Journal articles on the topic 'Systems biology, density functional theory, computational modeling'
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1
Koch, Robert J., Guangfang Li, Shubham Pandey, Simon R. Phillpot, Hui Wang, and Scott T. Misture. "Complex modeling for the quantification of nanoscale disorder using genetic algorithms, density functional theory and line-profile analysis." Journal of Applied Crystallography 53, no. 4 (July 30, 2020): 1087–100. http://dx.doi.org/10.1107/s1600576720008225.
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A new, computationally efficient, complex modeling approach is presented for the quantification of the local and average atomic structure, nanostructure and microstructure of an Au0.25Cu0.75 alloy. High-resolution X-ray powder diffraction and whole pattern fitting show that the sample is phase pure, with isotropic lattice strain and a distribution of equiaxed crystallites of mean size 144 (11) nm, where each crystallite has on average four twin boundaries and an average of three deformation faults per four crystallites. Both small- and large-box model optimizations were used to extract local and long-range information from the pair distribution function. The large-box, 640 000-atom-ensemble optimization approach applied herein relies on differential evolution optimization and shows that the alloy has chemical short-range ordering, with correlation parameters of −0.26 (2) and 0.36 (8) in the first and second correlation shells, respectively. Locally, there is a 1.45 (8)% tetragonal distortion which on average results in a cubic atomic structure. The isotropic lattice strain is a result of atom-pair-dependent bond lengths, following the trend d Au—Au > d Au—Cu > d Cu—Cu, highlighted by density functional theory calculations. This approach is generalizable and should be extensible to other disordered systems, allowing for quantification of localized structure deviations.
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Arora, Yashika, Pushpinder Walia, Mitsuhiro Hayashibe, Makii Muthalib, Shubhajit Roy Chowdhury, Stephane Perrey, and Anirban Dutta. "Grey-box modeling and hypothesis testing of functional near-infrared spectroscopy-based cerebrovascular reactivity to anodal high-definition tDCS in healthy humans." PLOS Computational Biology 17, no. 10 (October 6, 2021): e1009386. http://dx.doi.org/10.1371/journal.pcbi.1009386.
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Transcranial direct current stimulation (tDCS) has been shown to evoke hemodynamics response; however, the mechanisms have not been investigated systematically using systems biology approaches. Our study presents a grey-box linear model that was developed from a physiologically detailed multi-compartmental neurovascular unit model consisting of the vascular smooth muscle, perivascular space, synaptic space, and astrocyte glial cell. Then, model linearization was performed on the physiologically detailed nonlinear model to find appropriate complexity (Akaike information criterion) to fit functional near-infrared spectroscopy (fNIRS) based measure of blood volume changes, called cerebrovascular reactivity (CVR), to high-definition (HD) tDCS. The grey-box linear model was applied on the fNIRS-based CVR during the first 150 seconds of anodal HD-tDCS in eleven healthy humans. The grey-box linear models for each of the four nested pathways starting from tDCS scalp current density that perturbed synaptic potassium released from active neurons for Pathway 1, astrocytic transmembrane current for Pathway 2, perivascular potassium concentration for Pathway 3, and voltage-gated ion channel current on the smooth muscle cell for Pathway 4 were fitted to the total hemoglobin concentration (tHb) changes from optodes in the vicinity of 4x1 HD-tDCS electrodes as well as on the contralateral sensorimotor cortex. We found that the tDCS perturbation Pathway 3 presented the least mean square error (MSE, median <2.5%) and the lowest Akaike information criterion (AIC, median -1.726) from the individual grey-box linear model fitting at the targeted-region. Then, minimal realization transfer function with reduced-order approximations of the grey-box model pathways was fitted to the ensemble average tHb time series. Again, Pathway 3 with nine poles and two zeros (all free parameters), provided the best Goodness of Fit of 0.0078 for Chi-Square difference test of nested pathways. Therefore, our study provided a systems biology approach to investigate the initial transient hemodynamic response to tDCS based on fNIRS tHb data. Future studies need to investigate the steady-state responses, including steady-state oscillations found to be driven by calcium dynamics, where transcranial alternating current stimulation may provide frequency-dependent physiological entrainment for system identification. We postulate that such a mechanistic understanding from system identification of the hemodynamics response to transcranial electrical stimulation can facilitate adequate delivery of the current density to the neurovascular tissue under simultaneous portable imaging in various cerebrovascular diseases.
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Pandey, Anoop Kumar, Vijay Singh, and Apoorva Dwivedi. "Quantum chemical calculations of a novel Specie – Boron Nano Bucket (B16) and the interaction of its complex (B15-Li) with drug Resorcinol." Journal of Computational Methods in Sciences and Engineering 20, no. 3 (September 30, 2020): 1017–28. http://dx.doi.org/10.3233/jcm-200032.
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At Nano-scale level, innovative biomedical techniques are developed in advanced drug delivery systems and targeted Nano-therapy. Ultrathin needles provide a low invasive and highly selective means for molecular delivery and cell manipulation. This article studies the geometry and the stability of Boron Nano-Bucket (B16 Cluster of Bucket Shape) and B15-Li complex by using computational modeling methods. The equilibrium geometry of Boron Nano-Bucket and BNB-Li complex in the ground state have been determined and analyzed by Density functional theory (DFT) employing 6-311 G (d, p) as the basis set. The frontier orbital HOMO-LUMO gap, Chemical Softness, Chemical Hardness have also been calculated to understand its complete Chemical Properties. In this study, we have also performed BNB-Li complex interaction with drug Resorcinol. The binding character interactive species have been determined by NBO and AIM analysis. From these studies, we can say that BNB and BNB-Li complex may also potentially able to stabilize ions around their structure like Carbon Nano Niddle (CNN) in future. The polar characteristics of CNN and their ability to carry ionic species, Li doped Boron Nano-Bucket might be suitable to act as drug carrier through nonpolar biologic media.
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Cruz-Cabeza, Aurora J., and Frank H. Allen. "Conformation and geometry of cyclopropane rings having π-acceptor substituents: a theoretical and database study." Acta Crystallographica Section B Structural Science 67, no. 1 (December 18, 2010): 94–102. http://dx.doi.org/10.1107/s0108768110049517.
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The 3e′ orbitals of cyclopropane have the correct symmetry to interact with low-lying unoccupied orbitals of π-acceptor substituents and maximum overlap occurs when the two orbital systems are parallel, i.e. when the π-acceptor bisects the ring in projection down the substituent bond. Since the cyclopropyl group is a common component of active pharmaceutical and agrochemical ingredients, it is important that these strong conjugative interactions are well modelled by computational techniques, and clearly represented in experimental crystal structures. Here we show that torsion angle distributions derived from crystal structure data in the Cambridge Structural Database are in excellent correspondence with torsional energy profiles computed using density functional theory (DFT) for a range of substituents: —COOR, —CONR 2, —NO2, vinyl and phenyl. We also show that crystal structure information is invaluable in modelling conformations of compounds that contain multiply substituted rings, where steric interactions require some substituents to adopt energetically disfavoured conformations. Further, conjugative interactions with π-acceptors lead to significant asymmetry in the cyclopropane ring bond lengths and again the experimental and computational results are in excellent agreement. Such asymmetry effects are additive, and this explains bond-length variations in cyclopropane rings bearing two or more π-acceptor substituents.
