Arthur R. C. McCray, Tao Zhou, Saugat Kandel
et al.
AbstractThe manipulation and control of nanoscale magnetic spin textures are of rising interest as they are potential foundational units in next-generation computing paradigms. Achieving this requires a quantitative understanding of the spin texture behavior under external stimuli using in situ experiments. Lorentz transmission electron microscopy (LTEM) enables real-space imaging of spin textures at the nanoscale, but quantitative characterization of in situ data is extremely challenging. Here, we present an AI-enabled phase-retrieval method based on integrating a generative deep image prior with an image formation forward model for LTEM. Our approach uses a single out-of-focus image for phase retrieval and achieves significantly higher accuracy and robustness to noise compared to existing methods. Furthermore, our method is capable of isolating sample heterogeneities from magnetic contrast, as shown by application to simulated and experimental data. This approach allows quantitative phase reconstruction of in situ data and can also enable near real-time quantitative magnetic imaging.
Liquid-liquid phase separation is now recognized as a common mechanism for regulating enzyme activity in cells. Insights from studies in cells are complemented by in vitro studies aimed at developing better understanding of mechanisms underlying such control. These mechanisms are often based on the influence of LLPS on the physicochemical properties of the enzyme's environment. Biochemical mechanisms underlying such regulation include the potential for concentrating reactants together, tuning reaction rates, and controlling competing metabolic pathways. LLPS is thus a powerful tool with extensive utilities for the cell, e.g. for consolidating cell survival under stress or rerouting metabolic pathways in response to the energy state of the cell. By examining the evidence of how LLPS affects enzyme catalysis, we can begin to understand emerging concepts and expand our understanding of enzyme catalysis in living cells.
O. V. Gradov, P. A. Nasirov, A. A. Scrynnic
et al.
Micromanipulations, perfusions and measurements performed using glass microelectrodes filled with an electrolyte is a conventional technique for experimental morphological and membrane electrophysiological studies at a single cell and membrane surface level. The typical (effective) diameter of the end of the glass microelectrode is from 500 up to less than 100 nm, which prevents one from observing it using a standard optical microscope in accordance with the optical resolution criteria, since the diameter less than 500 nm is indistinguishable within the interference zone. Microprocessor programming of the puller (microforge) that provides pulling and tearing allows to obtain in certain regimes the adjusted diameter and shape of the micropipette tip, although this result is not fully controlled due to the above limitations. In this connection it is necessary to design the control devices for the micropipette tips both at the preparation and operation stages (intracellular or extracellular insertion). This method also should provide visualization of the processes occurring upon interaction of the microelectrode tip with the cell in real time, depending on the electrode type and state, which allows to level the artifacts arising with the systematic error frequency from the uncontrolled operation of the micropipette tip after different ways of the microelectrode filling with the electrolyte. We propose an installation scheme that solves the above problems by means of introducing an interferometric device for microscopic control of the microelectrode and micromanipulator or microperfusor, for the first time for a given type of optical instruments combined with the interferometric optical scheme.
Almost all current approaches for engineering modular logic components in synthetic biology use first-order regulators, including most CRISPR/CAS, TAL, zinc finger, and RNA interference systems. Many practitioners understand intuitively that second and higher order binding is necessary for scalability, and this is easy to show for single-input single-output systems. However, no study to date has analysed whether a more complex system, utilizing e.g. feedback or error correction, can produce scalable computation from first-order regulators. We prove here that first order repressor systems cannot support bistability. In the process, we introduce a function G to measure signal quality in molecular systems, and we show that G always decreases in dynamic feedback systems as well as static feed-forward logic cascades of first-order repressors. As a result, first order repressors cannot build memory or signal buffering elements. Finally, we suggest G as a potential new property for characterization of standard biological parts.
Osman Kahraman, William S. Klug, Christoph A. Haselwandter
Membrane proteins deform the surrounding lipid bilayer, which can lead to membrane-mediated interactions between neighboring proteins. Using the mechanosensitive channel of large conductance (MscL) as a model system, we demonstrate how the observed differences in protein structure can affect membrane-mediated interactions and cooperativity among membrane proteins. We find that distinct oligomeric states of MscL lead to distinct gateway states for the clustering of MscL, and predict signatures of MscL structure and spatial organization in the cooperative gating of MscL. Our modeling approach establishes a quantitative relation between the observed shapes and cooperative function of membrane~proteins.
