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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 73 records · Page 4Linked to original sources

bioRxiv2018

Fast calcium transients in neuronal spines is driven by extreme statistics

Extreme statistics describe the distribution of rare events that can define the timescales of transduction within cellular microdomains. We combine biophysical modeling and analysis of live-cell calcium imaging to explain the fast calcium transient in spines. We show that in the presence of a spine apparatus (SA), which is an extension of the smooth endoplasmic reticulum (ER), calcium transients during synaptic inputs rely on rare and extreme calcium ion trajectories. Using numerical simulations, we predicted the asymmetrical distributions of Ryanodine receptors and SERCA pumps that we confirmed experimentally. When calcium ions are released in the spine head, the fastest ions arriving at the base determine the transient timescale through a calcium-induced calcium release mechanism. In general, the fastest particles arriving at a small target are likely to be a generic mechanism that determines the timescale of molecular transduction in cellular neuroscience.\n\nSignificance statementIntrigued by fast calcium transients of few milliseconds in dendritic spines, we investigated its underlying biophysical mechanism. We show here that it is generated by the diffusion of the fastest calcium ions when the spine contains a Spine Apparatus, an extension of the endoplasmic reticulum. This timescale is modulated by the initial number of released calcium ions and the asymmetric distribution of its associated calcium release associated Ryanodyne receptors, present only at the base of a spine. This novel mechanism of calcium signaling that we have unraveled here is driven by the fastest particles. To conclude, the rate of arrival of the fastest particles (ions) to a small target receptor defines the timescale of activation instead of the classical forward rate of chemical reactions introduced by von Smoluchowski in 1916. Applying this new rate theory to transduction should refine our understanding of the biophysical mechanisms underlying molecular signaling.

cell biology↗

bioRxiv2018

Linking single-cell measurements of mass, growth rate, and gene expression

We introduce a microfluidic platform that enables single-cell mass and growth rate measurements upstream of single-cell RNA-sequencing (scRNA-seq) to generate paired single-cell biophysical and transcriptional data sets. Biophysical measurements are collected with a serial suspended microchannel resonator platform (sSMR) that utilizes automated fluidic state switching to load individual cells at fixed intervals, achieving a throughput of 120 cells per hour. Each single-cell is subsequently captured downstream for linked molecular analysis using an automated collection system. From linked measurements of a murine leukemia (L1210) and pro-B cell line (FL5.12), we identify gene expression signatures that correlate significantly with cell mass and growth rate. In particular, we find that both cell lines display a cell-cycle signature that correlates with cell mass, with early and late cell-cycle signatures significantly enriched amongst genes with negative and positive correlations with mass, respectively. FL5.12 cells also show a significant correlation between single-cell growth efficiency and a G1-S transition signature, providing additional transcriptional evidence for a phenomenon previously observed through biophysical measurements alone. Importantly, the throughput and speed of our platform allows for the characterization of phenotypes in dynamic cellular systems. As a proof-of-principle, we apply our system to characterize activated murine CD8+ T cells and uncover two unique features of CD8+ T cells as they become proliferative in response to activation: i) the level of coordination between cell cycle gene expression and cell mass increases, and ii) translation-related gene expression increases and shows a correlation with single-cell growth efficiency. Overall, our approach provides a new means of characterizing the transcriptional mechanisms of normal and dysfunctional cellular mass and growth rate regulation across a range of biological contexts.

bioengineering↗

bioRxiv2018

Deletion of the alternative splice variant KChIP4a in midbrain dopamine neurons selectively enhances extinction learning

