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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Random Tensor Networks with Non-trivial Links

Abstract Random tensor networks are a powerful toy model for understanding the entanglement structure of holographic quantum gravity. However, unlike holographic quantum gravity, their entanglement spectra are flat. It has therefore been argued that a better model consists of random tensor networks with link states that are not maximally entangled, i.e., have non-trivial spectra. In this work, we initiate a systematic study of the entanglement properties of these networks. We employ tools from free probability, random matrix theory, and one-shot quantum information theory to study random tensor networks with bounded and unbounded variation in link spectra, and in cases where a subsystem has one or multiple minimal cuts. If the link states have bounded spectral variation, the limiting entanglement spectrum of a subsystem with two minimal cuts can be expressed as a free product of the entanglement spectra of each cut, along with a Marchenko–Pastur distribution. For a class of states with unbounded spectral variation, analogous to semiclassical states in quantum gravity, we relate the limiting entanglement spectrum of a subsystem with two minimal cuts to the distribution of the minimal entanglement across the two cuts. In doing so, we draw connections to previous work on split transfer protocols, entanglement negativity in random tensor networks, and Euclidean path integrals in quantum gravity.

Physics↗
DOE OSTI2015

Image feature based GPS trace filtering for road network generation and road segmentation

We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segment road regions. Lastly, our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.

58 GEOSCIENCES↗
DOE OSTI2023

TDD LoRa and Delta Encoding in Low-Power Networks of Environmental Sensor Arrays for Temperature and Deformation Monitoring

Abstract Densely distributed sensor networks can revolutionize environmental observations by providing real-time data with an unprecedented spatiotemporal resolution. However, field deployments often pose unique challenges in terms of power provisions and wireless connectivity. We present a framework for wirelessly connected distributed sensor arrays for near-surface temperature and/or deformation monitoring. Our research focuses on a novel time division duplex implementation of the LoRa protocol, enabling battery powered base stations and avoiding collisions within the network. In order to minimize transmissions and improve battery life throughout the network, we propose a dedicated delta encoding algorithm that utilizes the spatial and temporal similarity in the acquired data sets. We implemented the developed technologies in a AA battery powered hardware platform that can be used as a wireless data logger or base station, and we conducted an assessment of the power consumption. Without data compression, the projected battery life for a data logger is 4.74 years, and a wireless base stations can last several weeks or months depending on the amount of network traffic. The delta encoding algorithm can further improve this battery life with a factor of up to 3.50. Our results demonstrate the viability of the proposed methods for low-power environmental wireless sensor networks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations

Gillespie’s direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie’s direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termed network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie’s direct method for network-free simulation. Lastly, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Dystonia-4 (DYT4)-associated TUBB4A mutants exhibit disorganized microtubule networks and inhibit neuronal process growth

Highlights: • Dystonia-4-associated TUBB4A mutants (R2G) exhibit disorganized tubulin networks. • Dystonia-4-associated TUBB4A mutants (A271T) exhibit disorganized tubulin networks. • Dystonia-4-associated mutants have diminished ability to interact with alpha-type tubulins. • Dystonia-4-associated mutants show inhibited neurite growth in a neuronal cell line. Dystonia-1 (DYT1) is an autosomal dominant early-onset torsion form of dystonia, a neurological disease affecting movement. DYT1 is the prototypic hereditary dystonia and is caused by the mutation of the tor1a gene. The gene product has chaperone functions important for the control of protein folding and stability. Dystonia-4 (DYT4) is another autosomal dominant dystonia that is characterized by onset in the second to third decade of progressive laryngeal dysphonia. DYT4 is associated with the mutation of the tubb4a gene, although it remains to be understood how disease-associated mutation affects biochemical as well as cell biological properties of the gene product as the microtubule component (a tubulin beta subunit). Herein we demonstrate that DYT4-associated TUBB4A missense mutants (Arg2-to-Gly or Ala271-to-Thr) form disorganized tubulin networks in cells. Transfected mutants are indeed expressed in cytoplasmic regions, as observed in wild-type transfectants. However, mutant proteins do not exhibit typical radial tubulin networks. Rather, they have diminished ability to interact with tubulin alpha subunits. Processes do not form in sufficient amounts in cells of the N1E-115 neuronal cell line expressing each of these mutants as compared to parental cells. Together, DYT4-associated TUBB4A mutants themselves form aberrant tubulin networks and inhibit neuronal process growth, possibly explaining progress through the pathological states at cellular levels.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Sampling two-dimensional isometric tensor network states

