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Results for “biophysics”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2026

Stochastic Biophysics of Cellular Radiosensitivity: From Molecular Noise and Repair Kinetics to Evolutionary Demographics

Radiation-induced DNA double-strand breaks (DSBs) drive cellular mortality, mutagenesis, and evolutionary bottlenecks. Classical phenomenological models such as the Linear-Quadratic (LQ) framework predict macroscopic survival but obscure the single-cell stochasticity that governs rare outcomes, including tumor recurrence and radioresistant persistence. We develop a stochastic differential equation framework describing DSB induction and repair as a Feller square-root process. Exact closed-form expressions for the foci mean and variance enable efficient likelihood-based inference of repair kinetics and effective molecular noise from single-cell $\gamma$-H2AX data without repeated Monte Carlo simulation. Coupling these kinetics to a cumulative damage hazard through the Feynman-Kac formalism links microscopic damage dynamics to macroscopic survival and provides a mechanistic mapping to the classical LQ form. Sensitivity analysis further shows that physical damage induction acts additively, whereas survival depends nonlinearly on repair rate, damage hazard, and stochastic noise. Increased noise enhances population survival by broadening the distribution of cumulative damage, consistent with Jensen's inequality and a bet-hedging-like survival advantage for cells experiencing transiently low damage loads. This framework therefore connects stochastic single-cell biophysics with population-level radiosensitivity and provides a tractable basis for studying how cellular heterogeneity shapes survival under radiation stress.

biophysics↗

bioRxiv2026

The biophysical interaction network that couples zona occludens-1 to membrane proteins at tight junctions

Tight junctions are dynamic membrane-bound protein assemblies that maintain barrier function for tissues as cells divide, grow, and move through development and throughout life. Maintenance of tissue barriers requires spatially separated intracellular and extracellular signaling pathways to be linked through novel and dynamic protein interaction networks. Tight junction intracellular signaling is governed by zona occludens-1 (ZO-1), a Membrane-Associated GUanylate Kinase (MAGUK) family protein that recruits and assembles transmembrane proteins and scaffolds them to the actin cytoskeleton through its own oligomerization, localization to membranes, and phase separation. Here we report comprehensive quantitative studies that nearly completely maps the intracellular interaction network between ZO-1 and tight junction transmembrane proteins that elucidates the biophysical principles governing their binding and a mechanism for how stimuli transmit through membranes. We establish where, how, and with whom each protein interacts, and identify how membrane proteins enable ZO-1 and greater tight junction function through specific interactions or by phase separation. Our findings reveal the intracellular interactome that triggers and regulates tight junction assembly, the active and direct roles each membrane protein plays in these processes, and provide a mechanism for how intra/extracellular signaling is coupled to transmit mechanochemical signals between cells via tight junctions. These insights provide a prospective blueprint for how other MAGUK/membrane protein interaction networks form and function across biology.

biophysics↗

bioRxiv2014

Scaling properties of evolutionary paths in a biophysical model of protein adaptation

The enormous size and complexity of genotypic sequence space frequently requires consideration of coarse-grained sequences in empirical models. We develop scaling relations to quantify the effect of this coarse-graining on properties of fitness landscapes and evolutionary paths. We first consider evolution on a simple Mount Fuji fitness landscape, focusing on how the length and predictability of evolutionary paths scale with the coarse-grained sequence length and number of alleles. We obtain simple scaling relations for both the weak- and strong-selection limits, with a non-trivial crossover regime at intermediate selection strengths. We apply these results to evolution on a biophysical fitness landscape designed to describe how proteins evolve new binding interactions while maintaining their folding stability. We combine numerical calculations for coarse-grained protein sequences with the scaling relations to obtain quantitative properties of the model for realistic binding interfaces and a full amino acid alphabet.

Evolutionary Biology↗

bioRxiv2016

The basis of sharp spike onset in standard biophysical models

In most vertebrate neurons, spikes initiate in the axonal initial segment. When recorded in the soma, they have a surprisingly sharp onset, as if sodium (Na) channels opened abruptly. On the basis of multicompartmental models, this phenomenon has been previously attributed to the progressive sharpening of spikes as they backpropagate from the initiation site to the soma. Here we show in the same models that the biophysical basis of the phenomenon is not backpropagation but critical resistive coupling between the soma and the initial segment. Accordingly, spike initiation results from the interplay between Na current and axial resistive current flowing from axonal initiation site to soma, rather than between local Na and potassium transmembrane currents at the initiation site. The sharp onset of somatic spikes is therefore not an artifact of recording spikes at the incorrect location, but rather the signature that spike initiation does not follow the standard account.