The adhesion of cell membranes is mediated by the binding of membrane-anchored receptor and ligand proteins. In this article, we review recent results from simulations and theory that lead to novel insights on how the binding equilibrium and kinetics of these proteins is affected by the membranes and by the membrane anchoring and molecular properties of the proteins. Simulations and theory both indicate that the binding equilibrium constant K2D and the on- and off-rate constants of anchored receptors and ligands in their 'two-dimensional' (2D) membrane environment strongly depend on the membrane roughness from thermally excited shape fluctuations on nanoscales. Recent theory corroborated by simulations provides a general relation between K2D} and the binding constant K3D of soluble variants of the receptors and ligands that lack the membrane anchors and are free to diffuse in three dimensions (3D).
Many chemical reactions in biological cells occur at very low concentrations of constituent molecules. Thus, transcriptional gene-regulation is often controlled by poorly expressed transcription-factors, such as E.coli lac repressor with few tens of copies. Here we study the effects of inherent concentration fluctuations of substrate-molecules on the seminal Michaelis-Menten scheme of biochemical reactions. We present a universal correction to the Michaelis-Menten equation for the reaction-rates. The relevance and validity of this correction for enzymatic reactions and intracellular gene-regulation is demonstrated. Our analytical theory and simulation results confirm that the proposed variance-corrected Michaelis-Menten equation predicts the rate of reactions with remarkable accuracy even in the presence of large non-equilibrium concentration fluctuations. The major advantage of our approach is that it involves only the mean and variance of the substrate-molecule concentration. Our theory is therefore accessible to experiments and not specific to the exact source of the concentration fluctuations.
Jingkui Wang, Benjamin Pfeuty, Quentin Thommen
et al.
Fundamental biological processes such as transcription and translation, where a genetic sequence is sequentially read by a macromolecule, have been well described by a classical model of non-equilibrium statistical physics, the totally asymmetric exclusion principle (TASEP). This model describes particles hopping between sites of a one-dimensional lattice, with the particle current determining the transcription or translation rate. An open problem is how to analyze a TASEP where particles can pause randomly, as has been observed during transcription. In this work, we report that surprisingly, a simple mean-field model predicts well the particle current for all values of the average pause duration, using a simple description of blocking behind paused particles.
In numerous systems in biophysics and related fields, scientists measure (with very smart methods) individual molecules (e.g. biopolymers (proteins, DNA, RNA, etc), nano - crystals, ion channels), aiming at finding a model from the data. But the noise is not solved accurately in not so few cases and this may lead to misleading models. Here, we solve the noise. We consider two state photon trajectories from any on off kinetic scheme (KS): the process emitting photons with a rate Ξ³on when it is in the on state, and emitting with a rate Ξ³off when it is in the off state. We develop a filter that removes the noise resulting in clean data also in cases where binning fails. The filter is a numerical algorithm with various new statistical treatments. It is based on a new general likelihood function developed here, with observable dependent form. The filter can solve the noise, in the detectable region of the rate space: that is, we also find a region where the data is "too" noisy. Consistency tests will find the region's type from the data. If the data is ruled "too noisy", binning obviously fails, and one should apply simpler methods on the raw data and realizing that the extracted information is partial. We show that not applying the filter while cleaning results in erroneous rates. This filter (with minor adjustments) can solve the noise in any discrete state trajectories, yet extensions are needed in "tackling" the noise from other data, e.g. continuous data and FRET data. The filter developed here is complementary with our previous projects in this field, where we have solved clean two state data with the development of reduced dimensions forms (RDFs): only the combined procedures enabling building the most accurate model from noisy trajectories from single molecules
Multiple Sclerosis (MS) is a disorder that usually appears in adults in their thirties. It has a prevalence that ranges between 2 and 150 per 100 000. Epidemiological studies of MS have provided hints on possible causes for the disease ranging from genetic, environmental and infectious factors to other factors of vascular origin. Despite the tremendous effort spent in the last few years, none of the hypotheses formulated so far has gained wide acceptance and the causes of the disease remain unknown. From a clinical point of view, a high correlation has been recently observed between MS and Chronic Cerebro-Spinal Venous Insufficiency (CCSVI) in a statistically significant number of patients. In this pathological situation CCSVI may induce alterations of blood pressure in brain microvessels, thereby perturbing the exchange of small hydrophilic molecules between the blood and the external cells. In the presence of large pressure alterations it cannot be excluded also the leakage of macromolecules that otherwise would not cross the vessel wall. All these disorders may trigger immune defenses with the destruction of myelin as a side effect. In the present work we investigate the role of perturbed blood pressure in brain microvessels as driving force for an altered exchange of small hydrophilic solutes and leakage of macromolecules into the interstitial fluid. With a simplified, yet realistic, model we obtain closed-form steady-state solutions for fluid flow and solute transport across the microvessel wall. Finally, we use these results (i) to interpret experimental data available in the literature and (ii) to carry out a preliminary analysis of the disorder in the exchange processes triggered by an increase of blood pressure, thereby relating our preliminary results to the hypothesised vascular connection to MS.
