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Additive manufacturing has become an important tool for fabricating advanced systems and devices for visible nanophotonics. However, the lack of simulation and optimization methods taking into account the essential physics of the optimization process leads to barriers for greater adoption. This issue can often result in sub-optimal optical responses in fabricated devices on both local and global scales. We propose that physics-informed design and optimization methods, and in particular physics-informed machine learning, are particularly well-suited to overcome these challenges by incorporating known physics, constraints, and fabrication knowledge directly into the design framework.

The concept of a protein diffusing in its free-energy folding landscape has been fruitful for both theory and experiment. Yet the choice of the reaction coordinate (RC) introduces an undesirable degree of arbitrariness into the problem. We analyze extensive simulation data of an α-helix in explicit water solvent as it stochastically folds and unfolds. The free-energy profiles for different RCs exhibit significant variations, some having an activation barrier, while others not. We show that this variation has little effect on the predicted folding kinetics if the diffusivity profiles are properly taken into account. This kinetic quasi-universality is rationalized by an RC rescaling, which, due to the reparameterization invariance of the Fokker–Planck equation, allows the combination of free-energy and diffusivity effects into a single function, the rescaled free-energy profile. This rescaled free energy indeed shows less variation among different RCs than the bare free energy and diffusivity profiles separately do, if we properly distinguish between RCs that contain knowledge of the native state and those that are purely geometric in nature. Our method for extracting diffusivity profiles is easily applied to experimental single molecule time series data and might help to reconcile conflicts that arise when comparing results from different experimental probes for the same protein. [ABSTRACT FROM AUTHOR]

Plasmonic sensors are one of the most powerful optical biosensors, which can be used for the detection of small numbers of molecules at ultra-low concentrations. However, the detection of dilute analytes of low molecular weight (

C-terminally phosphorylated TAR DNA-binding protein of 43 kDa (TDP-43) marks the proteinaceous inclusions that characterize a number of age-related neurodegenerative diseases, including amyotrophic lateral sclerosis, frontotemporal lobar degeneration and Alzheimer's disease. TDP-43 phosphorylation at S403/S404 and (especially) at S409/S410 is, in fact, accepted as a biomarker of proteinopathy. These residues are located within the low complexity domain (LCD), which also drives the protein's liquid-liquid phase separation (LLPS). The impact of phosphorylation at these LCD sites on phase separation of the protein is a topic of great interest, as these post-translational modifications and LLPS are both implicated in proteinopathies. Here, we employed a combination of experimental and simulation-based approaches to explore this question on a phosphomimetic model of the TDP-43 LCD. Our turbidity and fluorescence microscopy data show that phosphomimetic Ser-to-Asp substitutions at residues S403, S404, S409 and S410 alter the LLPS behavior of TDP-43 LCD. In particular, unlike the LLPS of unmodified protein, LLPS of the phosphomimetic variants displays a biphasic dependence on salt concentration. Through coarse-grained modeling, we find that this biphasic salt dependence is derived from an altered mechanism of phase separation, in which LLPS-driving short-range intermolecular hydrophobic interactions are modulated by long-range attractive electrostatic interactions. Overall, this in vitro and in silico study provides a physiochemical foundation for understanding the impact of pathologically relevant C-terminal phosphorylation on the LLPS of TDP-43 in a more complex cellular environment., (© 2024 The Author(s). Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

Imaging and characterizing the dynamics of cellular adhesion in blood samples is of fundamental importance in understanding biological function. In vitro microscopy methods are widely used for this task but typically require diluting the blood with a buffer to allow for transmission of light. However, whole blood provides crucial signaling cues that influence adhesion dynamics, which means that conventional approaches lack the full physiological complexity of living microvasculature. We can reliably image cell interactions in microfluidic channels during whole blood flow by motion blur microscopy (MBM) in vitro and automate image analysis using machine learning. MBM provides a low cost, easy to implement alternative to intravital microscopy, for rapid data generation where understanding cell interactions, adhesion, and motility is crucial. MBM is generalizable to studies of various diseases, including cancer, blood disorders, thrombosis, inflammatory and autoimmune diseases, as well as providing rich datasets for theoretical modeling of adhesion dynamics., (© 2024. The Author(s).)