Neuroscience↗

bioRxiv2016

Biophysically motivated regulatory network inference: progress and prospects

Via a confluence of genomic technology and computational developments the possibility of network inference methods that automatically learn large comprehensive models of cellular regulation is closer than ever. This perspective will focus on enumerating the elements of computational strategies that, when coupled to appropriate experimental designs, can lead to accurate large-scale models of chromatin-state and transcriptional regulatory structure and dynamics. We highlight four research questions that require further investigation in order to make progress in network inference: using overall constraints on network structure like sparsity, use of informative priors and data integration to constrain individual model parameters, estimation of latent regulatory factor activity under varying cell conditions, and new methods for learning and modeling regulatory factor interactions. We conclude that methods combining advances in these four categories of required effort with new genomic technologies will result in biophysically motivated dynamic genome-wide regulatory network models for several of the best studied organisms and cell types.

Bioinformatics↗

bioRxiv2016

Tuning the course of evolution on the biophysical fitness landscape of an RNA virus

Predicting viral evolution remains a major challenge with profound implications for public health. Viral evolutionary pathways are determined by the fitness landscape, which maps viral genotype to fitness. However, a quantitative description of the landscape and the evolutionary forces on it remain elusive. Here, we apply a biophysical fitness model based on capsid folding stability and antibody binding affinity to predict the evolutionary pathway of norovirus escaping a neutralizing antibody. The model is validated by experimental evolution in bulk culture and in a drop-based microfluidics device, the \"Evolution Chip\", which propagates millions of independent viral sub-populations. We demonstrate that along the axis of binding affinity, selection for escape variants and drift due to random mutations have the same direction. However, along folding stability, selection and drift are opposing forces whose balance is tuned by viral population size. Our results demonstrate that predictable epistatic tradeoffs shape viral evolution.

evolutionary biology↗

bioRxiv2017

Understanding inhibitor resistance in Mps1 kinase through novel biophysical assays and structures

Monopolar spindle 1 (Mps1/TTK) is a protein kinase essential in mitotic checkpoint signalling, preventing anaphase until all chromosomes are properly attached to spindle microtubules. Mps1 has emerged as a potential target for cancer therapy, and a variety of compounds have been developed to inhibit its kinase activity. Mutations in the catalytic domain of Mps1 that give rise to inhibitor resistance, but retain catalytic activity and do not display cross-resistance to other Mps1 inhibitors, have been described. Here we characterize the interactions of two such mutants, Mps1 C604Y and C604W, which raise resistance to two closely related compounds, NMS-P715 and its derivative Cpd-5, but not to the well-characterised Mps1 inhibitor, reversine. We show that estimates of the IC50 (employing a novel specific and efficient assay that utilizes a fluorescently labelled substrate) and of the binding affinity (KD) indicate that in both mutants, Cpd-5 should be better tolerated than the closely related NMS-P715. To gain further insight, we determined the crystal structure of the Mps1 kinase mutants bound to Cpd-5 and NMS-P715, and compare the binding modes of Cpd-5, NMS-P715 and reversine. The difference in steric hindrance between Tyr/Trp604 and the trifluoromethoxy moiety of NMS-P715, the methoxy moiety of Cpd-5, and complete absence of such a group in reversine, account for differences we observe in vitro. Our analysis enforces the notion that inhibitors targeting Mps1 drug-resistant mutations can emerge as a feasible intervention strategy based on existing scaffolds, if the clinical need arises.\n\nSummary statementThe inhibition of specific Mps1 kinase inhibitors towards the wild-type protein and inhibitor-resistant mutants is explained by a novel specific activity assay, biophysical characterisation, and X-ray structures.\n\nAbbreviations

biochemistry↗

bioRxiv2017

Beyond The Linear Genome: Comprehensive Determination Of The Endogenous Circular Elements In C. elegans And Human Genomes Via An Unbiased Genomic-Biophysical Method

