arXiv subjects

Michael Hinczewski

Publications and source records attributed to Michael Hinczewski.

At least 37 records · Page 2Linked to original sources

arXiv2019

Modeling the growth of organisms validates a general relation between metabolic costs and natural selection

Metabolism and evolution are closely connected: if a mutation incurs extra energetic costs for an organism, there is a baseline selective disadvantage that may or may not be compensated for by other adaptive effects. A long-standing, but to date unproven, hypothesis is that this disadvantage is equal to the fractional cost relative to the total resting metabolic expenditure. This hypothesis has found a recent resurgence as a powerful tool for quantitatively understanding the strength of selection among different classes of organisms. Our work explores the validity of the hypothesis from first principles through a generalized metabolic growth model, versions of which have been successful in describing organismal growth from single cells to higher animals. We build a mathematical framework to calculate how perturbations in maintenance and synthesis costs translate into contributions to the selection coefficient. This allows us to show that the hypothesis is an approximation to the actual baseline selection coefficient. Moreover we can derive the correct prefactor in its functional form, as well as analytical bounds on its accuracy for any given realization of the model. We illustrate our general framework using a special case of the growth model, which we show provides a quantitative description of metabolic expenditures in data collected from a wide array of unicellular organisms (both prokaryotes and eukaryotes). In all these cases we demonstrate that the hypothesis is an excellent approximation, allowing estimates of baseline selection coefficients to within 15% of their actual values. Even in a broader biological parameter range, covering growth data from multicellular organisms, the hypothesis continues to work well, always within an order of magnitude of the correct result. Our work thus justifies its use as a versatile tool, setting the stage for its wider deployment.

q-bio.PE↗

arXiv2019

Generalized Brewster-angle effect in thin-film optical absorbers and its application for graphene hydrogen sensing

Generalized Brewster angle (GBA) is the incidence angle at which polarization by reflection for p- and s-polarized light takes place. Realizing s-polarization Brewster effect requires a material with magnetic response which is challenging at optical frequencies since the magnetic response of materials at these frequencies is extremely weak. Here, we experimentally realize GBA effect in the visible using a thin-film absorber system consisting of a dielectric film on an absorbing substrate. Polarization by reflection is realized for both p- and s- polarized light at different angles of incidence and multiple wavelengths. We provide a theoretical framework for the generalized Brewster effect in thin-film light absorbers. We demonstrate hydrogen gas sensing using a single layer graphene film transferred on a thin-film absorber at the GBA with ~1 fg/mm2 aerial mass sensitivity. The ultrahigh sensitivity stems from the strong phase sensitivity near point of darkness, particularly at the GBA, and the strong light-matter interaction in planar nanocavities. These findings depart from the traditional domain of thin-films as mere interference optical coatings and highlight its many potential applications including gas sensing and biosensing.

physics.optics↗

arXiv2018

Thermodynamic Properties of Molecular Communication

In this paper, we consider the energy cost of communicating using molecular communication. In a simplified scenario, we show that the energy bound in Landauer's principle can be achieved, implying that molecular communication can approach fundamental thermodynamic limits.

cs.IT↗

arXiv2017

Optimal information transfer in enzymatic networks: A field theoretic formulation

Signaling in enzymatic networks is typically triggered by environmental fluctuations, resulting in a series of stochastic chemical reactions, leading to corruption of the signal by noise. For example, information flow is initiated by binding of extracellular ligands to receptors, which is transmitted through a {cascade involving} kinase-phosphatase stochastic chemical reactions. For a class of such networks, we develop a general field-theoretic approach in order to calculate the error in signal transmission as a function of an appropriate control variable. Application of the theory to a simple push-pull network, a module in the kinase-phosphatase cascade, recovers the exact results for error in signal transmission previously obtained using umbral calculus (Phys. Rev. X., {\bf 4}, 041017 (2014)). We illustrate the generality of the theory by studying the minimal errors in noise reduction in a reaction cascade with two connected push-pull modules. Such a cascade behaves as an effective {three-species} network with a pseudo intermediate. In this case, optimal information transfer, resulting in the smallest square of the error between the input and output, occurs with a time delay, which is given by the inverse of the decay rate of the pseudo intermediate. There are substantial deviations between simulations and predictions of the linear theory in error in signal propagation in an enzymatic push-pull network for a certain range of parameters. Inclusion of second order perturbative corrections shows that differences between simulations and theoretical predictions are minimized. Our study establishes that {a} field theoretic formulation {of} stochastic biological {signaling offers} a systematic way to understand error propagation in networks of arbitrary complexity.

