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At least 721 records · Page 40Linked to original sources

Anti-jamming in a fungal transport network

Congestion limits the efficiency of transport networks ranging from highways to the internet. Fungal hyphal networks are studied as an examples of optimal biological transport networks, but the scheduling and direction of traffic to avoid congestion has not been examined. We show here that the Neurospora crassa fungal network exhibits anticongestion: more densely packed nuclei flow faster along hyphal highways, and transported nuclei self-organize into fast flowing solitons. Concentrated transport by solitons may allow cells to cycle between growing and acting as transport conduits.

physics.bio-ph↗

Multi-Constituent Simulation of Thrombus Deposition

In this paper, we present a spatio-temporal mathematical model for simulating the formation and growth of a thrombus. Blood is treated as a multi-constituent mixture comprised of a linear fluid phase and a thrombus (solid) phase. The transport and reactions of 10 chemical and biological species are incorporated using a system of coupled convection-reaction-diffusion (CRD) equations to represent three processes in thrombus formation: initiation, propagation and stabilization. Computational fluid dynamic (CFD) simulations using the libraries of OpenFOAM were performed for two illustrative benchmark problems: in vivo thrombus growth in an injured blood vessel and in vitro thrombus deposition in micro-channels (1.5mm x 1.6mm x 0.1mm) with small crevices (125μm x 75μm and 125μm x 137μm). For both problems, the simulated thrombus deposition agreed very well with experimental observations, both spatially and temporally. Based on the success with these two benchmark problems, which have very different flow conditions and biological environments, we believe that the current model will provide useful insight into the genesis of thrombosis in blood-wetted devices, and provide a tool for the design of less thrombogenic devices.

physics.bio-ph↗

The leaky integrator with recurrent inhibition as a predictor

It is shown that the leaky integrator, the basis for many neuronal models, possesses a negative group delay when a time-delayed recurrent inhibition is added to it. By means of this negative group delay, the leaky integrator becomes a predictor for some frequency components of the input signal. The prediction properties are derived analytically and an application to a local field potential is provided.

q-bio.NC↗

Parasite Spreading in Spatial Ecological Multiplex Networks

Network ecology is a rising field of quantitative biology representing ecosystems as complex networks. A suitable example is parasite spreading: several parasites may be transmitted among their hosts through different mechanisms, each one giving rise to a network of interactions. Modelling these networked, ecological interactions at the same time is still an open challenge. We present a novel spatially-embedded multiplex network framework for modelling multi-host infection spreading through multiple routes of transmission. Our model is inspired by T. cruzi, a parasite transmitted by trophic and vectorial mechanisms. Our ecological network model is represented by a multiplex in which nodes represent species populations interacting through a food web and a parasite contaminative layer at the same time. We modelled an SI dynamics in two different scenarios: a simple theoretical food web and an empirical one. Our simulations in both scenarios show that the infection is more widespread when both the trophic and the contaminative interactions are considered with equal rates. This indicates that trophic and contaminative transmission may have additive effects in real ecosystems. We also find that the ratio of vectors-to-host in the community (i) crucially influences the infection spread, (ii) regulates a percolating phase transition in the rate of parasite transmission and (iii) increases the infection rate in hosts. By immunising the same fractions of predator and prey populations, we show that the multiplex topology is fundamental in outlining the role that each host species plays in parasite transmission in a given ecosystem. We also show that the multiplex models provide a richer phenomenology in terms of parasite spreading dynamics compared to mono-layer models. Our work opens new challenges and provides new quantitative tools for modelling multi-channel spreading in networked systems.

q-bio.QM↗

Test of the diffusing-diffusivity mechanism using near-wall colloidal dynamics

The diffusing-diffusivity mechanism proposed by Chubynsky and Slater [PRL 113, 098302, 2014] predicts that, in environments where the diffusivity changes gradually, the displacement distribution becomes non-Gaussian, even though the mean-squared displacement (MSD) grows linearly with time. Here, we report single-particle tracking measurements of the diffusion of colloidal spheres near a planar wall. Because the local effective diffusivity is known, we have been able to carry out the first direct test of this mechanism for diffusion in inhomogeneous media.

physics.bio-ph↗

Noise control in gene regulatory networks with negative feedback

Genes and proteins regulate cellular functions through complex circuits of biochemical reactions. Fluctuations in the components of these regulatory networks result in noise that invariably corrupts the signal, possibly compromising function. Here, we create a practical formalism based on ideas introduced by Wiener and Kolmogorov (WK) for filtering noise in engineered communications systems to quantitatively assess the extent to which noise can be controlled in biological processes involving negative feedback. Application of the theory, which reproduces the previously proven scaling of the lower bound for noise suppression in terms of the number of signaling events, shows that a tetracycline repressor-based negative-regulatory gene circuit behaves as a WK filter. For the class of Hill-like nonlinear regulatory functions, this type of filter provides the optimal reduction in noise. Our theoretical approach can be readily combined with experimental measurements of response functions in a wide variety of genetic circuits, to elucidate the general principles by which biological networks minimize noise.