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Kersen, David E. Chen, Gaia Tavoni, and Vijay Balasubramanian. "Connectivity and dynamics in the olfactory bulb." PLOS Computational Biology 18, no. 2 (February 7, 2022): e1009856. http://dx.doi.org/10.1371/journal.pcbi.1009856.
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Dendrodendritic interactions between excitatory mitral cells and inhibitory granule cells in the olfactory bulb create a dense interaction network, reorganizing sensory representations of odors and, consequently, perception. Large-scale computational models are needed for revealing how the collective behavior of this network emerges from its global architecture. We propose an approach where we summarize anatomical information through dendritic geometry and density distributions which we use to calculate the connection probability between mitral and granule cells, while capturing activity patterns of each cell type in the neural dynamical systems theory of Izhikevich. In this way, we generate an efficient, anatomically and physiologically realistic large-scale model of the olfactory bulb network. Our model reproduces known connectivity between sister vs. non-sister mitral cells; measured patterns of lateral inhibition; and theta, beta, and gamma oscillations. The model in turn predicts testable relationships between network structure and several functional properties, including lateral inhibition, odor pattern decorrelation, and LFP oscillation frequency. We use the model to explore the influence of cortex on the olfactory bulb, demonstrating possible mechanisms by which cortical feedback to mitral cells or granule cells can influence bulbar activity, as well as how neurogenesis can improve bulbar decorrelation without requiring cell death. Our methodology provides a tractable tool for other researchers.
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Kostrobij, P. P., B. M. Markovych, and I. A. Ryzha. "Semi-infinite metallic system: QST versus DFT." Mathematical Modeling and Computing 9, no. 1 (2022): 178–85. http://dx.doi.org/10.23939/mmc2022.01.178.
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Modeling and investigation of thermodynamic characteristics of spatially-finite metallic systems is an essential task of modern nanophysics. We show that the widely used DFT (density functional theory) is less efficient than the QST (quantum-statistical theory) approach.
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Söderlind, Per, G. Kotliar, K. Haule, P. M. Oppeneer, and D. Guillaumont. "Computational modeling of actinide materials and complexes." MRS Bulletin 35, no. 11 (November 2010): 883–88. http://dx.doi.org/10.1557/mrs2010.715.
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In spite of being rare, actinide elements provide the building blocks for many fascinating condensed-matter systems, both from an experimental and theoretical perspective. Experimental observations of actinide materials are difficult because of rarity, toxicity, radioactivity, and even safety and security. Theory, on the other hand, has its own challenges. Complex crystal and electronic structures are often encountered in actinide materials, as well as pronounced electron correlation effects. Consequently, theoretical modeling of actinide materials and their 5f electronic states is very difficult. Here, we review recent theoretical efforts to describe and sometimes predict the behavior of actinide materials and complexes, such as phase stability including density functional theory (DFT), DFT in conjunction with an additional Coulomb repulsion U (DFT+U), and DFT in combination with dynamical mean-field theory (DFT+DMFT).
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KOLB, BRIAN, and T. THONHAUSER. "MOLECULAR BIOLOGY AT THE QUANTUM LEVEL: CAN MODERN DENSITY FUNCTIONAL THEORY FORGE THE PATH?" Nano LIFE 02, no. 02 (June 2012): 1230006. http://dx.doi.org/10.1142/s1793984412300063.
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Recent years have seen vast improvements in the ability of rigorous quantum-mechanical methods to treat systems of interest to molecular biology. In this review article, we survey common computational methods used to study such large, weakly bound systems, starting from classical simulations and reaching to quantum chemistry and density functional theory. We sketch their underlying frameworks and investigate their strengths and weaknesses when applied to potentially large biomolecules. In particular, density functional theory — a framework that can treat thousands of atoms on firm theoretical ground — can now accurately describe systems dominated by weak van der Waals interactions. This newfound ability has rekindled interest in using this tried-and-true approach to investigate biological systems of real importance. In this review, we focus on some new methods within the density functional theory that allow for accurate inclusion of the weak interactions that dominate binding in biological macromolecules. Recent work utilizing these methods to study biologically relevant systems will be highlighted, and a vision for the future of density functional theory within molecular biology will be discussed.
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Palos, Etienne, Saswata Dasgupta, Eleftherios Lambros, and Francesco Paesani. "Data-driven many-body potentials from density functional theory for aqueous phase chemistry." Chemical Physics Reviews 4, no. 1 (March 2023): 011301. http://dx.doi.org/10.1063/5.0129613.
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Density functional theory (DFT) has been applied to modeling molecular interactions in water for over three decades. The ubiquity of water in chemical and biological processes demands a unified understanding of its physics, from the single molecule to the thermodynamic limit and everything in between. Recent advances in the development of data-driven and machine-learning potentials have accelerated simulation of water and aqueous systems with DFT accuracy. However, anomalous properties of water in the condensed phase, where a rigorous treatment of both local and non-local many-body (MB) interactions is in order, are often unsatisfactory or partially missing in DFT models of water. In this review, we discuss the modeling of water and aqueous systems based on DFT and provide a comprehensive description of a general theoretical/computational framework for the development of data-driven many-body potentials from DFT reference data. This framework, coined MB-DFT, readily enables efficient many-body molecular dynamics (MD) simulations of small molecules, in both gas and condensed phases, while preserving the accuracy of the underlying DFT model. Theoretical considerations are emphasized, including the role that the delocalization error plays in MB-DFT potentials of water and the possibility to elevate DFT and MB-DFT to near-chemical-accuracy through a density-corrected formalism. The development of the MB-DFT framework is described in detail, along with its application in MB-MD simulations and recent extension to the modeling of reactive processes in solution within a quantum mechanics/MB molecular mechanics (QM/MB-MM) scheme, using water as a prototypical solvent. Finally, we identify open challenges and discuss future directions for MB-DFT and QM/MB-MM simulations in condensed phases.
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Plass, Winfried. "Vanadium haloperoxidases as supramolecular hosts: Synthetic and computational models." Pure and Applied Chemistry 81, no. 7 (June 30, 2009): 1229–39. http://dx.doi.org/10.1351/pac-con-08-10-19.
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In the active-site cavity of vanadium haloperoxidases vanadate as the prosthetic group is solely fixed by one covalent bond to a histidine residue and embedded in a supramolecular environment of extensive hydrogen bonds. Structural aspects of relevant vanadium complexes with supramolecular interactions, including assemblies with chiral hosts, are presented. The importance of hydrogen-bonding relays is presented together with relevant examples. The reactivity of related functional mimics containing vanadium and molybdenum toward the oxidation of thioethers is described. Computational modeling based on density functional theory (DFT) is used for the investigation of model systems. The resulting implications for structure and function of vanadium haloperoxidases, including their substrate and cofactor specificity, are discussed.
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Champagne, Aurélie, Samuel Dechamps, Simon M. M. Dubois, Aurélien Lherbier, Viet-Hung Nguyen, and Jean-Christophe Charlier. "Computational Atomistic Modeling in Carbon Flatland and Other 2D Nanomaterials." Applied Sciences 10, no. 5 (March 3, 2020): 1724. http://dx.doi.org/10.3390/app10051724.