cond-mat.stat-mech↗

arXiv2016

Protein Collapse is Encoded in the Folded State Architecture

Natural protein sequences that self-assemble to form globular structures are compact with high packing densities in the folded states. It is known that proteins unfold upon addition of denaturants, adopting random coil structures. The dependence of the radii of gyration on protein size in the folded and unfolded states obeys the same scaling laws as synthetic polymers. Thus, one might surmise that the mechanism of collapse in proteins and polymers ought to be similar. However, because the number of amino acids in single domain proteins is not significantly greater than about two hundred, it has not been resolved if the unfolded states of proteins are compact under conditions that favor the folded states - a problem at the heart of how proteins fold. By adopting a theory used to derive polymer-scaling laws, we find that the propensity for the unfolded state of a protein to be compact is universal and is encoded in the contact map of the folded state. Remarkably, analysis of over 2000 proteins shows that proteins rich in $β$-sheets have greater tendency to be compact than $α$-helical proteins. The theory provides insights into the reasons for the small size of single domain proteins and the physical basis for the origin of multi-domain proteins. Application to non-coding RNA molecules show that they have evolved to collapse sharing similarities to $β$-sheet proteins. An implication of our theory is that the evolution of natural foldable sequences is guided by the requirement that for efficient folding they should populate minimum energy compact states under folding conditions. This concept also supports the compaction selection hypothesis used to rationalize the unusually condensed states of viral RNA molecules.

q-bio.BM↗

arXiv2016

Noise Filtering and Prediction in Biological Signaling Networks

Information transmission in biological signaling circuits has often been described using the metaphor of a noise filter. Cellular systems need accurate, real-time data about their environmental conditions, but the biochemical reaction networks that propagate, amplify, and process signals work with noisy representations of that data. Biology must implement strategies that not only filter the noise, but also predict the current state of the environment based on information delayed due to the finite speed of chemical signaling. The idea of a biochemical noise filter is actually more than just a metaphor: we describe recent work that has made an explicit mathematical connection between signaling fidelity in cellular circuits and the classic theories of optimal noise filtering and prediction that began with Wiener, Kolmogorov, Shannon, and Bode. This theoretical framework provides a versatile tool, allowing us to derive analytical bounds on the maximum mutual information between the environmental signal and the real-time estimate constructed by the system. It helps us understand how the structure of a biological network, and the response times of its components, influences the accuracy of that estimate. The theory also provides insights into how evolution may have tuned enzyme kinetic parameters and populations to optimize information transfer.

q-bio.MN↗

arXiv2016

Directly measuring single molecule heterogeneity using force spectroscopy

One of the most intriguing results of single molecule experiments on proteins and nucleic acids is the discovery of functional heterogeneity: the observation that complex cellular machines exhibit multiple, biologically active conformations. The structural differences between these conformations may be subtle, but each distinct state can be remarkably long-lived, with random interconversions between states occurring only at macroscopic timescales, fractions of a second or longer. Though we now have proof of functional heterogeneity in a handful of systems---enzymes, motors, adhesion complexes---identifying and measuring it remains a formidable challenge. Here we show that evidence of this phenomenon is more widespread than previously known, encoded in data collected from some of the most well-established single molecule techniques: AFM or optical tweezer pulling experiments. We present a theoretical procedure for analyzing distributions of rupture/unfolding forces recorded at different pulling speeds. This results in a single parameter, quantifying the degree of heterogeneity, and also leads to bounds on the equilibration and conformational interconversion timescales. Surveying ten published datasets, we find heterogeneity in five of them, all with interconversion rates slower than 10 s$^{-1}$. Moreover, we identify two systems where additional data at realizable pulling velocities is likely to find a theoretically predicted, but so far unobserved cross-over regime between heterogeneous and non-heterogeneous behavior. The significance of this regime is that it will allow far more precise estimates of the slow conformational switching times, one of the least understood aspects of functional heterogeneity.