Andrei S. Kozlov, Johannes Baumgart, Thomas Risler
et al.
The detection of sound begins when energy derived from acoustic stimuli deflects the hair bundles atop hair cells. As hair bundles move, the viscous friction between stereocilia and the surrounding liquid poses a fundamental challenge to the ear's high sensitivity and sharp frequency selectivity. Part of the solution to this problem lies in the active process that uses energy for frequency-selective sound amplification. Here we demonstrate that a complementary part involves the fluid-structure interaction between the liquid within the hair bundle and the stereocilia. Using force measurement on a dynamically scaled model, finite-element analysis, analytical estimation of hydrodynamic forces, stochastic simulation and high-resolution interferometric measurement of hair bundles, we characterize the origin and magnitude of the forces between individual stereocilia during small hair-bundle deflections. We find that the close apposition of stereocilia effectively immobilizes the liquid between them, which reduces the drag and suppresses the relative squeezing but not the sliding mode of stereociliary motion. The obliquely oriented tip links couple the mechanotransduction channels to this least dissipative coherent mode, whereas the elastic horizontal top connectors stabilize the structure, further reducing the drag. As measured from the distortion products associated with channel gating at physiological stimulation amplitudes of tens of nanometres, the balance of forces in a hair bundle permits a relative mode of motion between adjacent stereocilia that encompasses only a fraction of a nanometre. A combination of high-resolution experiments and detailed numerical modelling of fluid-structure interactions reveals the physical principles behind the basic structural features of hair bundles and shows quantitatively how these organelles are adapted to the needs of sensitive mechanotransduction.
Jens Karschau, J. Julian Blow, Alessandro P. S. de Moura
DNA replication is an essential process in biology and its timing must be robust so that cells can divide properly. Random fluctuations in the formation of replication starting points, called origins, and the subsequent activation of proteins lead to variations in the replication time. We analyse these stochastic properties of DNA and derive the positions of origins corresponding to the minimum replication time. We show that under some conditions the minimization of replication time leads to the grouping of origins, and relate this to experimental data in a number of species showing origin grouping.
Berg and Purcell [Biophys. J. 20, 193 (1977)] calculated how the accuracy of concentration sensing by single-celled organisms is limited by noise from the small number of counted molecules. Here we generalize their results to the sensing of concentration ramps, which is often the biologically relevant situation (e.g. during bacterial chemotaxis). We calculate lower bounds on the uncertainty of ramp sensing by three measurement devices: a single receptor, an absorbing sphere, and a monitoring sphere. We contrast two strategies, simple linear regression of the input signal versus maximum likelihood estimation, and show that the latter can be twice as accurate as the former. Finally, we consider biological implementations of these two strategies, and identify possible signatures that maximum likelihood estimation is implemented by real biological systems.
At the onset of X Chromosomes Inactivation, the vital process whereby female mammal cells equalize X products with respect to males, the X chromosomes are colocalized along their Xic (X-Inactivation Center) regions. The mechanism inducing recognition and pairing of the X's remains, though, elusive. Starting from recent discoveries on the molecular factors and on the DNA sequences (the so-called ``pairing sites'') involved, we dissect the mechanical basis of Xic colocalization by using a Statistical Physics model. We show that soluble DNA specific binding molecules, as those experimentally identified, can be indeed sufficient to induce the spontaneous colocalization of the homologous chromosomes, but only when their concentration, or chemical affinity, rises above a threshold value, as a consequence of a thermodynamic phase transition. We derive the likelihood of pairing and its probability distribution. Chromosome dynamics has two stages: an initial independent Brownian diffusion followed, after a characteristic time scale, by recognition and pairing. Finally, we investigate the effects of DNA deletion/insertions in the region of pairing sites and compare model predictions to available experimental data.