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As in many countries, the rise of nanosciences in Belgium has been triggered in the eighties in the one hand, by the development of scanning tunneling and atomic force microscopes offering an unprecedented possibility to visualize and manipulate the atoms, and in the other hand, by the synthesis of nano-objects in particular carbon nanostructures such as fullerene and nanotubes. Concomitantly, the increasing calculating power and the emergence of computing facilities together with the development of DFT-based ab initio softwares have brought to nanosciences field powerful simulation tools to analyse and predict properties of nano-objects. Starting with 0D and 1D nanostructures, the floor is now occupied by the 2D materials with graphene being the bow of this 2D ship. In this review article, some specific examples of 2D systems has been chosen to illustrate how not only density functional theory (DFT) but also tight-binding (TB) techniques can be daily used to investigate theoretically the electronic, phononic, magnetic, and transport properties of these atomically thin layered materials.
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Badu, Shyam, Roderick Melnik, and Sundeep Singh. "Analysis of Photosynthetic Systems and Their Applications with Mathematical and Computational Models." Applied Sciences 10, no. 19 (September 29, 2020): 6821. http://dx.doi.org/10.3390/app10196821.
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In biological and life science applications, photosynthesis is an important process that involves the absorption and transformation of sunlight into chemical energy. During the photosynthesis process, the light photons are captured by the green chlorophyll pigments in their photosynthetic antennae and further funneled to the reaction center. One of the most important light harvesting complexes that are highly important in the study of photosynthesis is the membrane-attached Fenna–Matthews–Olson (FMO) complex found in the green sulfur bacteria. In this review, we discuss the mathematical formulations and computational modeling of some of the light harvesting complexes including FMO. The most recent research developments in the photosynthetic light harvesting complexes are thoroughly discussed. The theoretical background related to the spectral density, quantum coherence and density functional theory has been elaborated. Furthermore, details about the transfer and excitation of energy in different sites of the FMO complex along with other vital photosynthetic light harvesting complexes have also been provided. Finally, we conclude this review by providing the current and potential applications in environmental science, energy, health and medicine, where such mathematical and computational studies of the photosynthesis and the light harvesting complexes can be readily integrated.
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Borrego-Sánchez, Ana, Mahmoud Awad, and Claro Sainz-Díaz. "Molecular Modeling of Adsorption of 5-Aminosalicylic Acid in the Halloysite Nanotube." Minerals 8, no. 2 (February 11, 2018): 61. http://dx.doi.org/10.3390/min8020061.
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Halloysite nanotubes are becoming interesting materials for drug delivery. The knowledge of surface interactions is important for optimizing this application. The aim of this work is to perform a computational study of the interaction between 5-aminosalicylic acid (5-ASA) drug and halloysite nanotubes for the development of modified drug delivery systems. The optimization of this nanotube and the adsorption of different conformers of the 5-ASA drug on the internal surface of halloysite in the presence and absence of water were performed using quantum mechanical calculations by using Density Functional Theory (DFT) and methods based on atomistic force fields for molecular modeling, respectively.
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Donà, Lorenzo, Jan Gerit Brandenburg, and Bartolomeo Civalleri. "Metal–organic frameworks properties from hybrid density functional approximations." Journal of Chemical Physics 156, no. 9 (March 7, 2022): 094706. http://dx.doi.org/10.1063/5.0080359.
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The chemical versatility and modular nature of Metal–Organic Frameworks (MOFs) make them unique hybrid inorganic–organic materials for several important applications. From a computational point of view, ab initio modeling of MOFs is a challenging and demanding task, in particular, when the system reaches the size of gigantic MOFs as MIL-100 and MIL-101 (where MIL stands for Materials Institute Lavoisier) with several thousand atoms in the unit cell. Here, we show how such complex systems can be successfully tackled by a recently proposed class of composite electronic structure methods revised for solid-state calculations. These methods rely on HF/density functional theory hybrid functionals (i.e., PBEsol0 and HSEsol) combined with a double-zeta quality basis set. They are augmented with semi-classical corrections to take into account dispersive interactions (D3 scheme) and the basis set superposition error (gCP). The resulting methodologies, dubbed “sol-3c,” are cost-effective yet reach the hybrid functional accuracy. Here, sol-3c methods are effectively applied to predict the structural, vibrational, electronic, and adsorption properties of some of the most common MOFs. Calculations are feasible even on very large MOFs containing more than 2500 atoms in the unit cell as MIL-100 and MIL-101 with reasonable computing resources. We propose to use our composite methods for the routine in silico screening of MOFs targeting properties beyond plain structural features.
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Huang, Liang-Feng, John R. Scully, and James M. Rondinelli. "Modeling Corrosion with First-Principles Electrochemical Phase Diagrams." Annual Review of Materials Research 49, no. 1 (July 2019): 53–77. http://dx.doi.org/10.1146/annurev-matsci-070218-010105.
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Understanding and predicting materials corrosion under electrochemical environments are of increasing importance to both established and developing industries and technologies, including construction, marine materials, geology, and biomedicine, as well as to energy generation, storage, and conversion. Owing to recent progress in the accuracy and capability of density functional theory (DFT) calculations to describe the thermodynamic stability of materials, this powerful computational tool can be used both to describe materials corrosion and to design materials with the desired corrosion resistance by using first-principles electrochemical phase diagrams. We review the progress in simulating electrochemical phase diagrams of bulk solids, surface systems, and point defects in materials using DFT methods as well as the application of these ab initio phase diagrams in realistic environments. We conclude by summarizing the remaining challenges in the thermodynamic modeling of materials corrosion and promising future research directions.
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Gusarov, Sergey, Yuri Yu Dmitriev, Stanislav R. Stoyanov, and Andriy Kovalenko. "Koopmans’ multiconfigurational self-consistent field (MCSCF) Fukui functions and MCSCF perturbation theory." Canadian Journal of Chemistry 91, no. 9 (September 2013): 886–93. http://dx.doi.org/10.1139/cjc-2012-0526.
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Prediction of chemical reactivity has become one of the highest priority tasks of computational chemistry since the development of the methods of modeling electronic structure. Despite the general simplicity of the physical concept of reactivity and the rapid development of modern density functional theory (DFT) methods, this task remains state-of-the-art for systems with wavefunctions that have a multiconfigurational character. In such cases, for the accurate description of reactivity one needs to use multiconfigurational approaches that are much heavier computationally then ordinary single-determinant DFT methods. Moreover, the complexity of the calculation of reactivity is increased by the necessity to calculate ionic and transition states. These computational challenges can be addressed by employing the concepts of Koopmans’ theorem and its extension to a multiconfigurational case. We present a simplified methodology for the calculation of Fukui functions, based on Koopmans’ approximation for multiconfigurational Green’s functions developed in our previous works. Also, an extension of this methodology based on perturbation theory has been developed to improve accuracy.
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GUN'KO, VLADIMIR M. "MODELING OF INTERFACIAL BEHAVIOR OF WATER AND ORGANICS." Journal of Theoretical and Computational Chemistry 12, no. 07 (November 2013): 1350059. http://dx.doi.org/10.1142/s0219633613500594.