q-bio.BM↗

arXiv2016

Discrete step sizes of molecular motors lead to bimodal non-Gaussian velocity distributions under force

Fluctuations in the physical properties of biological machines are inextricably linked to their functions. Distributions of run-lengths and velocities of processive molecular motors, like kinesin-1, are accessible through single molecule techniques, yet there is lack a rigorous theoretical model for these probabilities up to now. We derive exact analytic results for a kinetic model to predict the resistive force ($F$) dependent velocity ($P(v)$) and run-length ($P(n)$) distribution functions of generic finitely processive molecular motors that take forward and backward steps on a track. Our theory quantitatively explains the zero force kinesin-1 data for both $P(n)$ and $P(v)$ using the detachment rate as the only parameter, thus allowing us to obtain the variations of these quantities under load. At non-zero $F$, $P(v)$ is non-Gaussian, and is bimodal with peaks at positive and negative values of $v$. The prediction that $P(v)$ is bimodal is a consequence of the discrete step-size of kinesin-1, and remains even when the step-size distribution is taken into account. Although the predictions are based on analyses of kinesin-1 data, our results are general and should hold for any processive motor, which walks on a track by taking discrete steps.

cond-mat.stat-mech↗

arXiv2016

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↗

arXiv2016

Phenomenological and microscopic theories for catch bonds

Lifetimes of bound states of protein complexes or biomolecule folded states typically decrease when subject to mechanical force. However, a plethora of biological systems exhibit the counter-intuitive phenomenon of catch bonding, where non-covalent bonds become stronger under externally applied forces. The quest to understand the origin of catch-bond behavior has lead to the development of phenomenological and microscopic theories that can quantitatively recapitulate experimental data. Here, we assess the successes and limitations of such theories in explaining experimental data. The most widely applied approach is a phenomenological two-state model, which fits all of the available data on a variety of complexes: actomyosin, kinetochore-microtubule, selectin-ligand, and cadherin-catenin binding to filamentous actin. With a primary focus on the selectin family of cell-adhesion complexes, we discuss the positives and negatives of phenomenological models and the importance of evaluating the physical relevance of fitting parameters. We describe a microscopic theory for selectins, which provides a structural basis for catch bonds and predicts a crucial allosteric role for residues Asn82--Glu88. We emphasize the need for new theories and simulations that can mimic experimental conditions, given the complex response of cell adhesion complexes to force and their potential role in a variety of biological contexts.

q-bio.BM↗

arXiv2015

Force-dependent switch in protein unfolding pathways and transition state movements

Although known that single domain proteins fold and unfold by parallel pathways, demonstration of this expectation has been difficult to establish in experiments. Unfolding rate, $k_\mathrm{u}(f)$, as a function of force $f$, obtained in single molecule pulling experiments on src SH3 domain, exhibits upward curvature on a $\log k_\mathrm{u}(f)$ plot. Similar observations were reported for other proteins for the unfolding rate $k_\mathrm{u}([C])$. These findings imply unfolding in these single domain proteins involves a switch in the pathway as $f$ or $[C]$ is increased from a low to a high value. We provide a unified theory demonstrating that if $\log k_\mathrm{u}$ as a function of a perturbation ($f$ or $[C]$) exhibits upward curvature then the underlying energy landscape must be strongly multidimensional. Using molecular simulations we provide a structural basis for the switch in the pathways and dramatic shifts in the transition state ensemble (TSE) in src SH3 domain as $f$ is increased. We show that a single point mutation shifts the upward curvature in $\log k_\mathrm{u}(f)$ to a lower force, thus establishing the malleability of the underlying folding landscape. Our theory, applicable to any perturbation that affects the free energy of the protein linearly, readily explains movement in the TSE in a $β$-sandwich (I27) protein and single chain monellin as the denaturant concentration is varied. We predict that in the force range accessible in laser optical tweezer experiments there should be a switch in the unfolding pathways in I27 or its mutants.

