophysical properties and subcellular localization, but the role of these subpopulations is not well-understood. Computational modeling predicts that intercalated disk-localized sodium channels with shifted steady-state activation and inactivation voltage-dependence promote faster cardiac conduction.

physiology↗

bioRxiv2023

Basolateral amygdala oscillations enable fear learning in a biophysical model

The basolateral amygdala (BLA) is a key site where fear learning takes place through synaptic plasticity. Rodent research shows prominent low theta ([~]3-6 Hz), high theta ([~]6-12 Hz), and gamma (>30 Hz) rhythms in the BLA local field potential recordings. However, it is not understood what role these rhythms play in supporting the plasticity. Here, we create a biophysically detailed model of the BLA circuit to show that several classes of interneurons (PV, SOM, and VIP) in the BLA can be critically involved in producing the rhythms; these rhythms promote the formation of a dedicated fear circuit shaped through spike-timing-dependent plasticity. Each class of interneurons is necessary for the plasticity. We find that the low theta rhythm is a biomarker of successful fear conditioning. The model makes use of interneurons commonly found in the cortex and, hence, may apply to a wide variety of associative learning situations. SignificanceOur paper accounts for the experimental evidence showing that amygdalar rhythms exist, suggests network origins for these rhythms, and points to their central role in the mechanisms of plasticity involved in associative learning. It is one of the few papers to address high-order cognition with biophysically detailed models, which are sometimes thought to be too detailed to be adequately constrained. Our paper provides a template for how to use information about brain rhythms to constrain biophysical models. It shows in detail, for the first time, how multiple interneurons help to provide time scales necessary for some kinds of spike-timing-dependent plasticity (STDP). It spells out the conditions under which such interactions between interneurons are needed for STDP and why. Finally, our work helps to provide a framework by which some of the discrepancies in the fear learning literature might be reevaluated. In particular, we discuss issues about Hebbian plasticity in fear learning; we show in the context of our model how neuromodulation might resolve some of those issues. The model addresses issues more general than that of fear learning since it is based on interactions of interneurons that are prominent in the cortex, as well as the amygdala.

neuroscience↗

bioRxiv2023

Chondroitin sulfate, dermatan sulfate, and hyaluronic acid differentially modify the biophysical properties of collagen-based hydrogels

Fibrillar collagens and glycosaminoglycans (GAGs) are structural biomolecules that are natively abundant to the extracellular matrix (ECM). Prior studies have quantified the effects of GAGs on the bulk mechanical properties of the ECM. However, there remains a lack of experimental studies on how GAGs alter other biophysical properties of the ECM, including ones that operate at the length scales of individual cells such as mass transport efficiency and matrix microstructure. Here we characterized and decoupled the effects of the GAG molecules chondroitin sulfate (CS) dermatan sulfate (DS) and hyaluronic acid (HA) on the stiffness (indentation modulus), transport (hydraulic permeability), and matrix microarchitecture (pore size and fiber radius) properties of collagen-based hydrogels. We complement these biophysical measurements of collagen hydrogels with turbidity assays to profile collagen aggregate formation. Here we show that CS, DS, and HA differentially regulate the biophysical properties of hydrogels due to their alterations to the kinetics of collagen self-assembly. In addition to providing information on how GAGs play significant roles in defining key physical properties of the ECM, this work shows new ways in which stiffness measurements, microscopy, microfluidics, and turbidity kinetics can be used complementary to reveal details of collagen self-assembly and structure.

bioengineering↗