Imaging and characterizing the dynamics of cellular adhesion in blood samples is of fundamental importance in understanding biological function. In vitro microscopy methods are widely used for this task, but typically require diluting the blood with a buffer to allow for transmission of light. However whole blood provides crucial mechanical and chemical signaling cues that influence adhesion dynamics, which means that conventional approaches lack the full physiological complexity of living microvasculature. We propose to overcome this challenge by a new in vitro imaging method which we call motion blur microscopy (MBM). By decreasing the source light intensity and increasing the integration time during imaging, flowing cells are blurred, allowing us to identify adhered cells. Combined with an automated analysis using machine learning, we can for the first time reliably image cell interactions in microfluidic channels during whole blood flow. MBM provides a low cost, easy to implement alternative to intravital microscopy, the in vivo approach for studying how the whole blood environment shapes adhesion dynamics. We demonstrate the method's reproducibility and accuracy in two example systems where understanding cell interactions, adhesion, and motility is crucial-sickle red blood cells adhering to laminin, and CAR-T cells adhering to E-selectin. We illustrate the wide range of data types that can be extracted from this approach, including distributions of cell size and eccentricity, adhesion durations, trajectories and velocities of adhered cells moving on a functionalized surface, as well as correlations among these different features at the single cell level. In all cases MBM allows for rapid collection and processing of large data sets, ranging from thousands to hundreds of thousands of individual adhesion events. The method is generalizable to study adhesion mechanisms in a variety of diseases, including cancer, blood disorders, thrombosis, inflammatory and autoimmune diseases, as well as providing rich datasets for theoretical modeling of adhesion dynamics., Competing Interests: Conflict-of-Interest Disclosure R.A., U.A.G., and Case Western Reserve University have financial interests in Hemex Health Inc. U.A.G., and Case Western Reserve University have financial interests in BioChip Labs Inc. U.A.G. and Case Western Reserve University have financial interests in Xatek Inc. U.A.G. has financial interests in DxNow Inc. Financial interests include licensed intellectual property, stock ownership, research funding, employment, and consulting. Hemex Health Inc. offers point-of-care diagnostics for hemoglobin disorders, anemia, and malaria. BioChip Labs Inc. offers commercial clinical microfluidic biomarker assays for inherited or acquired blood disorders. Xatek Inc. offers point-of-care global assays to evaluate the hemostatic process. DxNow Inc. offers microfluidic and bio-imaging technologies for in vitro fertilization, forensics, and diagnostics. The competing interests of Case Western Reserve University employees are overseen and managed by the Conflict of Interests Committee according to a Conflict-of-Interest Management Plan.

The evolution of resistance remains one of the primary challenges for modern medicine from infectious diseases to cancers. Many of these resistance-conferring mutations often carry a substantial fitness cost in the absence of treatment. As a result, we would expect these mutants to undergo purifying selection and be rapidly driven to extinction. Nevertheless, pre-existing resistance is frequently observed from drug-resistant malaria to targeted cancer therapies in non-small cell lung cancer (NSCLC) and melanoma. Solutions to this apparent paradox have taken several forms from spatial rescue to simple mutation supply arguments. Recently, in an evolved resistant NSCLC cell line, we found that frequency-dependent ecological interactions between ancestor and resistant mutant ameliorate the cost of resistance in the absence of treatment. Here, we hypothesize that frequency-dependent ecological interactions in general play a major role in the prevalence of pre-existing resistance. We combine numerical simulations with robust analytical approximations to provide a rigorous mathematical framework for studying the effects of frequency-dependent ecological interactions on the evolutionary dynamics of pre-existing resistance. First, we find that ecological interactions significantly expand the parameter regime under which we expect to observe pre-existing resistance. Next, even when positive ecological interactions between mutants and ancestors are rare, these resistant clones provide the primary mode of evolved resistance because even weak positive interaction leads to significantly longer extinction times. We then find that even in the case where mutation supply alone is sufficient to predict pre-existing resistance, frequency-dependent ecological forces still contribute a strong evolutionary pressure that selects for increasingly positive ecological effects (negative frequency-dependent selection). Finally, we genetically engineer several of the most common clinically observed resistance mechanisms to targeted therapies in NSCLC, a treatment notorious for pre-existing resistance. We find that each engineered mutant displays a positive ecological interaction with their ancestor. As a whole, these results suggest that frequency-dependent ecological effects can play a crucial role in shaping the evolutionary dynamics of pre-existing resistance.