cond-mat.soft↗

arXiv2015

Extracting folding landscape characteristics of biomolecules using mechanical forces

In recent years single molecule force spectroscopy has opened a new avenue to provide profiles of the complex energy landscape of biomolecules. In this field, quantitative analyses of the data employing sound theoretical models, have played a major role in interpreting data and anticipating outcomes of experiments. Here, we explain how by using temperature as a variable in mechanical unfolding of biomolecules in force spectroscopy, the roughness of the energy landscape can be measured without making any assumptions about the underlying reaction coordinate. Estimates of other aspects of the energy landscape such as free energy barriers or the transition state (TS) locations could depend on the precise model used to analyze the experimental data. We illustrate the inherent difficulties in obtaining the transition state location from loading rate or force-dependent unfolding rates. Because the transition state moves as the force or the loading rate is varied, which is reminiscent of the Hammond effect, it is in general difficult to invert the experimental data. The independence of the TS location on force holds good only for brittle or hard biomolecules whereas the TS location changes considerably if the molecule is soft or plastic. Finally, we discuss the goodness of the end-to-end distance (or pulling) coordinate of the molecule as a surrogate reaction coordinate in a situation like force-induced ligand-unbinding from a gated molecular receptor as well as force-quench refolding of an RNA hairpin.

q-bio.BM↗

arXiv2014

Cellular signaling networks function as generalized Wiener-Kolmogorov filters to suppress noise

Cellular signaling involves the transmission of environmental information through cascades of stochastic biochemical reactions, inevitably introducing noise that compromises signal fidelity. Each stage of the cascade often takes the form of a kinase-phosphatase push-pull network, a basic unit of signaling pathways whose malfunction is linked with a host of cancers. We show this ubiquitous enzymatic network motif effectively behaves as a Wiener-Kolmogorov (WK) optimal noise filter. Using concepts from umbral calculus, we generalize the linear WK theory, originally introduced in the context of communication and control engineering, to take nonlinear signal transduction and discrete molecule populations into account. This allows us to derive rigorous constraints for efficient noise reduction in this biochemical system. Our mathematical formalism yields bounds on filter performance in cases important to cellular function---like ultrasensitive response to stimuli. We highlight features of the system relevant for optimizing filter efficiency, encoded in a single, measurable, dimensionless parameter. Our theory, which describes noise control in a large class of signal transduction networks, is also useful both for the design of synthetic biochemical signaling pathways, and the manipulation of pathways through experimental probes like oscillatory input.

q-bio.MN↗

arXiv2014

Plasticity of hydrogen bond networks regulates mechanochemistry of cell adhesion complexes

Mechanical forces acting on cell adhesion receptor proteins regulate a range of cellular functions by formation and rupture of non-covalent interactions with ligands. Typically, force decreases the lifetimes of intact complexes (slip-bonds), making the discovery that these lifetimes can also be prolonged ("catch-bonds"), a surprise. We created a microscopic analytic theory by incorporating the structures of selectin and integrin receptors into a conceptual framework based on the theory of stochastic equations, which quantitatively explains a wide range of experimental data (including catch-bonds at low forces and slip-bonds at high forces). Catch-bonds arise due to force-induced remodeling of hydrogen bond networks, a finding that also accounts for unbinding in structurally unrelated integrin-fibronectin and actomyosin complexes. For the selectin family, remodeling of hydrogen bond networks drives an allosteric transition resulting in the formation of maximum number of hydrogen bonds determined only by the structure of the receptor and is independent of the ligand. A similar transition allows us to predict the increase in number of hydrogen bonds in a particular allosteric state of $α_5 β_1$ integrin-fibronectin complex, a conformation which is yet to be crystallized. We also make a testable prediction that a single point mutation (Tyr51Phe) in the ligand associated with selectin should dramatically alter the nature of the catch-bond compared to the wild type. Our work suggests that nature utilizes a ductile network of hydrogen bonds to engineer function over a broad range of forces.

q-bio.BM↗

arXiv2014

Evidence of disorder in biological molecules from single molecule pulling experiments