Investigations aimed at defining the 3-D configuration of eukaryotic chromosomes have consistently encountered an endogenous population of chromosome-derived circular genomic DNA, referred to as extrachromosomal circular DNA (eccDNA). While the production, distribution, and activities of eccDNAs remain understudied, eccDNA formation from specific regions of the linear genome has profound consequences on the regulatory and coding capabilities for these regions. High-throughput sequencing has only recently made extensive genomic mapping of eccDNA sequences possible and had yet to be applied using a rigorous approach that distinguishes ascertainment bias from true enrichment. Here, we define eccDNA distribution, utilizing a set of unbiased topology-dependent approaches for enrichment and characterization. We use parallel biophysical, enzymatic, and informatic approaches to obtain a comprehensive profiling of eccDNA in C. elegans and in three human cell types, where eccDNAs were previously uncharacterized. We also provide quantitative analysis of the eccDNA loci at both unique and repetitive regions. Our studies converge on and support a consistent picture in which endogenous genomic DNA circles are present in normal physiological DNA metabolism, and in which the circles come from both coding and noncoding genomic regions. Prominent among the coding regions generating DNA circles are several genes known to produce a diversity of protein isoforms, with mucin proteins and titin as specific examples.

genomics↗

bioRxiv2017

Organoid And Tissue Patterning Through Phase Separation: Use Of A Vertex Model To Relate Dynamics Of Patterning To Underlying Biophysical Parameters

AO_SCPLOWBSTRACTC_SCPLOWExactly a century ago, DArcy Thompson set an agenda for understanding tissue development in terms of underlying biophysical, mathematically-tractable mechanisms. One such mechanism, discovered by Steinberg in the 1960s, is adhesion-mediated sorting of cell mixtures into homotypic groups. Interest in this phase separation mechanism has recently surged, partly because of its use to create synthetic biological patterning mechanisms and partly because it has been found to drive events critical to the formation of organoids from stem cells, making the process relevant to biotechnology as well as to basic development. Here, we construct quantitative model of patterning by phase separation, informed by laboratory data, and use it to explore the relationship between degree of adhesive difference and speed, type and extent of resultant patterning. Our results can be used three ways; to predict the outcome of mix-ing cells with known properties, to estimate the properties required to make some designed organoid system, or to estimate underlying cellular properties from observed behaviour.

developmental biology↗

bioRxiv2017

Producing Hfq/Sm Proteins and sRNAs for Structural and Biophysical Studies of Ribonucleoprotein Assembly

Hfq is a bacterial RNA-binding protein that plays key roles in the post-transcriptional regulation of gene expression. Like other Sm proteins, Hfq assembles into toroidal discs that bind RNAs with varying affinities and degrees of sequence specificity. By simultaneously binding to a regulatory small RNA (sRNA) and an mRNA target, Hfq hexamers facilitate productive RNA[···]RNA interactions; the generic nature of this chaperone-like functionality makes Hfq a hub in many sRNA-based regulatory networks. That Hfq is crucial in diverse cellular pathways--including stress response, quorum sensing and biofilm formation-- has motivated genetic and RNAomic studies of its function and physiology (in vivo), as well as biochemical and structural analyses of Hfq[···]RNA interactions (in vitro). Indeed, crystallographic and bio-physical studies first established Hfq as a member of the phylogenetically-conserved Sm superfamily. Crystallography and other biophysical methodologies enable the RNA-binding properties of Hfq to be elucidated in atomic detail, but such approaches have stringent sample requirements, viz.: reconstituting and characterizing an Hfq*RNA complex requires ample quantities of well-behaved (sufficient purity, homogeneity) specimens of Hfq and RNA (sRNA, mRNA fragments, short oligoribonucleotides, or even single nucleotides). The production of such materials is covered in this Chapter, with a particular focus on recombinant Hfq proteins for crystallization experiments.\n\nAbbreviations\n\nJournal formatMethods in Molecular Biology (Springer Protocols series); this volume is entitled \"Bacterial Regulatory RNA: Methods and Protocols\"; an author guide is linked at http://www.springer.com/series/7651

biochemistry↗

bioRxiv2017

Inferring the selection window in antimicrobial resistance using deep mutational scanning data and biophysics-based fitness models

Mutant-selection window (MSW) hypothesis in antimicrobial resistance implies a range for antimicrobial concentration that promotes selection of single-step resistant mutants. Since the inception and experimental verification, MSW has been at the forefront of strategies to minimize development of antimicrobial resistance (AR). Setting the upper and lower limits of MSW requires an understanding of the dependence of selection coefficient of arising mutations to antimicrobial concentration. In this work, we employed a biophysics-based and experimentally calibrated fitness model to estimate MSW in the case of Ampicillin and Cefotaxime resistance in E.coli TEM-1 beta lactamase. In line with experimental observations, we show that selection is active at very low levels of antimicrobials. Furthermore, we elucidate the dependence of MSW to catalytic efficiency of mutants, fraction of mutants in the population and discuss the role of population genetic parameters such as population size and mutation rate. Altogether, our analysis and formalism provide a predictive model of MSW with direct implications in the design of dosage strategies.

