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arXiv2016

Using Fluorescence Recovery After Photobleaching (FRAP) to study dynamics of the Structural Maintenance of Chromosome (SMC) complex in vivo

The SMC complex, MukBEF, is important for chromosome organization and segregation in Escherichia coli. Fluorescently tagged MukBEF forms distinct spots (or 'foci') in the cell, where it is thought to carry out most of its chromosome associated activities. This chapter outlines the technique of Fluorescence Recovery After Photobleaching (FRAP) as a method to study the properties of YFP-tagged MukB in fluorescent foci. This method can provide important insight into the dynamics of MukB on DNA and be used to study its biochemical properties in vivo.

q-bio.SC↗

arXiv2016

Single molecule narrowfield microscopy of protein-DNA binding dynamics in glucose signal transduction of live yeast cells

Single-molecule narrowfield microscopy is a versatile tool to investigate a diverse range of protein dynamics in live cells and has been extensively used in bacteria. Here, we describe how these methods can be extended to larger eukaryotic, yeast cells, which contain sub-cellular compartments. We describe how to obtain single-molecule microscopy data but also how to analyse these data to track and obtain the stoichiometry of molecular complexes diffusing in the cell. We chose glucose mediated signal transduction of live yeast cells as the system to demonstrate these single-molecule techniques as transcriptional regulation is fundamentally a single molecule problem - a single repressor protein binding a single binding site in the genome can dramatically alter behaviour at the whole cell and population level.

q-bio.SC↗

arXiv2016

High resolution neural connectivity from incomplete tracing data using nonnegative spline regression

Whole-brain neural connectivity data are now available from viral tracing experiments, which reveal the connections between a source injection site and elsewhere in the brain. These hold the promise of revealing spatial patterns of connectivity throughout the mammalian brain. To achieve this goal, we seek to fit a weighted, nonnegative adjacency matrix among 100 $μ$m brain "voxels" using viral tracer data. Despite a multi-year experimental effort, injections provide incomplete coverage, and the number of voxels in our data is orders of magnitude larger than the number of injections, making the problem severely underdetermined. Furthermore, projection data are missing within the injection site because local connections there are not separable from the injection signal. We use a novel machine-learning algorithm to meet these challenges and develop a spatially explicit, voxel-scale connectivity map of the mouse visual system. Our method combines three features: a matrix completion loss for missing data, a smoothing spline penalty to regularize the problem, and (optionally) a low rank factorization. We demonstrate the consistency of our estimator using synthetic data and then apply it to newly available Allen Mouse Brain Connectivity Atlas data for the visual system. Our algorithm is significantly more predictive than current state of the art approaches which assume regions to be homogeneous. We demonstrate the efficacy of a low rank version on visual cortex data and discuss the possibility of extending this to a whole-brain connectivity matrix at the voxel scale.

q-bio.NC↗

arXiv2016

Single Molecule Data Analysis: An Introduction

We review methods of data analysis for biophysical data with a special emphasis on single molecule applications. Our review is intended for anyone, from student to established researcher. For someone just getting started, we focus on exposing the logic, strength and limitations of each method and cite, as appropriate, the relevant literature for implementation details. We review traditional frequentist and Bayesian parametric approaches to data analysis and subsequently extend our discussion to recent non-parametric and information theoretic methods.

physics.bio-ph↗

arXiv2017

Stochastic Model of Maturation and Vesicular Exchange in Cellular Organelles

The dynamical organization of membrane-bound organelles along intracellular transport pathways relies on vesicular exchange between organelles and on biochemical maturation of the organelle content by specific enzymes. The relative importance of each mechanism in controlling organelle dynamics remains controversial, in particular for transport through the Golgi apparatus. Using a stochastic model, we show that full maturation of membrane-bound compartments can be seen as the stochastic escape from a steady-state in which export is dominated by vesicular exchange. We show that full maturation can contribute a significant fraction of the total out-flux for small organelles such as endosomes and Golgi cisternae.

physics.bio-ph↗

arXiv2017

A Flexible Phase Retrieval Framework for Flux-limited Coherent X-Ray Imaging

Coherent X-ray diffraction imaging (CXDI) experiments are intrinsically limited by shot noise, a lack of prior knowledge about the sample's support, and missing measurements due to the experimental geometry. We propose a flexible, iterative phase retrieval framework that allows for accurate modeling of Gaussian or Poissonian noise statistics, modified support updates, regularization of reconstructed signals, and handling of missing data in the observations. The proposed method is efficiently solved using alternating direction method of multipliers (ADMM) and is demonstrated to consistently outperform state-of-the-art algorithms for low-photon phase retrieval from CXDI experiments, both for simulated diffraction patterns and for experimental measurements.