Noise control, together with other regulatory functions facilitated by microRNAs (miRNAs), is believed to have played important roles in the evolution of multicellular eukaryotic organisms. miRNAs can dampen protein fluctuations via enhanced degradation of messenger RNA (mRNA), but this requires compensation by increased mRNA transcription to maintain the same expression levels. The overall mechanism is metabolically expensive, leading to questions about how it might have evolved in the first place. We develop a stochastic model of miRNA noise regulation, coupled with a detailed analysis of the associated metabolic costs. Additionally, we calculate binding free energies for a range of miRNA seeds, the short sequences which govern target recognition. We argue that natural selection may have fine-tuned the Michaelis-Menten constant [Formula: see text] describing miRNA-mRNA affinity and show supporting evidence from analysis of experimental data. [Formula: see text] is constrained by seed length, and optimal noise control (minimum protein variance at a given energy cost) is achievable for seeds of 6 to 7 nucleotides in length, the most commonly observed types. Moreover, at optimality, the degree of noise reduction approaches the theoretical bound set by the Wiener-Kolmogorov linear filter. The results illustrate how selective pressure toward energy efficiency has potentially shaped a crucial regulatory pathway in eukaryotes., Competing Interests: Competing interests statement:The authors declare no competing interest.

Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes in both cancer and infectious diseases. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N * 2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more fully reflect the selection of drug resistant genotypes. Furthermore, using synthetic data and empirical dose-response data in cancer, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. By simulating a tumor treated with cyclic drug therapy, we find that mutant selection windows introduced by drug diffusion promote the proliferation of drug resistant cells. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 King et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N -allele fitness seascapes allow for N *2 N -1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more accurately reflect the selection fo drug resistant genotypes. Furthermore, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine.

The scientific effort to control the interaction between light and matter has grown exponentially in the last 2 decades. This growth has been aided by the development of scientific and technological tools enabling the manipulation of light at deeply sub-wavelength scales, unlocking a large variety of novel phenomena spanning traditionally distant research areas. Here, the role of chirality in light-matter interactions is reviewed by providing a broad overview of its properties, materials, and applications. A perspective on future developments is highlighted, including the growing role of machine learning in designing advanced chiroptical materials to enhance and control light-matter interactions across several scales., (© 2022 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

Structural coloring is a photostable and environmentally friendly coloring approach that harnesses optical interference and nanophotonic resonances to obtain colors with a range of applications including display technologies, colorful solar panels, steganography, décor, data storage, and anticounterfeiting measures. We show that optical coatings exhibiting the photonic Fano Resonance present an ideal platform for structural coloring; they provide full color access, high color purity, high brightness, controlled iridescence, and scalable manufacturing. We show that an additional oxide film deposited on Fano resonant optical coatings (FROCs) increases the color purity (up to 99%) and color gamut coverage range of FROCs to 61% of the CIE color space. For wide-area structural coloring applications, FROCs have a significant advantage over existing structural coloring schemes., (© 2023. The Author(s).)

