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1.
Phys Rev Lett ; 123(22): 228103, 2019 Nov 29.
Artigo em Inglês | MEDLINE | ID: mdl-31868401

RESUMO

Neuronal activity induces changes in blood flow by locally dilating vessels in the brain microvasculature. How can the local dilation of a single vessel increase flow-based metabolite supply, given that flows are globally coupled within microvasculature? Solving the supply dynamics for rat brain microvasculature, we find one parameter regime to dominate physiologically. This regime allows for robust increase in supply independent of the position in the network, which we explain analytically. We show that local coupling of vessels promotes spatially correlated increased supply by dilation.


Assuntos
Encéfalo/irrigação sanguínea , Microvasos/fisiologia , Modelos Cardiovasculares , Animais , Encéfalo/metabolismo , Microcirculação/fisiologia , Microvasos/inervação , Microvasos/metabolismo , Neurônios/fisiologia , Ratos
2.
Phys Rev Lett ; 123(23): 238102, 2019 Dec 06.
Artigo em Inglês | MEDLINE | ID: mdl-31868483

RESUMO

Collagen consists of three peptides twisted together through a periodic array of hydrogen bonds. Here we use this as inspiration to find design rules for programmed specific interactions for self-assembling synthetic collagenlike triple helices, starting from disordered configurations. The assembly generically nucleates defects in the triple helix, the characteristics of which can be manipulated by spatially varying the enthalpy of helix formation. Defect formation slows assembly, evoking kinetic pathologies that have been observed to mutations in the primary collagen amino acid sequence. The controlled formation and interaction between defects gives a route for hierarchical self-assembly of bundles of twisted filaments.


Assuntos
Colágeno/química , Modelos Químicos , Sequência de Aminoácidos , Modelos Moleculares , Nanoestruturas/química , Peptídeos/química , Conformação Proteica em alfa-Hélice
3.
Proc Natl Acad Sci U S A ; 116(49): 24402-24407, 2019 Dec 03.
Artigo em Inglês | MEDLINE | ID: mdl-31754038

RESUMO

Programmable self-assembly of smart, digital, and structurally complex materials from simple components at size scales from the macro to the nano remains a long-standing goal of material science. Here, we introduce a platform based on magnetic encoding of information to drive programmable self-assembly that works across length scales. Our building blocks consist of panels with different patterns of magnetic dipoles that are capable of specific binding. Because the ratios of the different panel-binding energies are scale-invariant, this approach can, in principle, be applied down to the nanometer scale. Using a centimeter-sized version of these panels, we demonstrate 3 canonical hallmarks of assembly: controlled polymerization of individual building blocks; assembly of 1-dimensional strands made of panels connected by elastic backbones into secondary structures; and hierarchical assembly of 2-dimensional nets into 3-dimensional objects. We envision that magnetic encoding of assembly instructions into primary structures of panels, strands, and nets will lead to the formation of secondary and even tertiary structures that transmit information, act as mechanical elements, or function as machines on scales ranging from the nano to the macro.

4.
Biophys J ; 117(3): 520-532, 2019 Aug 06.
Artigo em Inglês | MEDLINE | ID: mdl-31353036

RESUMO

The accurate prediction of RNA secondary structure from primary sequence has had enormous impact on research from the past 40 years. Although many algorithms are available to make these predictions, the inclusion of non-nested loops, termed pseudoknots, still poses challenges arising from two main factors: 1) no physical model exists to estimate the loop entropies of complex intramolecular pseudoknots, and 2) their NP-complete enumeration has impeded their study. Here, we address both challenges. First, we develop a polymer physics model that can address arbitrarily complex pseudoknots using only two parameters corresponding to concrete physical quantities-over an order of magnitude fewer than the sparsest state-of-the-art phenomenological methods. Second, by coupling this model to exhaustive enumeration of the set of possible structures, we compute the entire free energy landscape of secondary structures resulting from a primary RNA sequence. We demonstrate that for RNA structures of ∼80 nucleotides, with minimal heuristics, the complete enumeration of possible secondary structures can be accomplished quickly despite the NP-complete nature of the problem. We further show that despite our loop entropy model's parametric sparsity, it performs better than or on par with previously published methods in predicting both pseudoknotted and non-pseudoknotted structures on a benchmark data set of RNA structures of ≤80 nucleotides. We suggest ways in which the accuracy of the model can be further improved.