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Modeling of water structure at a surface of different adsorbents, as well as an influence of dissolved compounds or co-adsorbates on bound water, is of importance to understand the temperature dependence of the characteristics of bound water, especially at T < 273 K, in comparison with bulk water. 1 H NMR spectra giving useful information on the water structure can be obtained using different ways such as experimental measurements, direct ab initio and density functional theory (DFT) calculations or estimation using semiempirical calculations and appropriate calibration functions. Here, application of the last approach is analyzed with respect to a variety of relatively large hydrated systems. Despite the simplicity of this approach, it gives quantitative characterization of structural features of interfacial water and effects of different co-adsorbates and adsorbent surfaces on bound water.
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Flores-Holguín, Norma, Joaquín Ortega-Castro, Juan Frau, and Daniel Glossman-Mitnik. "Conceptual DFT-Based Computational Peptidology, Pharmacokinetics Study and ADMET Report of the Veraguamides A–G Family of Marine Natural Drugs." Marine Drugs 20, no. 2 (January 24, 2022): 97. http://dx.doi.org/10.3390/md20020097.
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As a continuation of our research on the chemical reactivity, pharmacokinetics and ADMET properties of cyclopeptides of marine origin with potential therapeutic abilities, in this work our already presented integrated molecular modeling protocol has been used for the study of the chemical reactivity and bioactivity properties of the Veraguamides A–G family of marine natural drugs. This protocol results from the estimation of the conceptual density functional theory (CDFT) chemical reactivity descriptors together with several chemoinformatics tools commonly considered within the process of development of new therapeutic drugs. CP-CDFT is a branch of computational chemistry and molecular modeling dedicated to the study of peptides, and it is a protocol that allows the estimation with great accuracy of the CDFT-based reactivity descriptors and the associated physical and chemical properties, which can aid in determining the ability of the studied peptides to behave as potential useful drugs. Moreover, the superiority of the MN12SX density functional over other long-range corrected density functionals for the prediction of chemical and physical properties in the presence of water as the solvent is clearly demonstrated. The research was supplemented with an investigation of the bioactivity of the molecular systems and their ADMET (absorption, distribution, metabolism, excretion, and toxicity) parameters, as is customary in medicinal chemistry. Some instances of the CDFT-based chemical reactivity descriptors’ capacity to predict the pKas of peptides as well as their potential as AGE inhibitors are also shown.
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Drosou, Maria, Christiana A. Mitsopoulou, Maylis Orio, and Dimitrios A. Pantazis. "EPR Spectroscopy of Cu(II) Complexes: Prediction of g-Tensors Using Double-Hybrid Density Functional Theory." Magnetochemistry 8, no. 4 (March 23, 2022): 36. http://dx.doi.org/10.3390/magnetochemistry8040036.
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Computational electron paramagnetic resonance (EPR) spectroscopy is an important field of applied quantum chemistry that contributes greatly to connecting spectroscopic observations with the fundamental description of electronic structure for open-shell molecules. However, not all EPR parameters can be predicted accurately and reliably for all chemical systems. Among transition metal ions, Cu(II) centers in inorganic chemistry and biology, and their associated EPR properties such as hyperfine coupling and g-tensors, pose exceptional difficulties for all levels of quantum chemistry. In the present work, we approach the problem of Cu(II) g-tensor calculations using double-hybrid density functional theory (DHDFT). Using a reference set of 18 structurally and spectroscopically characterized Cu(II) complexes, we evaluate a wide range of modern double-hybrid density functionals (DHDFs) that have not been applied previously to this problem. Our results suggest that the current generation of DHDFs consistently and systematically outperform other computational approaches. The B2GP-PLYP and PBE0-DH functionals are singled out as the best DHDFs on average for the prediction of Cu(II) g-tensors. The performance of the different functionals is discussed and suggestions are made for practical applications and future methodological developments.
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Saleev, Vladimir, and Alexandra Shipilova. "Ab initio study of optical and bulk properties of cesium lead halide perovskite solid solutions." Modern Physics Letters B 33, no. 31 (November 10, 2019): 1950386. http://dx.doi.org/10.1142/s021798491950386x.
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The first-principles calculations of band gaps and bulk moduli of cesium lead halide perovskite solid solutions, [Formula: see text] and [Formula: see text], are performed at the level of general gradient approximation of the density functional theory. We use supercell approach for computational modeling of disordered systems, which gives a description of the properties of the structure baasing on the average over a set of multiple configurations, namely distributions of different species over a given set of atomic positions. The calculations were performed with the CRYSTAL14 program package. The dependence of the band gap and bulk modulus on the content [Formula: see text] are investigated over the whole range [Formula: see text].
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Duan, Na, Zisen Gao, Baichun Hu, Dandan Ge, Wei Li, Tong Ye, Xiaohui Geng, and Xiaodong Li. "Computational insights into the binding pattern of mitochondrial calcium uniporter inhibitor through homology modeling, molecular dynamics simulation, binding free energy prediction and density functional theory calculation." Journal of Biomolecular Structure and Dynamics 38, no. 17 (November 27, 2019): 5095–107. http://dx.doi.org/10.1080/07391102.2019.1695674.
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Qian, Zhao, Guanzhong Jiang, Yingying Ren, Xi Nie, and Rajeev Ahuja. "Atomistic Modeling of Various Doped Mg2NiH4 as Conversion Electrode Materials for Lithium Storage." Crystals 9, no. 5 (May 17, 2019): 254. http://dx.doi.org/10.3390/cryst9050254.
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In this work, we have compared the potential applications of nine different elements doped Mg2NiH4 as conversion-type electrode materials in Li-ion batteries by means of state-of-the-art Density functional theory calculations. The electrochemical properties, such as specific capacity, volume change and average voltage, as well as the atomic and electronic structures of different doped systems have been investigated. The Na doping can improve the electrochemical capacity of the pristine material. Si and Ti doping can reduce the band gap and benefit the electronic conductivity of electrode materials. All of the nine doping elements can help to reduce the average voltage of negative electrodes and lead to reasonable volume changes. According to the computational screening, the Na, Si and Ti doping elements are thought to be promising to enhance the comprehensive properties of pure material. This theoretical study is proposed to encourage and expedite the development of metal-hydrides based lithium-storage materials.
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Markovic, Milica, Shimon Ben-Shabat, Shahar Keinan, Aaron Aponick, Ellen M. Zimmermann, and Arik Dahan. "Molecular Modeling-Guided Design of Phospholipid-Based Prodrugs." International Journal of Molecular Sciences 20, no. 9 (May 5, 2019): 2210. http://dx.doi.org/10.3390/ijms20092210.