Sampling a quantum system’s underlying probability distributions is an important computational task, e.g., for quantum advantage experiments and quantum Monte Carlo algorithms. Tensor networks are an invaluable tool for efficiently representing states of large quantum systems with limited entanglement. Algorithms for sampling one-dimensional (1D) tensor networks are well-established and utilized in several 1D tensor network methods. In this paper we introduce two novel sampling algorithms for two-dimensional (2D) isometric tensor network states (isoTNS) that generalize existing 1D tensor network sampling algorithms. Our first proposed algorithm performs independent sampling and yields a single configuration together with its associated probability. The second algorithm employs a greedy search strategy to identify high-probability configurations and their corresponding probabilities. Numerical results demonstrate the effectiveness of these algorithms across quantum states with varying entanglement and system size.

Dumitrescu, Eugene [ORNL] (ORCID:0000000158519567)↗
DOE OSTI2014

Assembling supramolecular networks by halogen bonding in coordination polymers driven by 5-bromonicotinic acid

A series of six coordination compounds ([Zn(5-Brnic){sub 2}]·1.5H{sub 2}O){sub n} (1), [Cd(5-Brnic){sub 2}]{sub n} (2), [Co(5-Brnic){sub 2}(H{sub 2}O){sub 2}]{sub n} (3), [Zn(5-Brnic){sub 2}(H{sub 2}biim)]{sub n} (4), ([Cd(5-Brnic){sub 2}(phen)]·H{sub 2}O){sub n} (5), and [Pb(5-Brnic){sub 2}(phen)] (6) have been generated by the hydrothermal method from the metal(II) nitrates, 5-bromonicotinic acid (5-BrnicH), and an optional ancillary 1,10-phenanthroline (phen) or 2,2′-biimidazole (H{sub 2}biim) ligand. All the products 1–6 have been characterized by IR spectroscopy, elemental, thermal, powder and single-crystal X-ray diffraction analyses. Their 5-bromonicotinate-driven structures vary from the 3D metal-organic framework with the seh-3,5-P21/c topology (in 2) and the 2D interdigitated layers with the sql topology (in 1 and 3), to the 1D chains (in 4 and 5) and the 0D discrete monomers (in 6). The 5-bromonicotinate moiety acts as a versatile building block and its tethered bromine atom plays a key role in reinforcing and extending the structures into diverse 3D supramolecular networks via the various halogen bonding Br⋯O, Br⋯Br, and Br⋯π interactions, as well as the N–H⋯O and C–H⋯O hydrogen bonds. The obtained results demonstrate a useful guideline toward engineering the supramolecular architectures in the coordination network assembly under the influence of various halogen bonding interactions. The luminescent (for 1, 2, 4, 5, and 6) and magnetic (for 3) properties have also been studied and discussed in detail. - Graphical abstract: Six coordination compounds driven by 5-bromonicotinic acid have been generated and structurally characterized, revealing diverse metal-organic networks that are further reinforced and extended via various halogen bonding interactions. - Highlights: • 5-Bromonicotinic acid is a versatile ligand for Zn, Cd, Co and Pb derivatives. • Careful selection of co-ligands and metals resulted in different network structures. • Halogen and hydrogen bonding interactions lead to various supramolecular networks. • Luminescent and magnetic properties were studied and discussed in detail.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Controllable synthesis of cobalt oxide nanoflakes on three-dimensional porous cobalt networks as high-performance cathode for alkaline hybrid batteries

Highlights: • Construct self-supported porous Co networks. • Porous Co/CoO composite films show high capacity and good cycling life. • Porous conductive metal network is favorable for fast ion/electron transfer. - Abstract: Herein we report porous three-dimensional cobalt networks supported CoO nanoflakes by the combination of successive electro-deposition methods. The electrodeposited Co networks have average large pores of ∼5 μm and all the branches are composed of interconnected nanoparticles. CoO nanoflakes with thickness of ∼15 nm are uniformly coated on the Co networks forming self-supported Co/CoO composite films. The as-prepared Co/CoO composite films possess combined properties of porous structure and strong mechanical stability. As cathode for alkaline hybrid batteries, the Co/CoO composite films exhibit good electrochemical performances with high capacity of 83.5 mAh g{sup −1} at 1 A g{sup −1} and stable high-rate cycling life (65 mAh g{sup −1} at 10 A g{sup −1} after 15,000 cycles). The hierarchical porous architecture provides positive roles in the enhancement of electrochemical properties, including fast electronic transportation path, short diffusion of ions and high contact area between the active material and the electrolyte.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Tau covariance patterns in Alzheimer's disease patients match intrinsic connectivity networks in the healthy brain