5.
Proc Natl Acad Sci U S A ; 116(31): 15344-15349, 2019 Jul 30.
Artigo em Inglês | MEDLINE | ID: mdl-31311866

RESUMO

The numerical solution of partial differential equations (PDEs) is challenging because of the need to resolve spatiotemporal features over wide length- and timescales. Often, it is computationally intractable to resolve the finest features in the solution. The only recourse is to use approximate coarse-grained representations, which aim to accurately represent long-wavelength dynamics while properly accounting for unresolved small-scale physics. Deriving such coarse-grained equations is notoriously difficult and often ad hoc. Here we introduce data-driven discretization, a method for learning optimized approximations to PDEs based on actual solutions to the known underlying equations. Our approach uses neural networks to estimate spatial derivatives, which are optimized end to end to best satisfy the equations on a low-resolution grid. The resulting numerical methods are remarkably accurate, allowing us to integrate in time a collection of nonlinear equations in 1 spatial dimension at resolutions 4× to 8× coarser than is possible with standard finite-difference methods.

6.
Proc Natl Acad Sci U S A ; 116(24): 11624-11629, 2019 Jun 11.
Artigo em Inglês | MEDLINE | ID: mdl-31127041

RESUMO

Deep neural networks have achieved state-of-the-art accuracy at classifying molecules with respect to whether they bind to specific protein targets. A key breakthrough would occur if these models could reveal the fragment pharmacophores that are causally involved in binding. Extracting chemical details of binding from the networks could enable scientific discoveries about the mechanisms of drug actions. However, doing so requires shining light into the black box that is the trained neural network model, a task that has proved difficult across many domains. Here we show how the binding mechanism learned by deep neural network models can be interrogated, using a recently described attribution method. We first work with carefully constructed synthetic datasets, in which the molecular features responsible for "binding" are fully known. We find that networks that achieve perfect accuracy on held-out test datasets still learn spurious correlations, and we are able to exploit this nonrobustness to construct adversarial examples that fool the model. This makes these models unreliable for accurately revealing information about the mechanisms of protein-ligand binding. In light of our findings, we prescribe a test that checks whether a hypothesized mechanism can be learned. If the test fails, it indicates that the model must be simplified or regularized and/or that the training dataset requires augmentation.

7.
Nat Commun ; 9(1): 4348, 2018 10 19.
Artigo em Inglês | MEDLINE | ID: mdl-30341303

RESUMO

Creating a selective gel that filters particles based on their interactions is a major goal of nanotechnology, with far-reaching implications from drug delivery to controlling assembly pathways. However, this is particularly difficult when the particles are larger than the gel's characteristic mesh size because such particles cannot passively pass through the gel. Thus, filtering requires the interacting particles to transiently reorganize the gel's internal structure. While significant advances, e.g., in DNA engineering, have enabled the design of nano-materials with programmable interactions, it is not clear what physical principles such a designer gel could exploit to achieve selective permeability. We present an equilibrium mechanism where crosslink binding dynamics are affected by interacting particles such that particle diffusion is enhanced. In addition to revealing specific design rules for manufacturing selective gels, our results have the potential to explain the origin of selective permeability in certain biological materials, including the nuclear pore complex.


Assuntos
Hidrogéis/química , Nanotecnologia/métodos , Polímeros/química , Difusão , Modelos Moleculares , Tamanho da Partícula , Permeabilidade
8.
J R Soc Interface ; 15(143)2018 06.
Artigo em Inglês | MEDLINE | ID: mdl-29875282

RESUMO

Lichens fix carbon dioxide from the air to build biomass. Crustose and foliose lichens grow as nearly flat, circular disks. Smaller individuals grow slowly, but with small, steady increases in radial growth rate over time. Larger individuals grow more quickly and with a roughly constant radial velocity maintained over the lifetime of the lichen. We translate the coffee drop effect to model lichen growth and demonstrate that growth patterns follow directly from the diffusion of carbon dioxide in the air around a lichen. When a lichen is small, carbon dioxide is fixed across its surface, and the entire thallus contributes to radial growth, but when a lichen is larger carbon dioxide is disproportionately fixed at the edges of an individual, which are the primary drivers of growth. Tests of the model against data suggest it provides an accurate, robust, and universal framework for understanding the growth dynamics of both large and small lichens in nature.