Could the phenomenon of catch bonding-force-strengthened cellular adhesion-play a role in sickle cell disease, where abnormal red blood cell (RBC) adhesion obstructs blood flow? Here, we investigate the dynamics of sickle RBCs adhering to a surface functionalized with the protein laminin (a component of the extracellular matrix around blood vessels) under physiologically relevant microscale flow. First, using total internal reflectance microscopy we characterize the spatial fluctuations of the RBC membrane above the laminin surface before detachment. The complex dynamics we observe suggest the possibility of catch bonding, where the mean detachment time of the cell from the surface initially increases to a maximum and then decreases as a function of shear force. We next conduct a series of shear-induced detachment experiments on blood samples from 25 sickle cell disease patients, quantifying the number and duration of adhered cells under both sudden force jumps and linear force ramps. The experiments reveal that a subset of patients does indeed exhibit catch bonding. By fitting the data to a theoretical model of the bond dynamics, we can extract the mean bond lifetime versus force for each patient. The results show a striking heterogeneity among patients, both in terms of the qualitative behavior (whether or not there is catch bonding) and in the magnitudes of the lifetimes. Patients with large bond lifetimes at physiological forces are more likely to have certain adverse clinical features, like a diagnosis of pulmonary arterial hypertension and intracardiac shunts. By introducing an in vitro platform for fully characterizing RBC-laminin adhesion dynamics, our approach could contribute to the development of patient-specific antiadhesive therapies for sickle cell disease. The experimental setup is also easily generalizable to studying adhesion dynamics in other cell types, for example, leukocytes or cancer cells, and can incorporate disease-relevant environmental conditions like oxygen deprivation., Competing Interests: Declaration of interests R.A., J.A.L., U.A.G., and Case Western Reserve University have financial interests in Hemex Health Inc. J.A.L., U.A.G., and Case Western Reserve University have financial interests in BioChip Labs Inc. U.A.G. and Case Western Reserve University have financial interests in Xatek Inc. U.A.G. has financial interests in DxNow Inc. Financial interests include licensed intellectual property, stock ownership, research funding, employment, and consulting. Hemex Health Inc. offers point-of-care diagnostics for hemoglobin disorders, anemia, and malaria. BioChip Labs Inc. offers commercial clinical microfluidic biomarker assays for inherited or acquired blood disorders. Xatek Inc. offers point-of-care global assays to evaluate the hemostatic process. DxNow Inc. offers microfluidic and bio-imaging technologies for in vitro fertilization, forensics, and diagnostics. Competing interests of Case Western Reserve University employees are overseen and managed by the Conflict of Interests Committee according to a Conflict-of-Interest Management Plan., (Copyright © 2023 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

The evolutionary consequences of quorum sensing in regulating bacterial cooperation are not fully understood. In this study, we reveal unexpected effects of regulating public good production through quorum sensing on bacterial population dynamics, showing that quorum sensing can be a collectively harmful alternative to unregulated production. We analyze a birth-death model of bacterial population dynamics accounting for public good production and the presence of non-producing cheaters. Our model demonstrates that when demographic noise is a factor, the consequences of controlling public good production according to quorum sensing depend on the cost of public good production and the growth rate of populations in the absence of public goods. When public good production is inexpensive, quorum sensing is a destructive alternative to unconditional production, in terms of the mean population extinction time. When costs are higher, quorum sensing becomes a constructive strategy for the producing strain, both stabilizing cooperation and decreasing the risk of population extinction., Competing Interests: The authors have declared that no competing interests exist.

Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painful vascular occlusion events. Numerous studies employ microfluidic-assay-based monitoring tools to quantify characteristics of adhered sRBCs from high resolution channel images. The current image analysis workflow relies on detailed morphological characterization and cell counting by a specially trained worker. This is time and labor intensive, and prone to user bias artifacts. Here we establish a morphology based classification scheme to identify two naturally arising sRBC subpopulations-deformable and non-deformable sRBCs-utilizing novel visual markers that link to underlying cell biomechanical properties and hold promise for clinically relevant insights. We then set up a standardized, reproducible, and fully automated image analysis workflow designed to carry out this classification. This relies on a two part deep neural network architecture that works in tandem for segmentation of channel images and classification of adhered cells into subtypes. Network training utilized an extensive data set of images generated by the SCD BioChip, a microfluidic assay which injects clinical whole blood samples into protein-functionalized microchannels, mimicking physiological conditions in the microvasculature. Here we carried out the assay with the sub-endothelial protein laminin. The machine learning approach segmented the resulting channel images with 99.1±0.3% mean IoU on the validation set across 5 k-folds, classified detected sRBCs with 96.0±0.3% mean accuracy on the validation set across 5 k-folds, and matched trained personnel in overall characterization of whole channel images with R2 = 0.992, 0.987 and 0.834 for total, deformable and non-deformable sRBC counts respectively. Average analysis time per channel image was also improved by two orders of magnitude (∼ 2 minutes vs ∼ 2-3 hours) over manual characterization. Finally, the network results show an order of magnitude less variance in counts on repeat trials than humans. This kind of standardization is a prerequisite for the viability of any diagnostic technology, making our system suitable for affordable and high throughput disease monitoring., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: UAG and Case Western Reserve University have financial interests in BioChip Labs Inc. (Cleveland, Ohio). BioChip Labs Inc. offers commercial clinical microfluidic biomarker assays for inherited or acquired blood disorders. Financial interests include licensed intellectual property, stock ownership, research funding, employment, and consulting. UAG and Case Western Reserve University have financial interests in Xatek Inc. (Cleveland, Ohio). Xatek Inc. offers point-of-care global assays to evaluate the hemostatic process. Financial interests include licensed intellectual property and stock ownership. UAG and Case Western Reserve University have financial interests in Hemex Health Inc. (Portland, Oregon). Hemex Health Inc. offers point-of-care diagnostics for hemoglobin disorders, anemia, and malaria. Financial interests include licensed intellectual property, stock ownership, research funding, employment, and consulting. UAG has financial interests in DxNow Inc. (Gaithersburg, MD). DxNow Inc. offers microfluidic and bio-imaging technologies for in vitro fertilization, forensics, and diagnostics. Financial interests include licensed intellectual property and stock ownership. Competing interests of Case Western Reserve University employees are overseen and managed by the Conflict of Interests Committee according to a Conflict-of-Interest Management Plan. A patent application has been filed related to the methods discussed in this work.

Accurate propagation of signals through stochastic biochemical networks involves significant expenditure of cellular resources. The same is true for regulatory mechanisms that suppress fluctuations in biomolecular populations. Wiener-Kolmogorov (WK) optimal noise filter theory, originally developed for engineering problems, has recently emerged as a valuable tool to estimate the maximum performance achievable in such biological systems for a given metabolic cost. However, WK theory has one assumption that potentially limits its applicability: it relies on a linear, continuum description of the reaction dynamics. Despite this, up to now no explicit test of the theory in nonlinear signaling systems with discrete molecular populations has ever seen performance beyond the WK bound. Here we report the first direct evidence of the bound being broken. To accomplish this, we develop a theoretical framework for multilevel signaling cascades, including the possibility of feedback interactions between input and output. In the absence of feedback, we introduce an analytical approach that allows us to calculate exact moments of the stationary distribution for a nonlinear system. With feedback, we rely on numerical solutions of the system's master equation. The results show WK violations in two common network motifs: a two-level signaling cascade and a negative feedback loop. However, the magnitude of the violation is biologically negligible, particularly in the parameter regime where signaling is most effective. The results demonstrate that while WK theory does not provide strict bounds, its predictions for performance limits are excellent approximations, even for nonlinear systems.

Anemia affects over 25% of the world's population with the heaviest burden borne by women and children. Genetic hemoglobin (Hb) variants, such as sickle cell disease, are among the major causes of anemia. Anemia and Hb variant are pathologically interrelated and have an overlapping geographical distribution. We present the first point-of-care (POC) platform to perform both anemia detection and Hb variant identification, using a single paper-based electrophoresis test. Feasibility of this new integrated diagnostic approach is demonstrated via testing individuals with anemia and/or sickle cell disease. Hemoglobin level determination is performed by an artificial neural network (ANN) based machine learning algorithm, which achieves a mean absolute error of 0.55 g dL -1 and a bias of -0.10 g dL -1 against the gold standard (95% limits of agreement: 1.5 g dL -1 ) from Bland-Altman analysis on the test set. Resultant anemia detection is achieved with 100% sensitivity and 92.3% specificity. With the same tests, subjects with sickle cell disease were identified with 100% sensitivity and specificity. Overall, the presented platform enabled, for the first time, integrated anemia detection and hemoglobin variant identification using a single point-of-care test.