According to the network model of neurodegeneration, the spread of pathogenic proteins occurs selectively along connected brain regions. We tested in vivo whether the distribution of filamentous tau (measured with [ 18 F]flortaucipir-PET), fibrillar amyloid-β ([ 11 C]PIB-PET) and glucose hypometabolism ([ 18 F]FDG-PET) follows the intrinsic functional organization of the healthy brain. We included 63 patients with Alzheimer's disease (AD; 30 male, 63 ± 8 years) who underwent [ 18 F]flortaucipir, [ 11 C]PIB and [ 18 F]FDG PET, and 1000 young adults (427 male, 21 ± 3 years) who underwent task-free fMRI. We selected six predefined disease epicenters as seeds for whole-brain voxelwise covariance analyses to compare correlated patterns of tracer uptake across AD patients against fMRI intrinsic connectivity patterns in young adults. We found a striking convergence between [18F]flortaucipir covariance patterns and intrinsic connectivity maps (range Spearman rho's: 0.32-0.78, p < .001), which corresponded with expected functional networks (range goodness-of-fit: 3.8-8.2). The topography of amyloid-β covariance patterns was more diffuse and less network-specific, while glucose hypometabolic patterns were more spatially restricted than tau but overlapped with functional networks. These findings suggest that the spatial patterns of tau and glucose hypometabolism observed in AD resemble the functional organization of the healthy brain, supporting the notion that tau pathology spreads through circumscribed brain networks and drives neurodegeneration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Nanopore networks in colloidal silica assemblies characterized by XCT for confined fluid flow modeling

Fluid flow through nanopore structures has exhibited different behavior than that described by Darcy's law, derived from pore networks of micrometer scale. Here, pressure assemblies of colloidal silica spheres of specified diameters from 94 to 620 nanometers were prepared as model nano-porous media. The smaller silica spheres tended to form random packing, but some substantial ordering was found in the packing of the larger silica spheres. X-ray computed tomography (XCT), with a voxel resolution of 16 nm, was used to characterize the nanopore networks. The porosity of the Nano-XCT pore network (34%) was less than the total porosity of the assemblies as determined by X-ray attenuation (52%), possibly due to the limits of voxel resolution during segmentation. Fluid flow in the nanopore networks was simulated using the single-phase Lattice Boltzmann Method, and the simulated permeability was compared with the empirical and experimental values. Based on our characterization, although a well-ordered packing of silica spheres was not achieved, it was found that the nanopore networks in the colloidal silica assemblies had pore size distributions corresponding to the particle sizes. The simulated permeability was less than the experimental measurement for water flow, but the complex packing of silica spheres and surface chemistry issues need to be considered in future research.

02 PETROLEUM↗
DOE OSTI2018

Ruta graveolens water extract inhibits cell-cell network formation in human umbilical endothelial cells via MEK-ERK1/2 pathway

Highlights: • RGWE inhibits HUVEC ability to form tubes on matrigel in vitro. • RGWE interferes, through MEK/ERK, with the VEGF-mediated signaling that leads to angiogenesis. • RGWE is proposed as a new therapeutic tool to prevent pathological angiogenesis. Angiogenesis is a process encompassing several steps such as endothelial cells proliferation, differentiation and migration to form a vascular network, involving different signal transduction pathways. Among these, ERK1/2 signaling mediates VEGF-dependent signaling pathway. Here we report that the water extract of Ruta graveolens (RGWE), widely known as a medicinal plant, is able to impair in a dose-dependent manner, cell network formation without affecting cell viability. Biochemical analysis showed that the major component of RGWE is rutin, unable to reproduce RGWE effect. We found that RGWE inhibits ERK1/2 phosphorylation and that this event is crucial in cell network formation since the transfection of HUVEC with a constitutively active MEK (caMEK), the ERK1/2 activator, induces a robust cell network formation as compared to untransfected and/or mock transfected cells and, more importantly, caMEK transfected cells became unresponsive to RGWE. Moreover, RGWE inhibits VEGF and nestin gene expression, necessary for vessel formation, and the caMEK transfection induces their higher expression. In conclusion, we report that RGWE is able to significantly impair vessels network formation without affecting cell viability, preventing ERK1/2 activation and, in turn, down-regulating VEGF and nestin expression. These findings point to RGWE as a potential therapeutic tool capable to interfere with pathologic angiogenesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. Furthermore, the Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Encoder–decoder neural network for solving the nonlinear Fokker–Planck–Landau collision operator in XGC