Heterogeneity in biological molecules, resulting in molecule-to-molecule variations in their dynamics and function, is an emerging theme. To elucidate the consequences of heterogeneous behavior at the single molecule level, we propose an exactly solvable model in which the unfolding rate due to mechanical force depends parametrically on an auxiliary variable representing an entropy barrier arising from fluctuations in internal dynamics. When the rate of fluctuations, a measure of dynamical disorder, is comparable to or smaller than the rate of force-induced unbinding, we show that there are two experimentally observable consequences: non-exponential survival probability at constant force, and a heavy-tailed rupture force distribution at constant loading rate. By fitting our analytical expressions to data from single molecule pulling experiments on proteins and DNA, we quantify the extent of disorder. We show that only by analyzing data over a wide range of forces and loading rates can the role of disorder due to internal dynamics be quantitatively assessed.

physics.bio-ph↗

arXiv2013

Design principles governing the motility of myosin V

The molecular motor myosin V exhibits a wide repertoire of pathways during the stepping process, which is intimately connected to its biological function. The best understood of these is hand-over-hand stepping by a swinging lever arm movement toward the plus-end of actin filaments, essential to its role as a cellular transporter. However, single-molecule experiments have also shown that the motor "foot stomps", with one hand detaching and rebinding to the same site, and backsteps under sufficient load. Explaining the complete taxonomy of myosin V's load-dependent stepping pathways, and the extent to which these are constrained by motor structure and mechanochemistry, are still open questions. Starting from a polymer model, we develop an analytical theory to understand the minimal physical properties that govern motor dynamics. In particular, we solve the first-passage problem of the head reaching the target binding site, investigating the competing effects of load pulling back at the motor, strain in the leading head that biases the diffusion in the direction of the target, and the possibility of preferential binding to the forward site due to the recovery stroke. The theory reproduces a variety of experimental data, including the power stroke and slow diffusive search regimes in the mean trajectory of the detached head, and the force dependence of the forward-to-backward step ratio, run length, and velocity. The analytical approach yields a formula for the stall force, identifying the relative contributions of the chemical cycle rates and mechanical features like the bending rigidities of the lever arms. Most importantly, by fully exploring the design space of the motor, we predict that myosin V is a robust motor whose dynamical behavior is not compromised by reasonable perturbations to the reaction cycle, and changes in the architecture of the lever arm.

q-bio.BM↗

arXiv2013

From mechanical folding trajectories to intrinsic energy landscapes of biopolymers

In single molecule laser optical tweezer (LOT) pulling experiments a protein or RNA is juxtaposed between DNA handles that are attached to beads in optical traps. The LOT generates folding trajectories under force in terms of time-dependent changes in the distance between the beads. How to construct the full intrinsic folding landscape (without the handles and the beads) from the measured time series is a major unsolved problem. By using rigorous theoretical methods---which account for fluctuations of the DNA handles, rotation of the optical beads, variations in applied tension due to finite trap stiffness, as well as environmental noise and the limited bandwidth of the apparatus---we provide a tractable method to derive intrinsic free energy profiles. We validate the method by showing that the exactly calculable intrinsic free energy profile for a Generalized Rouse Model, which mimics the two-state behavior in nucleic acid hairpins, can be accurately extracted from simulated time series in a LOT setup regardless of the stiffness of the handles. We next apply the approach to trajectories from coarse grained LOT molecular simulations of a coiled-coil protein based on the GCN4 leucine zipper, and obtain a free energy landscape that is in quantitative agreement with simulations performed without the beads and handles. Finally, we extract the intrinsic free energy landscape from experimental LOT measurements for the leucine zipper, which is independent of the trap parameters.

q-bio.BM↗

arXiv2011

Friction contribution to water-bond breakage kinetics

Based on the trajectories of the separation between water molecule pairs from MD simulations, we investigate the bond breakage dynamics in bulk water. From the spectrum of mean first-passage times, the Fokker-Planck equation allows us to derive the diffusivity profile along the separation coordinate and thus to unambiguously disentangle the effects of free-energy and local friction on the separation kinetics. For tightly coordinated water the friction is six times higher than in bulk, which can be interpreted in terms of a dominant reaction path that involves additional orthogonal coordinates.

cond-mat.soft↗