Midbrain dopamine neurons are essential for flexible control of adaptive behaviors. DA neurons that project to different target regions have unique biophysical properties, and it is thought that this diversity reflects functional specialization. This assumption implies the presence of specific genetic determinants with precise impacts on behavior. We tested this general hypothesis by homing in on one particular biophysical mechanism, Kv4 channel inactivation, using a combination of molecular, proteomic, electrophysiological, computational, and behavioral approaches. We demonstrate that KChIP4a, a singular Kv4 {beta}-subunit splice variant, prolongs hyperpolarization-rebound delays selectively in dopamine neurons projecting to the nucleus accumbens core, shifts the integration of inhibitory inputs and, in turn, selectively regulates learning from negative prediction-errors. Our results reveal a highly specialized, gene-to-behavior mechanistic chain that is only operative in a particular dopaminergic subsystem, illuminating how molecularly defined biophysical switches are employed for neuron subtype-specific information processing in the brain. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/344499v3_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@167984borg.highwire.dtl.DTLVardef@3f8fbborg.highwire.dtl.DTLVardef@f5402aorg.highwire.dtl.DTLVardef@147bb3b_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2018

Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes

The great potential of computational diffusion MRI (dMRI) relies on indirect inference of tissue microstructure and brain connections, since modelling and tractography frameworks map diffusion measurements to neuroanatomical features. This mapping however can be computationally highly expensive, particularly given the trend of increasing dataset sizes and the complexity in biophysical modelling. Limitations on computing resources can restrict data exploration and methodology development. A step forward is to take advantage of the computational power offered by recent parallel computing architectures, especially Graphics Processing Units (GPUs). GPUs are massive parallel processors that offer trillions of floating point operations per second, and have made possible the solution of computationally-intensive scientific problems that were intractable before. However, they are not inherently suited for all problems. Here, we present two different frameworks for accelerating dMRI computations using GPUs that cover the most typical dMRI applications: a framework for performing biophysical modelling and microstructure estimation, and a second framework for performing tractography and long-range connectivity estimation. The former provides a front-end and automatically generates a GPU executable file from a user-specified biophysical model, allowing accelerated non-linear model fitting in both deterministic and stochastic ways (Bayesian inference). The latter performs probabilistic tractography, it can generate whole-brain connectomes and supports new functionality for imposing anatomical constraints, such as inherent consideration of surface meshes (GIFTI files) along with volumetric images. We validate the frameworks against well-established CPU-based implementations and we show that despite the very different challenges for parallelising these problems, GPU-based designs can offer accelerations of more than two orders of magnitude in both cases.

neuroscience↗

bioRxiv2018

Weak electric fields promote resonance in neuronal spiking activity: analytical results from two-compartment cell and network models

Transcranial brain stimulation and evidence of ephaptic coupling have sparked strong interests in understanding the effects of weak electric fields on the dynamics of neuronal populations. While their influence on the subthreshold membrane voltage can be biophysically well explained using spatially extended neuron models, mechanistic analyses of neuronal spiking and network activity have remained a methodological challenge. More generally, this challenge applies to phenomena for which single-compartment (point) neuron models are oversimplified. Here we employ a pyramidal neuron model that comprises two compartments, allowing to distinguish basal-somatic from apical dendritic inputs and accounting for an extracellular field in a biophysically minimalistic way. Using an analytical approach we fit its parameters to reproduce the response properties of a canonical, spatial model neuron and dissect the stochastic spiking dynamics of single cells and large networks. We show that oscillatory weak fields effectively mimic anti-correlated inputs at the soma and dendrite and strongly modulate neuronal spiking activity in a rather narrow frequency band. This effect carries over to coupled populations of pyramidal cells and inhibitory interneurons, boosting network-induced resonance in the beta and gamma frequency bands. Our work contributes a useful theoretical framework for mechanistic analyses of population dynamics going beyond point neuron models, and provides insights on modulation effects of extracellular fields due to the morphology of pyramidal cells.\n\nAuthor SummaryThe elongated spatial structure of pyramidal neurons, which possess large apical dendrites, plays an important role for the integration of synaptic inputs and mediates sensitivity to weak extracellular electric fields. Modeling studies at the population level greatly contribute to our mechanistic understanding but face a methodological challenge because morphologically detailed neuron models are too complex for use in noisy, in-vivo like conditions and large networks in particular. Here we present an analytical approach based on a two-compartment spiking neuron model that can distinguish synaptic inputs at the apical dendrite from those at the somatic region and accounts for an extracellular field in a biophysically minimalistic way. We devised efficient methods to approximate the responses of a spatially more detailed pyramidal neuron model, and to study the spiking dynamics of single neurons and sparsely coupled large networks in the presence of fluctuating inputs. Using these methods we focused on how responses are affected by oscillatory weak fields. Our results suggest that ephaptic coupling may play a mechanistic role for oscillations of population activity and indicate the potential to entrain networks by weak electric stimulation.