evolutionary biology↗

bioRxiv2020

Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics

Multicompartment models have long been used to study the biophysical mechanisms underlying neural information processing. However, it has been challenging to infer the parameters of such models from data. Here, we build on recent advances in Bayesian simulation-based inference to estimate the parameters of detailed models of retinal neurons whose anatomical structure was based on electron microscopy data. We demonstrate how parameters of a cone, an OFF- and an ON-cone bipolar cell model can be inferred from standard two-photon glutamate imaging with simple light stimuli. The inference method starts with a prior distribution informed by literature knowledge and yields a posterior distribution over parameters highlighting parameters consistent with the data. This posterior allows determining how well parameters are constrained by the data and to what extent changes in one parameter can be compensated for by changes in another. To demonstrate the potential of such data-driven mechanistic neuron models, we created a simulation environment for external electrical stimulation of the retina as used in retinal neuroprosthetic devices. We used the framework to optimize the stimulus waveform to selectively target OFF- and ON-cone bipolar cells, a current major problem of retinal neuroprothetics. Taken together, this study demonstrates how a data-driven Bayesian simulation-based inference approach can be used to estimate parameters of complex mechanistic models with high-throughput imaging data.

neuroscience↗

bioRxiv2020

A Minimal Biophysical Model of Neocortical Pyramidal Cells: Implications for Frontal Cortex Microcircuitry and Field Potential Generation

Ca2+ spikes initiated in the apical dendrites of layer-5 pyramidal cells (PC) underlie nonlinear dynamic changes in the gain of cellular response, which is critical for top-down cognitive control. Detailed models with several compartments and dozens of ionic channels have been proposed to account for this Ca2+ spike-dependent gain with its associated critical frequency. However, current models do not account for all known Ca2+-dependent features. Previous attempts to include more features have required increasing complexity, limiting their interpretability and utility for studying large population dynamics. We present a minimal 2-compartment biophysical model, overcoming these limitations. In our model, a basal-dendritic/somatic compartment included typical Na+ and K+ conductances, while an apical-dendritic/trunk compartment included persistent Na+, hyperpolarization-activated cation (Ih), slow inactivation K+, muscarinic K+, and Ca2+ L-type. The model replicated the Ca2+ spike morphology and its critical frequency plus three other defining features of layer-5 PC synaptic integration: linear frequency-current relationships, backpropagation-activated Ca2+ spike firing, and a shift in the critical frequency by blocking Ih. Simulating 1,000 synchronized layer-5 PCs, we reproduced the current source density patterns evoked by Ca2+-spikes both with and without Ih current. Thus, a 2-compartment model with five non-classic ionic currents in the apical-dendrites reproduces all features of these neurons. We discuss the utility of this minimal model to study the microcircuitry of agranular areas of the frontal lobe involved in cognitive control and responsible for event-related potentials such as the error-related negativity. Significance StatementA tractable model of layer-5 pyramidal cells replicates all known features crucial for distal synaptic integration in these neurons. This minimal model enables new multi-scale investigations of microcircuit functions with associated current flows measured by intracranial local field potentials. It thus establishes a foundation for the future computational evaluation of scalp electroencephalogram signatures imprinted by Ca2+ spikes in pyramidal cells, a phenomenon underlying many brain cognitive processes.

neuroscience↗

bioRxiv2020

Learning precise spatiotemporal sequences via biophysically realistic circuits with modular structure

The ability to express and learn temporal sequences is an essential part of learning and memory. Learned temporal sequences are expressed in multiple brain regions and as such there may be common design in the circuits that mediate it. This work proposes a substrate for such representations, via a biophysically realistic network model that can robustly learn and recall discrete sequences of variable order and duration. The model consists of a network of spiking leaky-integrate-and-fire model neurons placed in a modular architecture designed to resemble cortical microcolumns. Learning is performed via a learning rule with "eligibility traces", which hold a history of synaptic activity before being converted into changes in synaptic strength upon neuromodulator activation. Before training, the network responds to incoming stimuli, and contains no memory of any particular sequence. After training, presentation of only the first element in that sequence is sufficient for the network to recall an entire learned representation of the sequence. An extended version of the model also demonstrates the ability to successfully learn and recall non-Markovian sequences. This model provides a possible framework for biologically realistic sequence learning and memory, and is in agreement with recent experimental results, which have shown sequence dependent plasticity in sensory cortex.

neuroscience↗

bioRxiv2020

Biophysical modeling of the SARS-CoV-2 viral cycle reveals ideal antiviral targets