An encoder–decoder neural network has been used to examine the possibility for acceleration of a partial integro-differential equation, the Fokker–Planck–Landau collision operator. This is part of the governing equation in the massively parallel particle-in-cell code XGC, which is used to study turbulence in fusion energy devices. The neural network emphasizes physics-inspired learning, where it is taught to respect physical conservation constraints of the collision operator by including them in the training loss, along with the ℓ 2 loss. In particular, network architectures used for the computer vision task of semantic segmentation have been used for training. A penalization method is used to enforce the ‘soft’ constraints of the system and integrate error in the conservation properties into the loss function. During training, quantities representing the particle density, momentum and energy for all species of the system are calculated at each configuration vertex, mirroring the procedure in XGC. This simple training has produced a median relative loss, across configuration space, of the order of 10 –4 , which is low enough if the error is of random nature, but not if it is of drift nature in time steps. The run time for the current Picard iterative solver of the operator is O(n 2 ), where n is the number of plasma species. As the XGC1 code begins to attack problems including a larger number of species, the collision operator will become expensive computationally, making the neural network solver even more important, especially since its training only scales as O(n). Here, a wide enough range of collisionality has been considered in the training data to ensure the full domain of collision physics is captured. An advanced technique to decrease the losses further will be subject of a subsequent report. Eventual work will include expansion of the network to include multiple plasma species.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Toughening of Thermoresponsive Arrested Networks of Elastin-Like Polypeptides To Engineer Cytocompatible Tissue Scaffolds

Formulation of tissue engineering or regenerative scaffolds from simple bioactive polymers with tunable structure and mechanics is crucial for the regeneration of complex tissues, and hydrogels from recombinant proteins, such as elastin-like polypeptides (ELPs), are promising platforms to support these applications. The arrested phase separation of ELPs has been shown to yield remarkably stiff, biocontinuous, nanostructured networks, but these gels are limited in applications by their relatively brittle nature. Here, a gel-forming ELP is chain-extended by telechelic oxidative coupling, forming extensible, tough hydrogels. Small angle scattering indicates that the chain-extended polypeptides form a fractal network of nanoscale aggregates over a broad concentration range, accessing moduli ranging from 5 kPa to over 1 MPa over a concentration range of 5–30 wt %. These networks exhibited excellent erosion resistance and allowed for the diffusion and release of encapsulated particles consistent with a bicontinuous, porous structure with a broad distribution of pore sizes. Furthermore, biofunctionalized, toughened networks were found to maintain the viability of human mesenchymal stem cells (hMSCs) in 2D, demonstrating signs of osteogenesis even in cell media without osteogenic molecules. Furthermore, chondrocytes could be readily mixed into these gels via thermoresponsive assembly and remained viable in extended culture. These studies demonstrate the ability to engineer ELP-based arrested physical networks on the molecular level to form reinforced, cytocompatible hydrogel matrices, supporting the promise of these new materials as candidates for the engineering and regeneration of stiff tissues.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Roles of Alkali Metals and Ionic Networks in Directing the Formation of Anionic Metal–Organic Frameworks

Integrating different types of fundamental chemical interactions to direct the self-assembly processes and to control the dimension of the network formed by each type of interactions is a promising strategy to develop advanced functional materials. Here, the ionic interaction of alkali metals (Na, K, Cs) towards multi-functional 2,2’-disulfo-4,4’-oxydibenzoic acid is utilized in two solvent systems, aqueous-amide and uro-amide, to synthesize six novel anionic framework materials with dramatically different features and dimensionalities: (Na 3 [Zn 4 O(L 4- ) 3 (e-murea)(H 2 O) 3 ](H 2 N(CH 3 ) 2 +) 3 (CPM-s1), [Zn 4 O(L 4- ) 3 (DMF)]((NH 2 (CH 3 ) 2 +) 6 ) (CPM-s2), Na 6 [(Zn 2 OH 2 ) 3 (L 3- ) 6 ](H 2 O) 3 (CPM-s3), K 3 [Zn 2 (L 4- ) 2 ](H 2 N(CH 3 ) 2 +)(e-murea) 2 (H 2 O) 3 (CPM-s4), K 4 [Zn(L 3- ) 2 ](H 2 O) 2 (CPM-s5), and Cs 6 [Zn(L 4- ) 2 ](H 2 O) 2 (CPM-s6), L 4- = 2,2’-disulfo-4,4’-oxydibenzoate, DMF = N,N-dimethylformamide, e-murea = 1,3-dimethyl-1-2-imidazolidinone). In CPM-s1 to CPM-s6, the alkali-sulfonate ionic bonding networks transformed from 0D clusters with Na + to 1D chains and 2D sheets with K + , and 3D network with Cs + . These vastly differing ionic domains influenced the coordination modes and spatial arrangements of the metal-carboxylate linkers, which consequently, directed the dimensionality of the coordination networks from 3D with Na + to 1D with Cs + . CPM-s1 and CPM-s2, made from tetrameric Zn 4 O clusters are rare examples of anionic MOF-5-type structures. CPM-s3 features an unprecedented hexameric Zn 6 cluster. Even though a large portion of the charge-balancing cations in these materials would hamper hydrogen bonding networks, some of them still exhibit very high ionic conductivity (e.g. 1.25 x 10 -3 S cm -1 in CPM-s3), suggesting the potential of these materials as solid-state electrolytes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Electroreduction-Driven Formation and Connectivity of Polyoxometalate Coordination Networks