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The lipidic prodrug approach is an emerging field for improving a number of biopharmaceutical and drug delivery aspects. Owing to their structure and nature, phospholipid (PL)-based prodrugs may join endogenous lipid processing pathways, and hence significantly improve the pharmacokinetics and/or bioavailability of the drug. Additional advantages of this approach include drug targeting by enzyme-triggered drug release, blood–brain barrier permeability, lymphatic targeting, overcoming drug resistance, or enabling appropriate formulation. The PL-prodrug design includes various structural modalities-different conjugation strategies and/or the use of linkers between the PL and the drug moiety, which considerably influence the prodrug characteristics and the consequent effects. In this article, we describe how molecular modeling can guide the structural design of PL-based prodrugs. Computational simulations can predict the extent of phospholipase A2 (PLA2)-mediated activation, and facilitate prodrug development. Several computational methods have been used to facilitate the design of the pro-drugs, which will be reviewed here, including molecular docking, the free energy perturbation method, molecular dynamics simulations, and free density functional theory. Altogether, the studies described in this article indicate that computational simulation-guided PL-based prodrug molecular design correlates well with the experimental results, allowing for more mechanistic and less empirical development. In the future, the use of molecular modeling techniques to predict the activity of PL-prodrugs should be used earlier in the development process.
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Marino, Tiziana, Maria Grazia Fortino, Nino Russo, Marirosa Toscano, and Marta Erminia Alberto. "Computational Mechanistic Insights on the NO Oxidation Reaction Catalyzed by Non-Heme Biomimetic Cr-N-Tetramethylated Cyclam Complexes." International Journal of Molecular Sciences 20, no. 16 (August 14, 2019): 3955. http://dx.doi.org/10.3390/ijms20163955.
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The conversion reaction of NO to NO3− ion catalyzed by the end-on [Cr(III)(n-TMC)(O2)(Cl)]+ superoxo and side-on [Cr(IV)(n-TMC)(O2)(Cl)]+ peroxo non-heme complexes (n = 12, 13, 14 and 15), which are biomimetic systems of nitric oxide dioxygenases (NODs), has been explored using a computational protocol in the framework of density functional theory. Results show that the potential energy profiles for the studied reactions lie above the reagent energies, regardless of the used catalyst. Both the O-O bond breaking in the biomimetics and the NO3− ion formation require low energy barriers suggesting an efficient catalytic power of the studied systems. The rate-determining step depends on ligand size.
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Ching, Wai-Yim, Puja Adhikari, Bahaa Jawad, and Rudolf Podgornik. "Towards Quantum-Chemical Level Calculations of SARS-CoV-2 Spike Protein Variants of Concern by First Principles Density Functional Theory." Biomedicines 11, no. 2 (February 10, 2023): 517. http://dx.doi.org/10.3390/biomedicines11020517.
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The spike protein (S-protein) is a crucial part of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with its many domains responsible for binding, fusion, and host cell entry. In this review we use the density functional theory (DFT) calculations to analyze the atomic-scale interactions and investigate the consequences of mutations in S-protein domains. We specifically describe the key amino acids and functions of each domain, which are essential for structural stability as well as recognition and fusion processes with the host cell; in addition, we speculate on how mutations affect these properties. Such unprecedented large-scale ab initio calculations, with up to 5000 atoms in the system, are based on the novel concept of amino acid–amino acid-bond pair unit (AABPU) that allows for an alternative description of proteins, providing valuable information on partial charge, interatomic bonding and hydrogen bond (HB) formation. In general, our results show that the S-protein mutations for different variants foster an increased positive partial charge, alter the interatomic interactions, and disrupt the HB networks. We conclude by outlining a roadmap for future computational research of biomolecular virus-related systems.
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Labus, Karolina, Lukasz Radosinski, and Piotr Kotowski. "Functional Properties of Two-Component Hydrogel Systems Based on Gelatin and Polyvinyl Alcohol—Experimental Studies Supported by Computational Analysis." International Journal of Molecular Sciences 22, no. 18 (September 14, 2021): 9909. http://dx.doi.org/10.3390/ijms22189909.
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The presented research is focused on an investigation of the effect of the addition of polyvinyl alcohol (PVA) to a gelatin-based hydrogel on the functional properties of the resulting material. The main purpose was to experimentally determine and compare the properties of hydrogels differing from the content of PVA in the blend. Subsequently, the utility of these matrices for the production of an immobilized invertase preparation with improved operational stability was examined. We also propose a useful computational tool to predict the properties of the final material depending on the proportions of both components in order to design the feature range of the hydrogel blend desired for a strictly specified immobilization system (of enzyme/carrier type). Based on experimental research, it was found that an increase in the PVA content in gelatin hydrogels contributes to obtaining materials with a visibly higher packaging density, degree of swelling, and water absorption capacity. In the case of hydrolytic degradation and compressive strength, the opposite tendency was observed. The functionality studies of gelatin and gelatin/PVA hydrogels for enzyme immobilization indicate the very promising potential of invertase entrapped in a gelatin/PVA hydrogel matrix as a stable biocatalyst for industrial use. The molecular modeling analysis performed in this work provides qualitative information about the tendencies of the macroscopic parameters observed with the increase in the PVA and insight into the chemical nature of these dependencies.
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Petrone, Alessio, Fulvio Perrella, Federico Coppola, Luigi Crisci, Greta Donati, Paola Cimino, and Nadia Rega. "Ultrafast photo-induced processes in complex environments: The role of accuracy in excited-state energy potentials and initial conditions." Chemical Physics Reviews 3, no. 2 (June 2022): 021307. http://dx.doi.org/10.1063/5.0085512.
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Light induces non-equilibrium time evolving molecular phenomena. The computational modeling of photo-induced processes in large systems, embedded in complex environments (i.e., solutions, proteins, materials), demands for a quantum and statistical mechanic treatment to achieve the required accuracy in the description of both the excited-state energy potentials and the choice of the initial conditions for dynamical simulations. On the other hand, the theoretical investigation on the atomistic scale of times and sizes of the ultrafast photo-induced reactivity and non-equilibrium relaxation dynamics right upon excitation requests tailored computational protocols. These methods often exploit hierarchic computation schemes, where a large part of the degrees of freedom are required to be treated explicitly to achieve the right accuracy. Additionally, part of the explicit system needs to be treated at ab initio level, where density functional theory, using hybrid functionals, represents a good compromise between accuracy and computational cost, when proton transfers, non-covalent interactions, and hydrogen bond dynamics play important roles. Thus, the modeling strategies presented in this review stress the importance of hierarchical quantum/molecular mechanics with effective non-periodic boundary conditions and efficient phase-sampling schemes to achieve chemical accuracy in ultrafast time-resolved spectroscopy and photo-induced phenomena. These approaches can allow explicit and accurate treatment of molecule/environment interactions, including also the electrostatic and dispersion forces of the bulk. At the same time, the specificities of the different case studies of photo-induced phenomena in solutions and biological environments are highlighted and discussed, with special attention to the computational and modeling challenges.
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Woo, Mino, Lubow Maier, Steffen Tischer, Olaf Deutschmann, and Martin Wörner. "A Qualitative Numerical Study on Catalytic Hydrogenation of Nitrobenzene in Gas-Liquid Taylor Flow with Detailed Reaction Mechanism." Fluids 5, no. 4 (December 8, 2020): 234. http://dx.doi.org/10.3390/fluids5040234.