physics.bio-ph↗

arXiv2016

A Polymer Model with Epigenetic Recolouring Reveals a Pathway for the de novo Establishment and 3D Organisation of Chromatin Domains

One of the most important problems in development is how epigenetic domains can be first established, and then maintained, within cells. To address this question, we propose a framework which couples 3D chromatin folding dynamics, to a "recolouring" process modelling the writing of epigenetic marks. Because many intra-chromatin interactions are mediated by bridging proteins, we consider a "two-state" model with self-attractive interactions between two epigenetic marks which are alike (either active or inactive). This model displays a first-order-like transition between a swollen, epigenetically disordered, phase, and a compact, epigenetically coherent, chromatin globule. If the self-attraction strength exceeds a threshold, the chromatin dynamics becomes glassy, and the corresponding interaction network freezes. By modifying the epigenetic read-write process according to more biologically-inspired assumptions, our polymer model with recolouring recapitulates the ultrasensitive response of epigenetic switches to perturbations, and accounts for multi-domain conformations, strikingly similar to the topologically-associating-domains observed in eukaryotic chromosomes.

cond-mat.soft↗

arXiv2016

A conditional likelihood is required to estimate the selection coefficient in ancient DNA

Time-series of allele frequencies are a useful and unique set of data to determine the strength of natural selection on the background of genetic drift. Technically, the selection coefficient is estimated by means of a likelihood function built under the hypothesis that the available trajectory spans a sufficiently large portion of the fitness landscape. Especially for ancient DNA, however, often only one single such trajectories is available and the coverage of the fitness landscape is very limited. In fact, one single trajectory is more representative of a process conditioned both in the initial and in the final condition than of a process free to visit the available fitness landscape. Based on two models of population genetics, here we show how to build a likelihood function for the selection coefficient that takes the statistical peculiarity of single trajectories into account. We show that this conditional likelihood delivers a precise estimate of the selection coefficient also when allele frequencies are close to fixation whereas the unconditioned likelihood fails. Finally, we discuss the fact that the traditional, unconditioned likelihood always delivers an answer, which is often unfalsifiable and appears reasonable also when it is not correct.

q-bio.PE↗

arXiv2016

Online games: a novel approach to explore how partial information influences human random searches

Many natural processes rely on optimizing the success ratio of a search process. We use an experimental setup consisting of a simple online game in which players have to find a target hidden on a board, to investigate the how the rounds are influenced by the detection of cues. We focus on the search duration and the statistics of the trajectories traced on the board. The experimental data are explained by a family of random-walk-based models and probabilistic analytical approximations. If no initial information is given to the players, the search is optimized for cues that cover an intermediate spatial scale. In addition, initial information about the extension of the cues results, in general, in faster searches. Finally, strategies used by informed players turn into non-stationary processes in which the length of each displacement evolves to show a well-defined characteristic scale that is not found in non-informed searches.

physics.bio-ph↗

arXiv2016

Collective motion of cells crawling on a substrate: roles of cell shape and contact inhibition

Contact inhibition plays a crucial role in the motility of cells, the process of wound healing, and the formation of tumors. By mimicking the mechanical motion of calls crawling on a substrate using a pseudopod, we constructed a minimal model for migrating cells which gives rise to contact inhibition of locomotion (CIL) naturally. The model cell consists of two disks, one in the front (a pseudopod) and the other one in the back (cell body), connected by a finitely extensible spring. Despite the simplicity of the model, the cells' collective behavior is highly nontrivial, depending on the shape of cells and whether CIL is enabled or not. Cells with a small front circle (i.e. a narrow pseudopod) form immobile colonies. In contrast, cells with a large front circle (i.e. such as a lamellipodium) exhibit coherent migration without any explicit alignment mechanism being present in the model. This suggests that crawling cells often exhibit broad fronts because it helps them avoid clustering. Upon increasing the density, the cells develop density waves which propagate against the direction of cell migration and finally arrest at higher densities.