We present the synthesis of metal oxide coordination networks based on Preyssler-type polyoxoanions ([NaP 5 W 30 O 110 ] 14– and [NaP 5 MoW 29 O 110 ] 14– ) bridged with metal–aquo complexes ([M(H 2 O) n ] m+ , M m+ = Co 2+ , Ni 2+ , Zn 2+ , Y 3+ ), induced by electrochemical reduction. Networks bridged with first-row transition metals are isostructural with a previously reported Co-bridged structure, while the Y 3+ -bridged structure is new. All networks feature an uncommon binding motif of the metal cation to the oxygen atoms at cap positions, which we hypothesize is due to increased electron density at the cap upon reduction. Oxidation of a Zn 2+ -bridged network resulted in a new structure in which Zn 2+ –O cap bonds are lost, indicating the importance of reduction in the connectivity of these polyoxometalate-based coordination networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Water Network Shape-Dependence of Local Interactions with the Microhydrated –NO 2 – and –CO 2 – Anionic Head Groups by Cold Ion Vibrational Spectroscopy

We report the structural evolutions of water networks and solvatochromic response of the CH 3 NO 2 – radical anion in the OH and CH stretching regions by analysis of the vibrational spectra displayed by cryogenically cooled CH 3 NO 2 – ·(H 2 O) n=1–6 clusters. The OH stretching bands evolve with a surprisingly large discontinuity at n = 6, which features the emergence of an intense, strongly red-shifted band along with a weaker feature that appears in the region assigned to a free OH fundamental. Very similar behavior is displayed by the perdeuterated carboxylate clusters, RCO 2 – ·(H 2 O) n=5–7 (R = CD 3 CD 2 ), indicating that this behavior is a general feature in the microhydration of the triatomic anionic domain and not associated with CH oscillators. Electronic structure calculations trace this behavior to the formation of a “book” isomer of the water hexamer that adopts a configuration in which one of the water molecules resides in an acceptor–acceptor–donor (AAD) (A = acceptor, D = donor) H-bonding site. Excitation of the bound OH in the AAD site explores the local network topology best suited to stabilize an incipient –XO 2 H–OH–(H 2 O) 2 intracluster proton-transfer reaction. These systems thus provide particularly clear examples where the network shape controls the potential energy landscape that governs water network-mediated, intracluster proton transfer. The CH stretching bands of the CH 3 NO 2 – ·(H 2 O) n=1–6 clusters also exhibit strong solvatochromic shifts, but in this case, they smoothly blue-shift with increasing hydration with no discontinuity at n = 6. Furthermore, this behavior is analyzed in the context of the solute-ion polarizability response and partial charge transfer to the water networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Exploring Allosteric Pathways of a V-Type Enzyme with Dynamical Perturbation Networks

Elucidation of the allosteric pathways in proteins is a computational challenge that strongly benefits from combination of atomistic molecular dynamics (MD) simulations and coarse-grained analysis of the complex dynamical network of chemical interactions based on graph theory. Here, we introduce and assess the performances of the dynamical perturbation network analysis of allosteric pathways in a prototypical V-type allosteric enzyme. Dynamical atomic contacts obtained from MD simulations are used to weight the allosteric protein graph, which involves an extended network of contacts perturbed by the effector binding in the allosteric site. The outcome showed good agreement with previously reported theoretical and experimental extended studies and it provided recognition of new potential allosteric spots that can be exploited in future mutagenesis experiments. Lastly, overall, the dynamical perturbation network analysis proved to be a powerful computational tool, complementary to other network-based approaches that can assist the full exploitation of allosteric phenomena for advances in protein engineering and rational drug design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