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While the number of computational studies considering two-phase flows in microfluidic systems with or without mass transfer is increasing, numerical studies incorporating chemical reactions are still rare. This study aims to simulate the catalytic hydrogenation of nitrobenzene in gas-liquid Taylor flow by combining interface-resolving numerical simulations of two-phase flow and mass transfer by a volume-of-fluid method with detailed modeling of the heterogeneous chemical reaction by software package DETCHEMTM. Practically relevant physical properties are utilized for hydrodynamic and mass transfer simulations in combination with a preliminary reaction mechanism based on density functional theory. Simulations of mass transfer are conducted using a predetermined velocity field and Taylor bubble shape. At the beginning of the simulation when liquid nitrobenzene is not saturated by hydrogen, axial profiles of surface species concentrations and reaction rates show local variations. As hydrogen dissolves in nitrobenzene, the concentration profiles of surface species at the wall become uniform, eventually reaching an equilibrium state. Neglecting the local variation in a short initial period will allow further simplification of modeling surface reactions within a Taylor flow.
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Lee, Yueh-Lin, Yuhua Duan, Dane Morgan, Dan Sorescu, Harry Abernathy, and Gregory A. Hackett. "Density Functional Theory Modeling of Cation Diffusion in Bulk Lanthanum Manganite and Tetragonal Zirconia for Solid Oxide Fuel Cell Applications." ECS Meeting Abstracts MA2018-01, no. 32 (April 13, 2018): 1941. http://dx.doi.org/10.1149/ma2018-01/32/1941.
Full textAbstract:
Cation diffusion in perovskites such as La1-xSrxMnO3±δ (LSM) and fluorites such as Yttria Stabilized Zirconia (YSZ) plays a key role in controlling performance and long-term stability of solid oxide fuel cells (SOFCs) and of the corresponding electrode/electrolyte interfaces. For point defects based cation migration mechanisms, discrepancies between experimental studies and atomistic modeling results are generally observed when trying to identify the apparent activation energies of the cation diffusivities. In particular, computational modeling of the simple point defect migration generally overestimates the apparent activation energies by several eVs [1-3] relative to experimental results. Despite the existence of few proposed defect cluster pathways in the SOFC literature, the corresponding energetics based on atomistic level modeling is quite limited. As a result, outstanding questions related to the influence of the local bonding environment of the material upon the activation energies of cation diffusion and upon the energetics of the defect cluster carriers are still open. In this presentation, density functional theory (DFT) calculations of the cation diffusion involving mechanisms beyond the isolated point defect migration mechanism are discussed in connection to SOFC applications. The barriers of the cations, cation vacancy clusters, and cation impurities migration as well as their corresponding saddle point configurations in bulk LaMnO3±δ (LMO) and tetragonal ZrO2 are investigated. For cation vacancy migration in both LaMnO3 and tetragonal ZrO2 systems, it was revealed that significant reduction in the barriers of 1~3 eV takes place when there exists an additional nearest neighbor vacancy or a nearest neighbor vacancy pair bound to the original cation vacancy transport carrier (i.e., partially or fully bound Schottky defects). Such a significant reduction in the migration barriers of isolated cation vacancy migration due to the presence of an additional nearest neighbor vacancy or a vacancy pair invokes the need to examine other possible defect cluster diffusion mechanisms that exhibit attractive or weakly repulsive interactions between point defects. For example, our recent studies [1,3] suggest that the presence of a nearest neighbor B-site cation vacancy in bulk LMO decreases the electrostatic repulsion and steric constraints to the migrating A-site cations in the transition state configurations, leading to a more active A-site cation diffusion with apparent activation energies in good agreement with the experimental measurements. An analogous scenario is also observed for the Lanthanide impurity migration (via a cation vacancy related mechanism) in tetragonal ZrO2, where the migration barriers of partial and full Schottky bound defects are about 1.5 and 2.4 eV lower than those of simple cation vacancy migration (about 3 eV). By considering the attractive interactions of -1.0 and -1.4 eV for the partial (VZr-VO) and full (VO-VZr-VO) bound Schottky defects along with the separated Schottky defect formation energies of 4~5 eV (in a 288-atom ZrO2 supercells), it follows that cation impurity diffusion via partially and fully bonded Schottky defects could be more active than the cation vacancy migration of the separated Schottky defects in tetragonal ZrO2. Finally, the trends in the migration barriers of the defect cluster mechanisms among several types of cation impurities relevant for SOFC applications in LMO and tetragonal ZrO2 will be also discussed. References: Y.-L. Lee, Y. Duan, D. Morgan, D. C. Sorescu, H. Abernathy, and G. Hackett, “Density-Functional-Theory Modeling of Cation Diffusion in Bulk La1−xSrxMnO3±δ (x=0.0–0.25) for Solid-Oxide Fuel-Cell Cathodes”, Physical Review Applied, 8, 044001 (2017). B. Puchala, Y.-L. Lee, and D. Morgan, A-Site Diffusion in La1−xSrxMnO3: Ab Initio and Kinetic Monte Carlo Calculations, Journal of The Electrochemical Society, 160 (8) F877-F882 (2013). Y.-L. Lee, Y. Duan, D. Morgan, D. C. Sorescu, H. Abernathy, and G. Hackett, “Density Functional Theory Modeling of A-site Cation Diffusion in Bulk LaMnO3±δ for Solid Oxide Fuel Cell Cathodes”, ECS Transactions, 78(1)2797-2806 (2017).
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Biaggne, Austin, Lawrence Spear, German Barcenas, Maia Ketteridge, Young C. Kim, Joseph S. Melinger, William B. Knowlton, Bernard Yurke, and Lan Li. "Data-Driven and Multiscale Modeling of DNA-Templated Dye Aggregates." Molecules 27, no. 11 (May 27, 2022): 3456. http://dx.doi.org/10.3390/molecules27113456.
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Dye aggregates are of interest for excitonic applications, including biomedical imaging, organic photovoltaics, and quantum information systems. Dyes with large transition dipole moments (μ) are necessary to optimize coupling within dye aggregates. Extinction coefficients (ε) can be used to determine the μ of dyes, and so dyes with a large ε (>150,000 M−1cm−1) should be engineered or identified. However, dye properties leading to a large ε are not fully understood, and low-throughput methods of dye screening, such as experimental measurements or density functional theory (DFT) calculations, can be time-consuming. In order to screen large datasets of molecules for desirable properties (i.e., large ε and μ), a computational workflow was established using machine learning (ML), DFT, time-dependent (TD-) DFT, and molecular dynamics (MD). ML models were developed through training and validation on a dataset of 8802 dyes using structural features. A Classifier was developed with an accuracy of 97% and a Regressor was constructed with an R2 of above 0.9, comparing between experiment and ML prediction. Using the Regressor, the ε values of over 18,000 dyes were predicted. The top 100 dyes were further screened using DFT and TD-DFT to identify 15 dyes with a μ relative to a reference dye, pentamethine indocyanine dye Cy5. Two benchmark MD simulations were performed on Cy5 and Cy5.5 dimers, and it was found that MD could accurately capture experimental results. The results of this study exhibit that our computational workflow for identifying dyes with a large μ for excitonic applications is effective and can be used as a tool to develop new dyes for excitonic applications.