cond-mat.soft↗

arXiv2016

Structural Protein-based Flexible Whispering Gallery Mode Resonators

Nature provides a set of solutions for photonic structures that are finely tuned, organically diverse and optically efficient. Exquisite knowledge of structure-property relationships in proteins aids in the design of materials with desired properties for building devices with novel functionalities, which are difficult to achieve or previously unattainable. Recent bio-inspired photonic platforms made from proteinaceous materials lay the groundwork for many functional device applications, such as electroluminescence in peptide nucleic acids, multiphoton absorption in amyloid fibers and silk waveguides and inverse opals. Here we report whispering-gallery-mode (WGM) microresonators fabricated entirely from semi-crystalline structural proteins (i.e., squid ring teeth, SRT, from Loligo vulgaris and its recombinant) with unconventional thermo-optic response. We demonstrated waveguides, add-drop filters and flexible resonators as first examples of energy-efficient, highly flexible, biocompatible and biodegradable protein-based photonic devices. Optical switching efficiency in these devices is over thousand times greater than the values reported for Silica WGM resonators. This work opens the way for designing energy efficient functional photonic devices using structure- property relationships of proteins.

physics.bio-ph↗

arXiv2016

On the mathematical consequences of binning spike trains

We initiate a mathematical analysis of hidden effects induced by binning spike trains of neurons. Assuming that the original spike train has been generated by a discrete Markov process, we show that binning generates a stochastic process which is not Markov any more, but is instead a Variable Length Markov Chain (VLMC) with unbounded memory. We also show that the law of the binned raster is a Gibbs measure in the DLR (Dobrushin-Lanford-Ruelle) sense coined in mathematical statistical mechanics. This allows the derivation of several important consequences on statistical properties of binned spike trains. In particular, we introduce the DLR framework as a natural setting to mathematically formalize anticipation, i.e. to tell "how good" our nervous system is at making predictions. In a probabilistic sense, this corresponds to condition a process by its future and we discuss how binning may affect our conclusions on this ability. We finally comment what could be the consequences of binning in the detection of spurious phase transitions or in the detection of wrong evidences of criticality.

q-bio.NC↗

arXiv2016

Molecular Dynamics simulations and Kelvin Probe Force microscopy to study of cholesterol-induced electrostatic nanodomains in complex lipid mixtures

The molecular arrangement of lipids and proteins within biomembranes and monolayers gives rise to complex film morphologies as well as regions of distinct electrical surface potential, topographical and electrostatic nanoscale domains. To probe these nanodomains in soft matter is a challenging task both experimentally and theoretically. This work addresses the effects of cholesterol, lipid composition, lipid charge, and lipid phase on the monolayer structure and the electrical surface potential distribution. Atomic Force Microscopy (AFM) was used to resolve topographical nanodomains and Kelvin Probe Force Microscopy (KPFM) to resolve electrical surface potential of these nanodomains in lipid monolayers. Model monolayers composed of dipalmitoylphosphatidylcholine (DPPC), 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), 1,2-dioleoyl-sn-glycero-3-[phospho-rac-(3-lysyl(1-glycerol))] (DOPG), sphingomyelin, and cholesterol were studied. It is shown that cholesterol changes nanoscale domain formation, affecting both topography and electrical surface potential. The molecular basis for differences in electrical surface potential was addressed with atomistic molecular dynamics (MD). MD simulations qualitatively match the experimental results, with 100s of mV difference in electrostatic potential between liquid-disordered bilayer (Ld, less cholesterol and lower chain order) and a liquid-ordered bilayer (Lo, more cholesterol and higher chain order). Importantly, the difference in electrostatic properties between Lo and Ld phases suggests a new mechanism by which membrane composition couples to membrane function.

cond-mat.soft↗

arXiv2017

Influences of transversely-isotropic rheology and translational diffusion on the stability of active suspensions

Suspensions of self-motile, elongated particles are a topic of significant current interest, exemplifying a form of `active matter'. Examples include self-propelling bacteria, algae and sperm, and artificial swimmers. Ericksen's model of a transversely-isotropic fluid [J. L. Ericksen, Colloid Polym. Sci. 173(2):117-122 (1960)] treats suspensions of non-motile particles as a continuum with an evolving preferred direction; this model describes fibrous materials as diverse as extracellular matrix, textile tufts and plant cell walls. Director-dependent effects are incorporated through a modified stress tensor with four viscosity-like parameters. By making fundamental connections with recent models for active suspensions, we propose a modification to Ericksen's model, mainly the inclusion of self motility; this can be considered the simplest description of an oriented suspension including transversely-isotropic effects. Motivated by the fact that transversely-isotropic fluids exhibit modified flow stability, we conduct a linear stability analysis of two distinct cases, aligned and isotropic suspensions of elongated active particles. Novel aspects include the anisotropic rheology and translational diffusion. In general anisotropic effects increase the instability of small perturbations, whilst translational diffusion stabilises a range of wave-directions and, in some cases, a finite range of wave-numbers, thus emphasising that both anisotropy and translational diffusion can have important effects in these systems.