neuroscience↗

bioRxiv2014

Relationship between protein thermodynamic constraints and variation of evolutionary rates among sites

Evolutionary-rate variation among sites within proteins depends on functional and biophysical properties that constrain protein evolution. It is generally accepted that proteins must be able to fold stably in order to function. However, the relationship between stability constraints and among-sites rate variation is not well understood. Here, we present a biophysical model that links the thermodynamic stability changes due to mutations at sites in proteins ({Delta}{Delta}G) to the rate at which mutations accumulate at those sites over evolutionary time. We find that such a "stability model" generally performs well, displaying correlations between predicted and empirically observed rates of up to 0.75 for some proteins. We further find that our model has comparable predictive power as does an alternative, recently proposed "stress model" that explains evolutionary-rate variation among sites in terms of the excess energy needed for mutants to adopt the correct active structure ({Delta}{Delta}G*). The two models make distinct predictions, though, and for some proteins the stability model outperforms the stress model and vice versa. We conclude that both stability and stress constrain site-specific sequence evolution in proteins.

Biophysics↗

bioRxiv2017

Quantitative hybrid modeling reveals predictable mechanical stress response of growing tumor spheroids

Model simulations indicate that the response of growing cell populations on mechanical stress follows the same functional relationship and is predictable over different cell lines and growth conditions despite the response curves look largely different. We develop a hybrid model strategy in which cells are represented by coarse-grained individual units calibrated with a high resolution cell model and parameterized measurable biophysical and cell-biological parameters. Cell cycle progression in our model is controlled by volumetric strain, the latter being derived from a bio-mechanical relation between applied pressure and cell compressibility. After parameter calibration from experiments with mouse colon carcinoma cells growing against the resistance of an elastic alginate capsule, the model adequately predicts the growth curve in i) soft and rigid capsules, ii) in different experimental conditions where the mechanical stress is generated by osmosis via a high molecular weight dextran solution, and iii) for other cell types with varying doubling times. Our model simulation results suggest that the growth response of cell population upon externally applied mechanical stress is the same, as it can be quantitatively predicted using the same growth progression function.\n\nAuthor summaryThe effect of mechanical resistance on the growth of tumor cells remains today largely unquantified. We studied data from two different experimental setups that monitor the growth of tumor cells under mechanical compression. The existing data in the first experiment examined growing CT26 cells in an elastic permeable capsule. In the second experiment, growth of tumor cells under osmotic stress of the same cell line as well as other cell lines were studied. We have developed and agent-based model with measurable biophysical and cell-biological parameters that can simulate both experiments. Cell cycle progression in our model is a Hill function of cell volumetric strain, derived from a bio-mechanical relation between applied pressure and cell compressibility. After calibration of the model parameters within the data of the first experiment, we are able predict the growth rates in the second experiment. We show that that the growth response of cell populations upon externally applied mechanical stress in the two different experiments and over different cell lines can be predicted using the same growth progression function once the growth kinetics of the cell lines in abscence of mechanical stress is known.

biophysics↗

bioRxiv2017

Structural Propensity Database Of Proteins

An overwhelming amount of experimental evidence suggests that elucidations of protein function, interactions, and pathology are incomplete without inclusion of intrinsic protein disorder and structural dynamics. Thus, to expand our understanding of intrinsic protein disorder, we have created a database of secondary structure (SS) propensities for proteins (dSPP) as a reference resource for experimental research and computational biophysics. The dSPP comprises SS propensities of 7,094 unrelated proteins, as gauged from NMR chemical shift measurements in solution and solid state. Here, we explain the concept of SS propensity and analyze dSPP entries of therapeutic relevance, -synuclein, MOAG-4, and the ZIKA NS2B-NS3 complex to show: (1) how propensity mapping generates novel structural insights into intrinsically disordered regions of pathologically relevant proteins, (2) how computational biophysics tools can benefit from propensity mapping, and (3) how the residual disorder estimation based on NMR chemical shifts compares with sequence-based disorder predictors. This work demonstrates the benefit of propensity estimation as a method that reports both on protein structure, lability, and disorder.