q-bio.MN↗

PySpike - A Python library for analyzing spike train synchrony

Understanding how the brain functions is one of the biggest challenges of our time. The analysis of experimentally recorded neural firing patterns (spike trains) plays a crucial role in addressing this problem. Here, the PySpike library is introduced, a Python package for spike train analysis providing parameter-free and time-scale independent measures of spike train synchrony. It allows to compute similarity and dissimilarity profiles, averaged values and distance matrices. Although mainly focusing on neuroscience, PySpike can also be applied in other contexts like climate research or social sciences. The package is available as Open Source on Github and PyPI.

physics.data-an↗

Model of the Songbird Nucleus HVC as a Network of Central Pattern Generators

We propose a functional architecture of the adult songbird nucleus HVC in which the core element is a "functional syllable unit" (FSU). In this model, HVC is organized into FSUs, each of which provides the basis for the production of one syllable in vocalization. Within each FSU, the inhibitory neuron population takes one of two operational states: (A) simultaneous firing wherein all inhibitory neurons fire simultaneously, and (B) competitive firing of the inhibitory neurons. Switching between these basic modes of activity is accomplished via changes in the synaptic strengths among the inhibitory neurons. The inhibitory neurons connect to excitatory projection neurons such that during state (A) the activity of projection neurons is suppressed, while during state (B) patterns of sequential firing of projection neurons can occur. The latter state is stabilized by feedback from the projection to the inhibitory neurons. Song composition for specific species is distinguished by the manner in which different FSUs are functionally connected to each other. Ours is a computational model built with biophysically based neurons. We illustrate that many observations of HVC activity are explained by the dynamics of the proposed population of FSUs, and we identify aspects of the model that are currently testable experimentally. In addition, and standing apart from the core features of an FSU, we propose that the transition between modes may be governed by the biophysical mechanism of neuromodulation.

q-bio.NC↗

Qualitative stability of nonlinear networked systems

In many large systems, such as those encountered in biology or economics, the dynamics are nonlinear and are only known very coarsely. It is often the case, however, that the signs (excitation or inhibition) of individual interactions are known. This paper extends to nonlinear systems the classical criteria of linear sign stability introduced in the 70's, yielding simple sufficient conditions to determine stability using only the sign patterns of the interactions.

eess.SY↗

A universal tradeoff between power, precision and speed in physical communication

Maximizing the speed and precision of communication while minimizing power dissipation is a fundamental engineering design goal. Also, biological systems achieve remarkable speed, precision and power efficiency using poorly understood physical design principles. Powerful theories like information theory and thermodynamics do not provide general limits on power, precision and speed. Here we go beyond these classical theories to prove that the product of precision and speed is universally bounded by power dissipation in any physical communication channel whose dynamics is faster than that of the signal. Moreover, our derivation involves a novel connection between friction and information geometry. These results may yield insight into both the engineering design of communication devices and the structure and function of biological signaling systems.

cond-mat.stat-mech↗

Vesicle adhesion reveals novel universal relationships for biophysical characterization

Adhesion plays an integral role in diverse biological functions ranging from cellular transport to tissue development. Estimation of adhesion strength, therefore, becomes important to gain biophysical insight into these phenomena. In this Letter, we use curvature elasticity to present non-intuitive, yet remarkably simple, universal relationships that capture vesicle-substrate interactions. Our study reveals that the inverse of the height, exponential of the contact area, and the force required to detach the vesicle from the substrate vary linearly with the square root of the adhesion energy. These relationships not only provide efficient strategies to tease out adhesion energy of biological molecules but can also be used to characterize the physical properties of elastic biomimetic nanoparticles. We validate the modeling predictions with experimental data from two previous studies.

physics.bio-ph↗

Statistical Theory of Selectivity and Conductivity in Biological Channels

We present an equilibrium statistical-mechanical theory of selectivity in biological ion channels. In doing so, we introduce a grand canonical ensemble for ions in a channel's selectivity filter coupled to internal and external bath solutions for a mixture of ions at arbitrary concentrations, we use linear response theory to find the current through the filter for small gradients of electrochemical potential, and we show that the conductivity of the filter is given by the generalized Einstein relation. We apply the theory to the permeation of ions through the potassium selectivity filter, and are thereby able to resolve the long-standing paradox of why the high selectivity of the filter brings no associated delay in permeation. We show that the Eisenman selectivity relation follows directly from the condition of diffusion-limited conductivity through the filter. We also discuss the effect of wall fluctuations on the filter conductivity.

physics.bio-ph↗

Detection of HIV-1 antigen based on magnetic tunnel junction sensor and magnetic nanoparticles

In recent years, it is evidenced that the individuals newly infected HIV are transmitting the virus prior to knowing their HIV status. Identifying individuals that are early in infection with HIV antibody negative (window period) remains problematic. In the newly infected individuals, HIV antigen p24 is usually present in their serum or plasma 7-10 days before the HIV antibody. After antibody production initiates, the p24 antigen is bound into immune complexes. That means the detectable p24 antigens in serum/plasma are short-lived, and their amount is in the pg/ml range. Thus, a rapid quantitative bio-detection system with high-sensitivity is required to achieve early disease diagnosis. Magnetoresistive (MR) biosensor with ultra-high sensitivity possesses great potential in this area. In this study, a p24 detection assay using MgO-based magnetic tunnel junction (MTJ) sensor and 20-nm magnetic nanoparticles is reported.