Padinhateeri Ranjith, Kirone Mallick, Jean-Francois Joanny
et al.
We study the stochastic dynamics of growth and shrinkage of single actin filaments taking into account insertion, removal, and ATP hydrolysis of subunits either according to the vectorial mechanism or to the random mechanism. In a previous work, we developed a model for a single actin or microtubule filament where hydrolysis occurred according to the vectorial mechanism: the filament could grow only from one end, and was in contact with a reservoir of monomers. Here we extend this approach in several ways, by including the dynamics of both ends and by comparing two possible mechanisms of ATP hydrolysis. Our emphasis is mainly on two possible limiting models for the mechanism of hydrolysis within a single filament, namely the vectorial or the random model. We propose a set of experiments to test the nature of the precise mechanism of hydrolysis within actin filaments.
Biochemical networks can respond to temporal characteristics of time-varying signals. To understand how reliably biochemical networks can transmit information we must consider how an input signal as a function of time--the input trajectory--can be mapped onto an output trajectory. Here we estimate the mutual information between in- and output trajectories using a Gaussian model. We study how reliably the chemotaxis network of E. coli can transmit information on the ligand concentration to the flagellar motor, and find the input power spectrum that maximizes the information transmission rate.
Maurizio De Pitta, Vladislav Volman, Herbert Levine
et al.
The complex dynamics of intracellular calcium regulates cellular responses to information encoded in extracellular signals. Here, we study the encoding of these external signals in the context of the Li-Rinzel model. We show that by control of biophysical parameters the information can be encoded in amplitude modulation, frequency modulation or mixed (AM and FM) modulation. We briefly discuss the possible implications of this new role of information encoding for astrocytes.
We investigate the dynamics of two interacting diffusing particles in an infinite effectively one dimensional system; the particles interact through a step-like potential of width b and height phi_0 and are allowed to pass one another. By solving the corresponding 2+1-variate Fokker-Planck equation an exact result for the two particle conditional probability density function (PDF) is obtained for arbitrary initial particle positions. From the two-particle PDF we obtain the overtake probability, i.e. the probability that the two particles has exchanged positions at time t compared to the initial configuration. In addition, we calculate the trapping probability, i.e. the probability that the two particles are trapped close to each other (within the barrier width b) at time t, which is mainly of interest for an attractive potential, phi_0<0. We also investigate the tagged particle PDF, relevant for describing the dynamics of one particle which is fluorescently labeled. Our analytic results are in excellent agreement with the results of stochastic simulations, which are performed using the Gillespie algorithm.
For a cell moving in hydrodynamic flow above a wall, translational and rotational degrees of freedom are coupled by the Stokes equation. In addition, there is a close coupling of convection and diffusion due to the position-dependent mobility. These couplings render calculation of the mean encounter time between cell surface receptors and ligands on the substrate very difficult. Here we show for a two-dimensional model system how analytical progress can be achieved by treating motion in the vertical direction by an effective reaction term in the mean first passage time equation for the rotational degree of freedom. The strength of this reaction term can either be estimated from equilibrium considerations or used as a fit parameter. Our analytical results are confirmed by computer simulations and allow to assess the relative roles of convection and diffusion for different scaling regimes of interest.
Waipot Ngamsaad, Wannapong Triampo, Paisan Kanthang
et al.
Determining the middle of the bacteria cell and the proper placement of the septum is essential to the division of the bacterial cell. In E. coli, this process depends on the proteins MinC, MinD, and MinE. Here, the Lattice Boltzmann method (LBM) is used to study the dynamics of the oscillations of the min proteins from pole to pole. This determines the midcell division plane at the cellular level. The LBM is applied to the set of the deterministic reaction diffusion equations proposed by Howard et. al. [1] to describe the dynamics of the Min proteins. The LBM results are in good agreement with those of Howard et al, and agree qualitatively with the experimental results. Our good results indicate that the LBM can be an alternative computational tool for simulating problems dealing with complex biological system which are described by the reaction-diffusion equations.