Assuntos
Dióxido de Carbono/metabolismo , Líquens/crescimento & desenvolvimento , Modelos Biológicos
9.
Proc Natl Acad Sci U S A ; 115(12): 2936-2941, 2018 03 20.
Artigo em Inglês | MEDLINE | ID: mdl-29507204

RESUMO

The nasal cavity is a vital component of the respiratory system that heats and humidifies inhaled air in all vertebrates. Despite this common function, the shapes of nasal cavities vary widely across animals. To understand this variability, we here connect nasal geometry to its function by theoretically studying the airflow and the associated scalar exchange that describes heating and humidification. We find that optimal geometries, which have minimal resistance for a given exchange efficiency, have a constant gap width between their side walls, while their overall shape can adhere to the geometric constraints imposed by the head. Our theory explains the geometric variations of natural nasal cavities quantitatively, and we hypothesize that the trade-off between high exchange efficiency and low resistance to airflow is the main driving force shaping the nasal cavity. Our model further explains why humans, whose nasal cavities evolved to be smaller than expected for their size, become obligate oral breathers in aerobically challenging situations.


Assuntos
Cavidade Nasal/anatomia & histologia , Animais , Simulação por Computador , Humanos , Modelos Biológicos , Fenômenos Fisiológicos Respiratórios
10.
Proc Natl Acad Sci U S A ; 115(14): 3593-3598, 2018 04 03.
Artigo em Inglês | MEDLINE | ID: mdl-29555757

RESUMO

A ubiquitous feature of bacterial communities is the existence of spatial structures. These are often coupled to metabolism, whereby the spatial organization can improve chemical reaction efficiency. However, it is not clear whether or how a desired colony configuration, for example, one that optimizes some overall global objective, could be achieved by individual cells that do not have knowledge of their positions or of the states of all other cells. By using a model which consists of cells producing enzymes that catalyze coupled metabolic reactions, we show that simple, local rules can be sufficient for achieving a global, community-level goal. In particular, even though the optimal configuration varies with colony size, we demonstrate that cells regulating their relative enzyme levels based solely on local metabolite concentrations can maintain the desired overall spatial structure during colony growth. We also show that these rules can be very simple and hence easily implemented by cells. Our framework also predicts scenarios where additional signaling mechanisms may be required.


Assuntos
Bactérias/crescimento & desenvolvimento , Bactérias/metabolismo , Fenômenos Biológicos , Meio Ambiente , Modelos Biológicos , Fenômenos Bioquímicos
11.
J Phys Chem B ; 122(4): 1545-1550, 2018 02 01.
Artigo em Inglês | MEDLINE | ID: mdl-29338265

RESUMO

Understanding and controlling polyelectrolyte adsorption onto carbon nanotubes is a fundamental challenge in nanotechnology. Polyelectrolytes have been shown to stabilize nanotube suspensions through adsorbing onto the nanotube surface, and polyelectrolyte-coated nanotubes are emerging as building blocks for complex and addressable self-assembly. Conventional wisdom suggests that polyelectrolyte adsorption onto nanotubes is driven by specific chemical or van der Waals interactions. We develop a simple mean-field model and show that ion-image attraction significantly effects adsorption onto conducting nanotubes at low salt concentrations. Our theory suggests a simple strategy to selectively and reversibly functionalize carbon nanotubes on the basis of their electronic structures, which in turn modify the ion-image attraction.