biophysics↗

bioRxiv2017

Thermodynamic Integration in 3n Dimensions without Biases or Alchemy for Protein Interactions

Thermodynamic integration (TI), a powerful formalism for computing the Gibbs free energy, has been implemented for many biophysical processes characterized by one-dimensional order parameters with alchemical schemes that require delicate human efforts to choose/design biasing potentials for sampling the desired biophysical events and to remove their artifactitious consequences afterwards. Theoretically, an alchemical scheme is exact but practically, it causes error amplification. Small relative errors in the interaction parameters can be amplified many times in their propagation into the computed free energy [due to subtraction of similar numbers such as (105 {+/-} 5) - (100 {+/-} 5) = 5 {+/-} 7], which would render the results significantly less accurate than the input interaction parameters. In this paper, we present an unsophisticated implementation of TI in 3n dimensions (3nD) (n=1,2,3...) without alchemy or biasing potentials. In TI3nD, the errors in the interaction parameters will not be amplified and human efforts are not required to design biasing potentials that generate unphysical consequences. Using TI3nD, we computed the standard free energies of three protein complexes: trometamol in Salmonella effector SpvD (n=1), biotin in avidin (n=2), and Colicin E9 endonuclease with cognate immunity protein Im9 (n=3) and the hydration energies of ten biologically relevant compounds (n=1 for water, acetamide, urea, glycerol, trometamol, ammonium and n=2 for erythritol, 1,3-propanediol, xylitol, biotin). The computed results all agree with available experimental data. Each of the 13 computations is accomplishable within two (for a hydration problem) to ten (for the protein-recognition problem) days on an inexpensive workstation (two Xeon E5-2665 2.4GHz CPUs and one nVidia P5000 GPU).

biophysics↗

bioRxiv2017

Preparation of H-Ras GTPase conjugated to lipid nanodiscs for NMR spectroscopy

Ras GTPase is a peripheral membrane protein central to cellular signaling of growth and proliferation. Membrane attachment is critical for a range of Ras activities, therefore, ability to make faithful in-vitro samples of a mem-brane-bound Ras for detailed biophysical studies is a highly desirable goal. In this manuscript, we are describing preparation of a large-scale sample of isotopically labeled H-Ras conjugated to lipid nanodiscs. We demonstrate that the Ras-nanodisc sample is fairly stable to allow for a range of Nuclear Magnetic Resonance (NMR) and other biophysical measurements. The need to achieve a homogeneous protein-nanodisc ratio is also emphasized.

biophysics↗

bioRxiv2017

Shaping Epigenetic Memory via Genomic Bookmarking

Reconciling the stability of epigenetic patterns with the rapid turnover of histone modifications and their adaptability to external stimuli is an outstanding challenge. Here, we propose a new biophysical mechanism that can establish and maintain robust yet plastic epigenetic domains via genomic bookmarking (GBM). We model chromatin as a recolourable polymer whose segments bear non-permanent histone marks (or colours) which can be modified by \"writer\" proteins. The three-dimensional chromatin organisation is mediated by protein bridges, or \"readers\", such as Polycomb Repressive Complexes and Transcription Factors. The coupling between readers and writers drives spreading of biochemical marks and sustains the memory of local chromatin states across replication and mitosis. In contrast, GBM-targeted perturbations destabilise the epigenetic patterns. Strikingly, we demonstrate that GBM alone can explain the full distribution of Polycomb marks in a whole Drosophila chromosome. We finally suggest that our model provides a starting point for an understanding of the biophysics of cellular differentiation and reprogramming.

biophysics↗

bioRxiv2018

Solubilization kinetics determines the pulsatory dynamics of lipid vesicles exposed to surfactant