Optical coatings are integral components of virtually every optical instrument. However, despite being a century-old technology, there are only a handful of optical coating types. Here, we introduce a type of optical coatings that exhibit photonic Fano resonance, or a Fano-resonant optical coating (FROC). We expand the coupled mechanical oscillator description of Fano resonance to thin-film nanocavities. Using FROCs with thicknesses in the order of 300 nm, we experimentally obtained narrowband reflection akin to low-index-contrast dielectric Bragg mirrors and achieved control over the reflection iridescence. We observed that semi-transparent FROCs can transmit and reflect the same colour as a beam splitter filter, a property that cannot be realized through conventional optical coatings. Finally, FROCs can spectrally and spatially separate the thermal and photovoltaic bands of the solar spectrum, presenting a possible solution to the dispatchability problem in photovoltaics, that is, the inability to dispatch solar energy on demand. Our solar thermal device exhibited power generation of up to 50% and low photovoltaic cell temperatures (~30 °C), which could lead to a six-fold increase in the photovoltaic cell lifetime.

Deviations from linearity in the dependence of the logarithm of protein unfolding rates, log k u ( f ), as a function of mechanical force, f , measurable in single molecule experiments, can arise for many reasons. In particular, upward curvature in log k u ( f ) as a function of f implies that the underlying energy landscape must be multidimensional with the possibility that unfolding ensues by parallel pathways. Here, simulations using the SOP-SC model of a wild type β-sandwich protein and several mutants, with immunoglobulin folds, show upward curvature in the unfolding kinetics. There are substantial changes in the structures of the transition state ensembles as the force is increased, signaling a switch in the unfolding pathways. Our results, when combined with previous theoretical and experimental studies, show that parallel unfolding of structurally unrelated single domain proteins can be determined from the dependence of log k u ( f ) as a function of force (or log k u [C] where [C] is the denaturant concentration).

The molecular motor myosin V transports cargo by stepping on actin filaments, executing a random diffusive search for actin binding sites at each step. A recent experiment suggests that the joint between the myosin lever arms may not rotate freely, as assumed in earlier studies, but instead has a preferred angle giving rise to structurally constrained diffusion. We address this controversy through comprehensive analytical and numerical modeling of myosin V diffusion and stepping. When the joint is constrained, our model reproduces the experimentally observed diffusion, allowing us to estimate bounds on the constraint energy. We also test the consistency between the constrained diffusion model and previous measurements of step size distributions and the load dependence of various observable quantities. The theory lets us address the biological significance of the constrained joint and provides testable predictions of new myosin behaviors, including the stomp distribution and the run length under off-axis force., Competing Interests: DH, RT, MH, DT No competing interests declared, (© 2020, Hathcock et al.)

The generalized Brewster angle (GBA) is the incidence angle at polarization by reflection for p- or s-polarized light. Realizing an 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 the 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/mm 2 aerial mass sensitivity. The ultrahigh sensitivity stems from the strong phase sensitivity near the 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., Competing Interests: The authors declare no competing financial interest.

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.

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. We validate this result from physical principles through a general growth model and show it holds to excellent approximation for experimental parameters drawn from a wide range of species.