physics.bio-ph↗

arXiv2016

Shot Range and High Order Correlations in Proteins

The main chain dihedral angles play an important role to decide the protein conformation. The native states of a protein can be regard as an ensemble of a lot of similar conformations, in different conformations the main chain dihedral angles vary in a certain range. Each dihedral angle value can be described as a distribution, but only using the distribution can't describe the real conformation space. The reason is that the dihedral angle has correlation with others, especially the neighbor dihedral angles in primary sequence. In our study we analysis extensive molecular dynamics (MD) simulation trajectories of eleven proteins with different sizes and folds, we found that in stable second structure the correlations only exist between the dihedrals near to each other in primary sequence, long range correlations are rare. But in unstable structures (loop) long range correlations exist. Further we observed some characteristics of the short range correlations in different second structures (α-helix, β-sheet) and we found that we can approximate good high order dihedral angle distribution good only use three order distribution in stable second structure which illustrates that high order correlations (over three order) is small in stable second structure.

q-bio.BM↗

arXiv2018

Viscosity and effective temperature of an active dense system of self-propelled particles

We obtain a nonequilibrium theory for a simple model of a generic class of active dense systems consisting of self-propelled particles with a self-propulsion force, $f_0$, and persistence time, $τ_p$, of their motion. We consider two models of activity and find the system is characterized by an evolving effective temperature $T_{eff}(τ)$, defined through a generalized fluctuation-dissipation theorem. $T_{eff}(τ)$ is equal to the equilibrium temperature at very short time $τ$ and saturates to $T_{eff}=T_{eff}(τ\to\infty)$ at long times; The transition time $t_{trans}$ when $T_{eff}(τ)$ goes to the long-time limit depends on $τ_p$ alone and $t_{trans}\sim τ_p^{0.85}$ for both models. $f_0$ reduces the viscosity with increasing activity, $τ_p$ on the other hand, may increase or decrease viscosity depending on the details of how the activity is included. However, as a function of $T_{eff}$, viscosity shows the same behavior for different models of activity and $η\sim (T_{eff}-T)^{-γ}$ with $γ=1.74$. Our theory gives reasonable agreement when compared with experimental data and is consistent with several experiments on diverse systems.

cond-mat.soft↗

arXiv2017

Neural scaling laws for an uncertain world

Autonomous neural systems must efficiently process information in a wide range of novel environments, which may have very different statistical properties. We consider the problem of how to optimally distribute receptors along a one-dimensional continuum consistent with the following design principles. First, neural representations of the world should obey a neural uncertainty principle---making as few assumptions as possible about the statistical structure of the world. Second, neural representations should convey, as much as possible, equivalent information about environments with different statistics. The results of these arguments resemble the structure of the visual system and provide a natural explanation of the behavioral Weber-Fechner law, a foundational result in psychology. Because the derivation is extremely general, this suggests that similar scaling relationships should be observed not only in sensory continua, but also in neural representations of ``cognitive' one-dimensional quantities such as time or numerosity.

q-bio.NC↗

arXiv2016

Maxwell Matters

Charges are everywhere because most atoms are charged. Chemical bonds are formed by electrons with their charge. Charges move and interact according to Maxwell's equations in space and in atoms where the equations of electrodynamics are embedded in Schroedinger's equation as the potential. Maxwell's equations are universal, valid inside atoms and between stars from times much shorter than those of atomic motion (0.1 femtoseconds) to years (32 mega-seconds). Maxwell's equations enforce the conservation of current. Analysis shows that the electric field can take on whatever value is needed to ensure conservation of current. The properties of matter rearrange themselves to satisfy Maxwell's equations and conservation of current. Conservation of current is as universal as Maxwell's equations themselves. Yet equations of electrodynamics find little place in the literature of material physics, chemistry, or biochemistry. Kinetic models of chemistry and Markov treatments of atomic motion are ordinary differential equations in time and do not satisfy conservation of current unless modified significantly. Systems at equilibrium, without macroscopic flow, have thermal fluctuating currents that are conserved according to the Maxwell equations although their macroscopic averages are zero. The macroscopic consequences of atomic scale fluctuating thermal currents are not known but are likely to be substantial because of the nonlinear interactions in systems like these, in which 'everything interacts with everything else'. Conclusion: current needs to be conserved in models of material systems, even in simplified versions.

physics.bio-ph↗