Effective therapies for COVID-19 are urgently needed. Presently there are more than 800 COVID-19 clinical trials globally, many with drug combinations, resulting in an empirical process with an enormous number of possible combinations. To identify the most promising potential therapies, we developed a biophysical model for the SARS-CoV-2 viral cycle and performed a sensitivity analysis for individual model parameters and all possible pairwise parameter changes (162 = 256 possibilities). We found that model-predicted virion production is fairly insensitive to changes in most viral entry, assembly, and release parameters, but highly sensitive to some viral transcription and translation parameters. Furthermore, we found a cooperative benefit to pairwise targeting of transcription and translation, predicting that combined targeting of these processes will be especially effective in inhibiting viral production.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2020

Structural and biophysical correlation of anti-NANP antibodies with in vivo protection against P. falciparum

The most advanced P. falciparum circumsporozoite protein (PfCSP)-based malaria vaccine, RTS,S/AS01 (RTS,S), confers partial protection but with antibody titers that wane relatively rapidly, highlighting the need to elicit more potent and durable antibody responses. Here, we elucidate crystal structures, binding affinities and kinetics, and in vivo protection of eight anti-NANP antibodies (Abs) derived from an RTS,S phase 2a trial and encoded by three different heavy-chain germline genes. The structures reinforce the importance of homotypic Fab-Fab interactions in protective Abs and the overwhelmingly dominant preference for a germline-encoded aromatic residue for recognition of the NANP motif. A number of biophysical properties were analyzed and antibody affinity correlated best with protection in an in vivo mouse model, with the more potent antibodies also recognizing epitopes with repeating secondary structural motifs of type I {beta}- and Asn pseudo 310 turns. Such insights can be incorporated into design of more effective immunogens as well as antibodies for passive immunization.

microbiology↗

bioRxiv2020

Biophysical characterization of the ETV6 PNT domain polymerization interfaces

ETV6 is an ETS family transcriptional repressor that self-associates by its PNT domain to facilitate cooperative DNA binding. Chromosomal translocations frequently generate constitutively active oncoproteins with the ETV6 PNT domain fused to the kinase domain of one of many protein tyrosine kinases. Although an attractive target for therapeutic intervention, the propensity of the ETV6 PNT domain to polymerize via the tight head-to-tail association of two relatively flat interfaces makes it challenging to identify suitable small molecule inhibitors of this protein-protein interaction. Herein we provide a comprehensive biophysical characterization of the ETV6 PNT domain interaction interfaces to aid future drug discovery efforts and help define the mechanisms by which its self-association mediates transcriptional repression. Using NMR spectroscopy, X-ray crystallography, and molecular dynamics simulations, we demonstrate that ETV6 PNT domain variants with monomerizing mutations adopt very stable helical bundle folds that do not change in conformation upon self-association. Amide hydrogen exchange and surface plasmon resonance-monitored alanine scanning mutagenesis studies identified hot spot regions within the self-association interfaces. These regions include both central hydrophobic residues and flanking salt-bridging residues. Collectively, these studies indicate that small molecules targeted to these hydrophobic or charged regions within the relatively rigid interfaces could potentially serve as orthosteric inhibitors of ETV6 PNT domain polymerization.

biochemistry↗

bioRxiv2020

Live imaging and biophysical modeling support a button-based mechanism of somatic homolog pairing in Drosophila

The spatial configuration of the eukaryotic genome is organized and dynamic, providing the structural basis for regulated gene expression in living cells. In Drosophila melanogaster, 3D genome organization is characterized by somatic homolog pairing, where homologous chromosomes are intimately paired from end to end; however, the process by which homologs identify one another and pair has remained mysterious. A recent model proposed that specifically interacting "buttons" encoded along the lengths of homologous chromosomes drive somatic homolog pairing. Here, we turned this hypothesis into a precise biophysical model to demonstrate that a button-based mechanism can lead to chromosome-wide pairing. We tested our model and constrained its free parameters using live-imaging measurements of chromosomal loci tagged with the MS2 and PP7 nascent RNA labeling systems. Our analysis showed strong agreement between model predictions and experiments in the separation dynamics of tagged homologous loci as they transition from unpaired to paired states, and in the percentage of nuclei that become paired as embryonic development proceeds. In sum, as a result of this dialogue between theory and experiment, our data strongly support a button-based mechanism of somatic homolog pairing in Drosophila and provide a theoretical framework for revealing the molecular identity and regulation of buttons.

developmental biology↗