12.
Phys Rev Lett ; 119(20): 208101, 2017 Nov 17.
Artigo em Inglês | MEDLINE | ID: mdl-29219382

RESUMO

In many contexts, it is extremely costly to perform enough high-quality experimental measurements to accurately parametrize a predictive quantitative model. However, it is often much easier to carry out large numbers of experiments that indicate whether each sample is above or below a given threshold. Can many such categorical or "coarse" measurements be combined with a much smaller number of high-resolution or "fine" measurements to yield accurate models? Here, we demonstrate an intuitive strategy, inspired by statistical physics, wherein the coarse measurements are used to identify the salient features of the data, while the fine measurements determine the relative importance of these features. A linear model is inferred from the fine measurements, augmented by a quadratic term that captures the correlation structure of the coarse data. We illustrate our strategy by considering the problems of predicting the antimalarial potency and aqueous solubility of small organic molecules from their 2D molecular structure.

13.
Phys Rev Lett ; 119(14): 144501, 2017 Oct 06.
Artigo em Inglês | MEDLINE | ID: mdl-29053310

RESUMO

The relationship between the microstructure of a porous medium and the observed flow distribution is still a puzzle. We resolve it with an analytical model, where the local correlations between adjacent pores, which determine the distribution of flows propagated from one pore downstream, predict the flow distribution. Numerical simulations of a two-dimensional porous medium verify the model and clearly show the transition of flow distributions from δ-function-like via Gaussians to exponential with increasing disorder. Comparison to experimental data further verifies our numerical approach.

14.
Proc Natl Acad Sci U S A ; 114(20): 5136-5141, 2017 05 16.
Artigo em Inglês | MEDLINE | ID: mdl-28465441

RESUMO

Complex behaviors are typically associated with animals, but the capacity to integrate information and function as a coordinated individual is also a ubiquitous but poorly understood feature of organisms such as slime molds and fungi. Plasmodial slime molds grow as networks and use flexible, undifferentiated body plans to forage for food. How an individual communicates across its network remains a puzzle, but Physarum polycephalum has emerged as a novel model used to explore emergent dynamics. Within P. polycephalum, cytoplasm is shuttled in a peristaltic wave driven by cross-sectional contractions of tubes. We first track P. polycephalum's response to a localized nutrient stimulus and observe a front of increased contraction. The front propagates with a velocity comparable to the flow-driven dispersion of particles. We build a mathematical model based on these data and in the aggregate experiments and model identify the mechanism of signal propagation across a body: The nutrient stimulus triggers the release of a signaling molecule. The molecule is advected by fluid flows but simultaneously hijacks flow generation by causing local increases in contraction amplitude as it travels. The molecule is initiating a feedback loop to enable its own movement. This mechanism explains previously puzzling phenomena, including the adaptation of the peristaltic wave to organism size and P. polycephalum's ability to find the shortest route between food sources. A simple feedback seems to give rise to P. polycephalum's complex behaviors, and the same mechanism is likely to function in the thousands of additional species with similar behaviors.


Assuntos
Modelos Biológicos , Physarum polycephalum/fisiologia , Transdução de Sinais/fisiologia
15.
Proc Natl Acad Sci U S A ; 114(17): 4342-4347, 2017 04 25.
Artigo em Inglês | MEDLINE | ID: mdl-28396424

RESUMO

Colloidal particles endowed with specific time-dependent interactions are a promising route for realizing artificial materials that have the properties of living ones. Previous work has demonstrated how this system can give rise to self-replication. Here, we introduce the process of colloidal catalysis, in which clusters of particles catalyze the creation of other clusters through templating reactions. Surprisingly, we find that simple templating rules generically lead to the production of huge numbers of clusters. The templating reactions among this sea of clusters give rise to an exponentially growing catalytic cycle, a specific realization of Dyson's notion of an exponentially growing metabolism. We demonstrate this behavior with a fixed set of interactions between particles chosen to allow a catalysis of a specific six-particle cluster from a specific seven-particle cluster, yet giving rise to the catalytic production of a sea of clusters of sizes between 2 and 11 particles. The fact that an exponentially growing cycle emerges naturally from such a simple scheme demonstrates that the emergence of exponentially growing metabolisms could be simpler than previously imagined.