We establish a biophysical model for the dynamics of lipid vesicles exposed to surfactants. The solubilization of the lipid membrane due to the insertion of surfactant molecules induces a reduction of membrane surface area at almost constant vesicle volume. This results in a rate-dependent increase of membrane tension and leads to the opening of a micron-sized pore. We show that solubilization kinetics due to surfactants can determine the regimes of pore dynamics: either the pores open and reseal within a second (short-lived pore), or the pore stays open up to a few minutes (long-lived pore). First, we validate our model with previously published experimental measurements of pore dynamics. Then, we investigate how the solubilization kinetics and membrane properties affect the dynamics of the pore and construct a phase diagram for short and long-lived pores. Finally, we examine the dynamics of sequential pore openings and show that cyclic short-lived pores occur at a period inversely proportional to the solubilization rate. By deriving a theoretical expression for the cycle period, we provide an analytic tool to measure the solubilization rate of lipid vesicles by surfactants. Our findings shed light on some fundamental biophysical mechanisms that allow simple cell-like structures to sustain their integrity against environmental stresses, and have the potential to aid the design of vesicle-based drug delivery systems.

biophysics↗

bioRxiv2017

Statistical methods for large ensemble of super-resolution stochastic single particle trajectories

Following recent progresses in super-resolution microscopy obtained in the last decade, massive amount of redundant single stochastic trajectories are now available for statistical analysis. Flows of trajectories of molecules or proteins are sampling the cell membrane or its interior at a very high time and space resolution. Several statistical analysis were developed to extract information contained in these data, such as the biophysical parameters of the underlying stochastic motion to reveal the cellular organization. These trajectories can further reveal hidden subcellular organization. We present here the statistical analysis of these trajectories based on the classical Langevin equation, which serves as a model of trajectories. Parametric and non-parametric estimators are constructed by discretizing the stochastic equations and they allow recovering tethering forces, diffusion tensor or membrane organization from measured trajectories, that differ from physical ones by a localization noise. Modeling, data analysis and automatic detection algorithms serve extracting novel biophysical features such as potential wells and other sub-structures, such as rings at an unprecedented spatiotem-poral resolution. It is also possible to reconstruct the surface membrane of a biological cell from the statistics of projected random trajectories.

biophysics↗

bioRxiv2017

Cell size regulation through tunable geometric localization of the bacterial actin cytoskeleton

In the rod-shaped bacterium Escherichia coli, the actin-like protein MreB localizes in a curvature-dependent manner and spatially coordinates cell-wall insertion to maintain cell shape across changing environments, although the molecular mechanism by which cell width is regulated remains unknown. Here, we demonstrate that the bitopic membrane protein RodZ regulates the biophysical properties of MreB and alters the spatial organization of E. coli cell-wall growth. The relative expression levels of MreB and RodZ changed in a manner commensurate with variations in growth rate and cell width. We carried out single-cell analyses to determine that RodZ systematically alters the curvature-based localization of MreB and cell width in a manner dependent on the concentration of RodZ. Finally, we identified MreB mutants that we predict using molecular dynamics simulations to alter the bending properties of MreB filaments at the molecular scale similar to RodZ binding, and showed that these mutants rescued rod-like shape in the absence of RodZ alone or in combination with wild-type MreB. Together, our results show that E. coli controls its shape and dimensions by differentially regulating RodZ and MreB to alter the patterning of cell-wall insertion, highlighting the rich regulatory landscape of cytoskeletal molecular biophysics.

biophysics↗

bioRxiv2017

Repertoire analysis of antibody CDR-H3 loops suggests affinity maturation does not typically result in rigidification