The adherens junctions between epithelial cells involve a protein complex formed by E-cadherin, β-catenin, α-catenin and F-actin. The stability of this complex was a puzzle for many years, since in vitro studies could reconstitute various stable subsets of the individual proteins, but never the entirety. The missing ingredient turned out to be mechanical tension: a recent experiment that applied physiological forces to the complex with an optical tweezer dramatically increased its lifetime, a phenomenon known as catch bonding. However, in the absence of a crystal structure for the full complex, the microscopic details of the catch bond mechanism remain mysterious. Building on structural clues that point to α-catenin as the force transducer, we present a quantitative theoretical model for how the catch bond arises, fully accounting for the experimental lifetime distributions. The underlying hypothesis is that force induces a rotational transition between two conformations of α-catenin, overcoming a significant energy barrier due to a network of salt bridges. This transition allosterically regulates the energies at the interface between α-catenin and F-actin. The model allows us to predict these energetic changes, as well as highlighting the importance of the salt bridge rotational barrier. By stabilizing one of the α-catenin states, this barrier could play a role in how the complex responds to additional in vivo binding partners like vinculin. Since significant conformational energy barriers are a common feature of other adhesion systems that exhibit catch bonds, our model can be adapted into a general theoretical framework for integrating structure and function in a variety of force-regulated protein complexes., Competing Interests: The authors have declared that no competing interests exist.

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 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 [Hinczewski and Thirumalai, Phys. Rev. X 4, 041017 (2014)2160-330810.1103/PhysRevX.4.041017]. 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 pseudointermediate. 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 pseudointermediate. Surprisingly, in these examples the minimum error computed using simulations that take nonlinearities and discrete nature of molecules into account coincides with the predictions of a linear theory. In contrast, 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.

Folded states of single domain globular proteins are compact with high packing density. The radius of gyration, R g , of both the folded and unfolded states increase as N ν where N is the number of amino acids in the protein. The values of the Flory exponent ν are, respectively, ≈⅓ and ≈0.6 in the folded and unfolded states, coinciding with those for homopolymers. However, the extent of compaction of the unfolded state of a protein under low denaturant concentration (collapsibility), conditions favoring the formation of the folded state, is unknown. We develop a theory that uses the contact map of proteins as input to quantitatively assess collapsibility of proteins. Although collapsibility is universal, the propensity to be compact depends on the protein architecture. Application of the theory to over two thousand proteins shows that collapsibility depends not only on N but also on the contact map reflecting the native structure. A major prediction of the theory is that β-sheet proteins are far more collapsible than structures dominated by α-helices. The theory and the accompanying simulations, validating the theoretical predictions, provide insights into the differing conclusions reached using different experimental probes assessing the extent of compaction of proteins. By calculating the criterion for collapsibility as a function of protein length we provide quantitative insights into the reasons why single domain proteins are small and the physical reasons for the origin of multi-domain proteins. Collapsibility of non-coding RNA molecules is similar β-sheet proteins structures adding support to "Compactness Selection Hypothesis".

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 led 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., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Surface plasmon resonance (SPR) sensors operate mainly on prism and grating coupling techniques, with spectral and angular scans being the two major interrogation schemes. Among them, the angular scan technique has several advantages including higher measurement precision owing to its higher signal-to-noise ratio. The currently available SPR sensor arrangements provide a maximum angular sensitivity of 500°-600° per RIU. Here, we report the study of grating coupled-hyperbolic metamaterial (GC-HMM) sensors with high angular sensitivity. The experimental studies show extraordinary angular sensitivities from visible to near infrared (NIR) wavelengths by exciting bulk plasmon polaritons associated with hyperbolic metamaterials, with a maximum of 7000° per RIU. This angular-scan plasmonic biosensor has been used for the detection of low molecular weight biomolecules such as biotin (244 Da) and high molecular weight macromolecules such as Cowpea mosaic virus (CPMV, 5.6 × 10 6 Da) at ultralow concentrations. The miniaturized sensing device can be integrated with microfluidic systems for the development of next-generation biosensors for lab-on-a-chip and point-of-care applications.

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, but rigorous theoretical models for these probabilities are lacking. Here, 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. 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. In addition, we predict the F dependence of these quantities. At nonzero F, P(v) is non-Gaussian and is bimodal with peaks at positive and negative values of v, which is due to the discrete step size of kinesin-1. 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.