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Cindrić, Maja, Irena Sović, Marija Mioč, Lucija Hok, Ida Boček, Petra Roškarić, Kristina Butković, et al. "Experimental and Computational Study of the Antioxidative Potential of Novel Nitro and Amino Substituted Benzimidazole/Benzothiazole-2-Carboxamides with Antiproliferative Activity." Antioxidants 8, no. 10 (October 12, 2019): 477. http://dx.doi.org/10.3390/antiox8100477.
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We present the synthesis of a range of benzimidazole/benzothiazole-2-carboxamides with a variable number of methoxy and hydroxy groups, substituted with nitro, amino, or amino protonated moieties, which were evaluated for their antiproliferative activity in vitro and the antioxidant capacity. Antiproliferative features were tested on three human cancer cells, while the antioxidative activity was measured using 1,1-diphenyl-picrylhydrazyl (DPPH) free radical scavenging and ferric reducing antioxidant power (FRAP) assays. Trimethoxy substituted benzimidazole-2-carboxamide 8 showed the most promising antiproliferative activity (IC50 = 0.6–2.0 µM), while trihydroxy substituted benzothiazole-2-carboxamide 29 was identified as the most promising antioxidant, being significantly more potent than the reference butylated hydroxytoluene BHT in both assays. Moreover, the latter also displays antioxidative activity in tumor cells. The measured antioxidative capacities were rationalized through density functional theory (DFT) calculations, showing that 29 owes its activity to the formation of two [O•∙∙∙H–O] hydrogen bonds in the formed radical. Systems 8 and 29 were both chosen as lead compounds for further optimization of the benzazole-2-carboxamide scaffold in order to develop more efficient antioxidants and/or systems with the antiproliferative activity.
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Lin, Yin-Pai, Sergei Piskunov, Laima Trinkler, Mitch Ming-Chi Chou, and Liuwen Chang. "Electronic and Optical Properties of Rocksalt Mg1−xZnxO and Wurtzite Zn1−xMgxO with Varied Concentrations of Magnesium and Zinc." Materials 15, no. 21 (November 1, 2022): 7689. http://dx.doi.org/10.3390/ma15217689.
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The structural, electronic and optical properties of rocksalt Mg1−xZnxO and wurtzite Zn1−xMgxO with the concentration of Zn and Mg varying from 0.125 to 0.875 were investigated using density functional theory (DFT), DFT+U, linear response theory and the Bethe–Salpeter equation. According to the experimental band gap for varied concentrations of magnesium and zinc, modeling the supercell was utilized for the varied concentrations of Mg/Zn/O compounds in order to not only avoid constructing the complicated interface systems that are observed in the experiments but also take into account the excitonic effects that usually require huge computational resources. From the calculated density of states, the Zn states are highly related to the edge of the conduction band minimum and responsible for the width of bandgap. In addition, the contribution of Zn–d states is below expectations as they are located away from the VBM. As for the optical response, an increase in Zn concentration would cause a red-shifted spectrum, on the whole. In contrast, the higher concentration of Mg also triggers the blue-shift of the optical spectrum. In addition, anisotropic properties could be found in the spectrum with consideration of the excitonic effects, whereas there is no apparent difference in optical response based on linear response theory. In addition, the optical features of this work reflect the characteristic peaks of the literature around the absorption onset.
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Abramyan, Ara, Zhiwei Liu, and Vojislava Pophristic. "An ab-initio study of pyrrole and imidazole arylamides." Journal of the Serbian Chemical Society 78, no. 11 (2013): 1789–95. http://dx.doi.org/10.2298/jsc130929104a.
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Arylamide foldamers have been shown to have a number of biological and medicinal applications. For example, a class of pyrrole-imidazole polyamide foldamers is capable of binding specific DNA sequences and preventing development of various gene disorders, most importantly cancer. Molecular dynamics (MD) simulations can provide crucial details in understanding the atomic level events related to foldamer/DNA binding. An important first step in the accurate simulation of these foldamer/DNA systems is the reparametrization of force field parameters for torsion around the aryl-amide bonds. Here we highlight our Density Functional Theory (DFT) potential energy profiles and derived force field parameters for four aryl-amide bond types for the pyrrole and imidazole building blocks extensively used in foldamer design for the DNA-binding polyamides. These results contribute to developing of computational tools for an appropriate molecular modeling of pyrrole-imidazole polyamide/DNA binding, and provide an insight into the chemical factors that influence the flexibility of the pyrrole-imidazole polyamides, and their binding to DNA.
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De Boeck, Jolan, Jan Rombouts, and Lendert Gelens. "A modular approach for modeling the cell cycle based on functional response curves." PLOS Computational Biology 17, no. 8 (August 11, 2021): e1009008. http://dx.doi.org/10.1371/journal.pcbi.1009008.
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Modeling biochemical reactions by means of differential equations often results in systems with a large number of variables and parameters. As this might complicate the interpretation and generalization of the obtained results, it is often desirable to reduce the complexity of the model. One way to accomplish this is by replacing the detailed reaction mechanisms of certain modules in the model by a mathematical expression that qualitatively describes the dynamical behavior of these modules. Such an approach has been widely adopted for ultrasensitive responses, for which underlying reaction mechanisms are often replaced by a single Hill function. Also time delays are usually accounted for by using an explicit delay in delay differential equations. In contrast, however, S-shaped response curves, which by definition have multiple output values for certain input values and are often encountered in bistable systems, are not easily modeled in such an explicit way. Here, we extend the classical Hill function into a mathematical expression that can be used to describe both ultrasensitive and S-shaped responses. We show how three ubiquitous modules (ultrasensitive responses, S-shaped responses and time delays) can be combined in different configurations and explore the dynamics of these systems. As an example, we apply our strategy to set up a model of the cell cycle consisting of multiple bistable switches, which can incorporate events such as DNA damage and coupling to the circadian clock in a phenomenological way.
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Oprea, Corneliu, and Mihai Gîrțu. "Structure and Electronic Properties of TiO2 Nanoclusters and Dye–Nanocluster Systems Appropriate to Model Hybrid Photovoltaic or Photocatalytic Applications." Nanomaterials 9, no. 3 (March 4, 2019): 357. http://dx.doi.org/10.3390/nano9030357.
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We report the results of a computational study of TiO2 nanoclusters of various sizes as well as of complex systems with various molecules adsorbed onto the clusters to set the ground for the modeling of charge transfer processes in hybrid organic–inorganic photovoltaics or photocatalytic degradation of pollutants. Despite the large number of existing computational studies of TiO2 clusters and in spite of the higher computing power of the typical available hardware, allowing for calculations of larger systems, there are still studies that use cluster sizes that are too small and not appropriate to address particular problems or certain complex systems relevant in photovoltaic or photocatalytic applications. By means of density functional theory (DFT) calculations, we attempt to find acceptable minimal sizes of the TinO2n+2H4 (n = 14, 24, 34, 44, 54) nanoclusters in correlation with the size of the adsorbed molecule and the rigidity of the backbone of the molecule to model systems and interface processes that occur in hybrid photovoltaics and photocatalysis. We illustrate various adsorption cases with a small rigid molecule based on coumarin, a larger rigid oligomethine cyanine dye with indol groups, and the penicillin V antibiotic having a flexible backbone. We find that the use of the n = 14 cluster to describe adsorption leads to significant distortions of both the cluster and the molecule and to unusual tridentate binding configurations not seen for larger clusters. Moreover, the significantly weaker bonding as well as the differences in the density of states and in the optical spectra suggest that the n = 14 cluster is a poor choice for simulating the materials used in the practical applications envisaged here. As the n = 24 cluster has provided mixed results, we argue that cluster sizes larger than or equal to n = 34 are necessary to provide the reliability required by photovoltaic and photocatalytic applications. Furthermore, the tendency to saturate the key quantities of interest when moving from n = 44 to n = 54 suggests that the largest cluster may bring little improvement at a significantly higher computational cost.