16.
Lab Chip ; 17(8): 1475-1480, 2017 04 11.
Artigo em Inglês | MEDLINE | ID: mdl-28294220

RESUMO

Many powders employed in the food and pharmaceutical industries are produced through spray drying because it is a cost efficient process that offers control over the particle size. However, most commercially available spray-driers cannot produce drops with diameters below 1 µm, limiting the size of spray-dried particles to values above 300 nm. We recently developed a microfluidic spray-drier that can form much smaller drops than commercially available spray-driers. This is achieved through a two-step process: first, the microfluidic spray-drier operates in the dripping regime to form 100 µm diameter primary drops in air and, second, subjects them to high shear stresses due to supersonic flow of air to break them into many much smaller secondary drops. In this paper, we describe the two essential steps required to form sub-µm diameter airborne drops inside microfluidic channels. We investigate the influence of the device geometry on the ability to operate the microfluidic spray-drier in the dripping regime. Moreover, we describe how these primary drops are nebulized into many secondary drops that are much smaller than the smallest dimension of the spray-drier channels.

17.
Proc Natl Acad Sci U S A ; 114(2): 257-262, 2017 01 10.
Artigo em Inglês | MEDLINE | ID: mdl-28034922

RESUMO

Controlling motion at the microscopic scale is a fundamental goal in the development of biologically inspired systems. We show that the motion of active, self-propelled colloids can be sufficiently controlled for use as a tool to assemble complex structures such as braids and weaves out of microscopic filaments. Unlike typical self-assembly paradigms, these structures are held together by geometric constraints rather than adhesive bonds. The out-of-equilibrium assembly that we propose involves precisely controlling the 2D motion of active colloids so that their path has a nontrivial topology. We demonstrate with proof-of-principle Brownian dynamics simulations that, when the colloids are attached to long semiflexible filaments, this motion causes the filaments to braid. The ability of the active particles to provide sufficient force necessary to bend the filaments into a braid depends on a number of factors, including the self-propulsion mechanism, the properties of the filament, and the maximum curvature in the braid. Our work demonstrates that nonequilibrium assembly pathways can be designed using active particles.

18.
Phys Rev Lett ; 117(23): 238004, 2016 Dec 02.
Artigo em Inglês | MEDLINE | ID: mdl-27982625

RESUMO

We construct a scheme for self-replicating square clusters of particles in two spatial dimensions, and validate it with computer simulations in a finite-temperature heat bath. We find that the self-replication reactions propagate through the bath in the form of Fisher waves. Our model reflects existing colloidal systems, but is simple enough to allow simulation of many generations and thereby the first study of evolutionary dynamics in an artificial system. By introducing spatially localized mutations in the replication rules, we show that the mutated cluster population can survive and spread with the expanding front in circular sectors of the colony.

19.
Phys Rev Lett ; 117(17): 178103, 2016 Oct 21.
Artigo em Inglês | MEDLINE | ID: mdl-27824465

RESUMO

How do the topology and geometry of a tubular network affect the spread of particles within fluid flows? We investigate patterns of effective dispersion in the hierarchical, biological transport network formed by Physarum polycephalum. We demonstrate that a change in topology-pruning in the foraging state-causes a large increase in effective dispersion throughout the network. By comparison, changes in the hierarchy of tube radii result in smaller and more localized differences. Pruned networks capitalize on Taylor dispersion to increase the dispersion capability.


Assuntos
Transporte Biológico , Physarum polycephalum , Modelos Biológicos
20.
Proc Natl Acad Sci U S A ; 113(48): 13564-13569, 2016 11 29.
Artigo em Inglês | MEDLINE | ID: mdl-27856761

RESUMO

Rapid determination of whether a candidate compound will bind to a particular target receptor remains a stumbling block in drug discovery. We use an approach inspired by random matrix theory to decompose the known ligand set of a target in terms of orthogonal "signals" of salient chemical features, and distinguish these from the much larger set of ligand chemical features that are not relevant for binding to that particular target receptor. After removing the noise caused by finite sampling, we show that the similarity of an unknown ligand to the remaining, cleaned chemical features is a robust predictor of ligand-target affinity, performing as well or better than any algorithm in the published literature. We interpret our algorithm as deriving a model for the binding energy between a target receptor and the set of known ligands, where the underlying binding energy model is related to the classic Ising model in statistical physics.


Assuntos
Biologia Computacional/métodos , Descoberta de Drogas/métodos , Algoritmos , Ligantes , Modelos Teóricos , Ligação Proteica , Proteínas/química
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