cond-mat.mes-hall↗

Thermodynamics of the collapse transition of the all-backbone peptide Gly15

Simulations show Gly$_{15}$, a polypeptide lacking any side-chains, can collapse in water. We assess the hydration thermodynamics in this collapse by calculating the hydration free energy at each of the end points of the reaction coordinate, here the end-to-end distance ($r$) in the chain. To examine the role of the various conformations for a given $r$, we study the conditional distribution, $P(R_g | r)$, of the radius of gyration for a given value of $r$. $P(R_g|r)$ is found to vary more gently compared to the corresponding variation in the excess hydration free energy. Using this insight within a multistate generalization of the potential distribution theorem, we calculate a reasonable upper bound for the hydration free energy of the peptide for a given $r$. On this basis we find that peptide hydration greatly favors the expanded state of the chain, despite primitive hydrophobic effects favoring chain collapse. The net free energy of collapse is seen to be a delicate balance between opposing intra-peptide and hydration effects, with intra-peptide contributions favoring collapse by a small margin. The favorable intra-peptide interactions are primarily electrostatic in origin, and found to arise primarily from interaction between C=O dipoles, hydrogen bonding interaction between C=O and N-H groups, and favorable interaction between N-H dipoles.

physics.bio-ph↗

Controlled propulsion and separation of helical particles at the nanoscale

Controlling the motion of nano and microscale objects in a fluid environment is a key factor in designing optimized tiny machines that perform mechanical tasks such as transport of drugs or genetic material in cells, fluid mixing to accelerate chemical reactions, and cargo transport in microfluidic chips. Directed motion is made possible by the coupled translational and rotational motion of asymmetric particles. A current challenge in achieving directed and controlled motion at the nanoscale lies in overcoming random Brownian motion due to thermal fluctuations in the fluid. We use a hybrid lattice-Boltzmann Molecular Dynamics method with full hydrodynamic interactions and thermal fluctuations to demonstrate that controlled propulsion of individual nanohelices in an aqueous environment is possible. We optimize the propulsion velocity and the efficiency of externally driven nanohelices. We quantify the importance of the thermal effects on the directed motion by calculating the Péclet number for various shapes, number of turns and pitch lengths of the helices. Consistent with the experimental microscale separation of chiral objects, our results indicate that in the presence of thermal fluctuations at Péclet numbers $>10$, chiral particles follow the direction of propagation according to its handedness and the direction of the applied torque making separation of chiral particles possible at the nanoscale. Our results provide criteria for the design and control of helical machines at the nanoscale.

physics.flu-dyn↗

Influence of pigment-protein coupling on excitation energy transfer in FMO complex

To explain experimentally observed oscillatory dynamics of highly efficient process of excitation energy transfer (EET) in Fenna-Matthews-Olson (FMO) complex, most theoretical models assume the same local protein environment around all the bacteriochlorophyll-a (BChla) sites, contradictory to the structural analysis of FMO complex. Using different values of pigment-protein couplings for different BChla sites, measured in the adiabatic limit of electron transfer, we theoretically investigate the effect of inhomogeneous local protein environment on excitation energy transfer. By employing non-Markovian master equation we demonstrate that the asymmetric system-bath coupling leads to the results consistent with the experimental observations. Quantum dynamical simulation suggests that the correlated fuctuations preserve the oscillation of excitation for long time-scales. Further different BChla sites have asymmetric time-scales of oscillations of excitation due to in-homogeneous pigment-protein couplings also.

physics.bio-ph↗

Quantifying efficient information transduction of biochemical signaling cascades

Cells can be considered as systems that utilize changes in thermodynamic entropy as information. Therefore, they serve as useful models for investigating the relationships between entropy production and information transmission, i.e., signal transduction. Based on the hypothesis that cells apply a chemical reaction cascade for the most efficient transduction of information, we adopted a coding design that minimizes the number of bits per concentration of molecules that are employed for information transduction. As a result, the average rate of entropy production is uniform across all cycles in a cascade reaction. Thus, the entropy production rate can be a valuable measure for the quantification of intracellular signal transduction.

q-bio.MN↗

Force spectroscopy in studying infection

Biophysical force spectroscopy tools - for example optical tweezers, magnetic tweezers, atomic force microscopy, - have been used to study elastic, mechanical, conformational and dynamic properties of single biological specimens from single proteins to whole cells to reveal information not accessible by ensemble average methods such as X-ray crystallography, mass spectroscopy, gel electrophoresis and so on. Here we review the application of these tools on a range of infection-related questions from antibody-inhibited protein processivity to virus-cell adhesion. In each case we focus on how the instrumental design tailored to the biological system in question translates into the functionality suitable for that particular study. The unique insights that force spectroscopy has gained to complement knowledge learned through population averaging techniques in interrogating biomolecular details prove to be instrumental in therapeutic innovations such as those in structure-based drug design.

q-bio.BM↗