Antibodies can rapidly evolve in specific response to antigens. Affinity maturation drives this evolution through cycles of mutation and selection leading to enhanced antibody specificity and affinity. Elucidating the biophysical mechanisms that underlie affinity maturation is fundamental to understanding B-cell immunity. An emergent hypothesis is that affinity maturation reduces the conformational flexibility of the antibodys antigen-binding paratope to minimize entropic losses incurred upon binding. In recent years, computational and experimental approaches have tested this hypothesis on a small number of antibodies, often observing a decrease in the flexibility of the Complementarity Determining Region (CDR) loops that typically comprise the paratope and in particular the CDR-H3 loop, which contributes a plurality of antigen contacts. However, there were a few exceptions, and previous studies were limited to a small handful of cases. Here, we determined the structural flexibility of the CDR-H3 loop for thousands of recently-determined homology models of the human peripheral blood cell antibody repertoire using rigidity theory. We found no clear delineation in the flexibility of naive and antigen-experienced antibodies. To account for possible sources of error, we additionally analyzed hundreds of human and mouse antibodies in the Protein Data Bank through both rigidity theory and B-factor analysis. By both metrics, we observed only a slight decrease in the CDR-H3 loop flexibility when comparing affinity-matured antibodies to naive antibodies, and the decrease was not as drastic as previously reported. Further analysis, incorporating molecular dynamics (MD) simulations, revealed a spectrum of changes in flexibility. Our results suggest that rigidification may be just one of many biophysical mechanisms for increasing affinity.

biophysics↗

bioRxiv2018

A First-principles Approach to Large-scale Nuclear Architecture

Model approaches to nuclear architecture have traditionally ignored the biophysical consequences of ATP-fueled active processes acting on chromatin. However, transcription-coupled activity is a source of stochastic forces that are substantially larger than the Brownian forces present at physiological temperatures. Here, we describe a first-principles approach to large-scale nuclear architecture in metazoans that incorporates cell-type-specific active processes. The model predicts the statistics of positional distributions, shapes and overlaps of each chromosome. Our simulations reproduce common organising principles underlying large-scale nuclear architecture across human cell nuclei in interphase. These include the differential positioning of euchromatin and heterochromatin, the territorial organisation of chromosomes including both gene-density-based and size-based chromosome radial positioning schemes, the non-random locations of chromosome territories and the shape statistics of individual chromosomes. We propose that the biophysical consequences of the distribution of transcriptional activity across chromosomes should be central to any chromosome positioning code.

biophysics↗

bioRxiv2018

Native tau structure is disrupted by disease-associated mutations that promote aggregation

Tauopathies are neurodegenerative diseases characterized by intracellular amyloid deposits of tau protein. Missense mutations in the tau gene (MAPT) correlate with aggregation propensity and cause dominantly inherited tauopathies, but their biophysical mechanism driving amyloid formation is poorly understood. Many disease-associated mutations localize within taus repeat domain at inter-repeat interfaces proximal to amyloidogenic sequences, such as 306VQIVYK311. Using cross-linking mass spectrometry, intramolecular FRET, recombinant protein and synthetic peptide systems, in silico modeling, and cell models, we conclude that the aggregation prone 306VQIVYK311 motif forms metastable compact structures with the upstream sequence that modulates aggregation propensity. Disease-associated mutations, isomerization of a critical proline, or alternative splicing are all sufficient to destabilize this local structure and trigger spontaneous aggregation. These findings provide a biophysical framework to explain the basis of early conformational changes that may underlie genetic and sporadic tau pathogenesis.

biophysics↗

bioRxiv2018

Deep clustering of protein folding simulations

We examine the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensional representations that can be used to extract quantitative insights into the atomistic mechanisms that underlie complex biological processes. In this paper, we use a convolutional variational autoencoder (CVAE) to learn low dimensional, biophysically relevant latent features from long time-scale protein folding simulations in an unsupervised manner. We demonstrate our approach on three model protein folding systems, namely the Fs-peptide (14 s aggregate sampling), villin head piece (single trajectory of 125 s) and the mixed {beta}-{beta}- (BBA) protein (223 + 102 s sampling across two independent trajectories). In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. The CVAE model predicts nearly 89% of all contacts within the folding trajectories correctly, while being able to extract folded, unfolded and potentially misfolded states in an unsupervised manner. Further, the CVAE model can be used to learn latent features of protein folding that can be applied to other independent trajectories, making it particularly attractive for identifying intrinsic features that correspond to conformational substates that share similar structural features. Together, we show that the CVAE model can quantitatively describe complex biophysical processes such as protein folding.

biophysics↗