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Kawakubo, Hideko, Yusuke Matsui, Itaru Kushima, Norio Ozaki, and Teppei Shimamura. "A network of networks approach for modeling interconnected brain tissue-specific networks." Bioinformatics 35, no. 17 (January 15, 2019): 3092–101. http://dx.doi.org/10.1093/bioinformatics/btz032.
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Abstract Motivation Recent sequence-based analyses have identified a lot of gene variants that may contribute to neurogenetic disorders such as autism spectrum disorder and schizophrenia. Several state-of-the-art network-based analyses have been proposed for mechanical understanding of genetic variants in neurogenetic disorders. However, these methods were mainly designed for modeling and analyzing single networks that do not interact with or depend on other networks, and thus cannot capture the properties between interdependent systems in brain-specific tissues, circuits and regions which are connected each other and affect behavior and cognitive processes. Results We introduce a novel and efficient framework, called a ‘Network of Networks’ approach, to infer the interconnectivity structure between multiple networks where the response and the predictor variables are topological information matrices of given networks. We also propose Graph-Oriented SParsE Learning, a new sparse structural learning algorithm for network data to identify a subset of the topological information matrices of the predictors related to the response. We demonstrate on simulated data that propose Graph-Oriented SParsE Learning outperforms existing kernel-based algorithms in terms of F-measure. On real data from human brain region-specific functional networks associated with the autism risk genes, we show that the ‘Network of Networks’ model provides insights on the autism-associated interconnectivity structure between functional interaction networks and a comprehensive understanding of the genetic basis of autism across diverse regions of the brain. Availability and implementation Our software is available from https://github.com/infinite-point/GOSPEL. Supplementary information Supplementary data are available at Bioinformatics online.
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Cocchi, Caterina, and Holger-Dietrich Saßnick. "Ab Initio Quantum-Mechanical Predictions of Semiconducting Photocathode Materials." Micromachines 12, no. 9 (August 24, 2021): 1002. http://dx.doi.org/10.3390/mi12091002.
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Ab initio Quantum-Mechanical methods are well-established tools for material characterization and discovery in many technological areas. Recently, state-of-the-art approaches based on density-functional theory and many-body perturbation theory were successfully applied to semiconducting alkali antimonides and tellurides, which are currently employed as photocathodes in particle accelerator facilities. The results of these studies have unveiled the potential of ab initio methods to complement experimental and technical efforts for the development of new, more efficient materials for vacuum electron sources. Concomitantly, these findings have revealed the need for theory to go beyond the status quo in order to face the challenges of modeling such complex systems and their properties in operando conditions. In this review, we summarize recent progress in the application of ab initio many-body methods to investigate photocathode materials, analyzing the merits and the limitations of the standard approaches with respect to the confronted scientific questions. In particular, we emphasize the necessary trade-off between computational accuracy and feasibility that is intrinsic to these studies, and propose possible routes to optimize it. We finally discuss novel schemes for computationally-aided material discovery that are suitable for the development of ultra-bright electron sources toward the incoming era of artificial intelligence.
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Trentham-Dietz, Amy, Oguzhan Alagoz, Christina Chapman, Xuelin Huang, Jinani Jayasekera, Nicolien T. van Ravesteyn, Sandra J. Lee, et al. "Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts." PLOS Computational Biology 17, no. 6 (June 17, 2021): e1009020. http://dx.doi.org/10.1371/journal.pcbi.1009020.
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Since 2000, the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.
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Jia, Shanshan, Dajun Xing, Zhaofei Yu, and Jian K. Liu. "Dissecting cascade computational components in spiking neural networks." PLOS Computational Biology 17, no. 11 (November 29, 2021): e1009640. http://dx.doi.org/10.1371/journal.pcbi.1009640.
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Finding out the physical structure of neuronal circuits that governs neuronal responses is an important goal for brain research. With fast advances for large-scale recording techniques, identification of a neuronal circuit with multiple neurons and stages or layers becomes possible and highly demanding. Although methods for mapping the connection structure of circuits have been greatly developed in recent years, they are mostly limited to simple scenarios of a few neurons in a pairwise fashion; and dissecting dynamical circuits, particularly mapping out a complete functional circuit that converges to a single neuron, is still a challenging question. Here, we show that a recent method, termed spike-triggered non-negative matrix factorization (STNMF), can address these issues. By simulating different scenarios of spiking neural networks with various connections between neurons and stages, we demonstrate that STNMF is a persuasive method to dissect functional connections within a circuit. Using spiking activities recorded at neurons of the output layer, STNMF can obtain a complete circuit consisting of all cascade computational components of presynaptic neurons, as well as their spiking activities. For simulated simple and complex cells of the primary visual cortex, STNMF allows us to dissect the pathway of visual computation. Taken together, these results suggest that STNMF could provide a useful approach for investigating neuronal systems leveraging recorded functional neuronal activity.
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Czernek, Jiří, Jiří Brus, and Vladimíra Czerneková. "A Cost Effective Scheme for the Highly Accurate Description of Intermolecular Binding in Large Complexes." International Journal of Molecular Sciences 23, no. 24 (December 12, 2022): 15773. http://dx.doi.org/10.3390/ijms232415773.
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There has been a growing interest in quantitative predictions of the intermolecular binding energy of large complexes. One of the most important quantum chemical techniques capable of such predictions is the domain-based local pair natural orbital (DLPNO) scheme for the coupled cluster theory with singles, doubles, and iterative triples [CCSD(T)], whose results are extrapolated to the complete basis set (CBS) limit. Here, the DLPNO-based focal-point method is devised with the aim of obtaining CBS-extrapolated values that are very close to their canonical CCSD(T)/CBS counterparts, and thus may serve for routinely checking a performance of less expensive computational methods, for example, those based on the density-functional theory (DFT). The efficacy of this method is demonstrated for several sets of noncovalent complexes with varying amounts of the electrostatics, induction, and dispersion contributions to binding (as revealed by accurate DFT-based symmetry-adapted perturbation theory (SAPT) calculations). It is shown that when applied to dimeric models of poly(3-hydroxybutyrate) chains in its two polymorphic forms, the DLPNO-CCSD(T) and DFT-SAPT computational schemes agree to within about 2 kJ/mol of an absolute value of the interaction energy. These computational schemes thus should be useful for a reliable description of factors leading to the enthalpic stabilization of extended systems.
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Buta, Maria Cristina, Ana Maria Toader, Bogdan Frecus, Corneliu I. Oprea, Fanica Cimpoesu, and Gabriela Ionita. "Molecular and Supramolecular Interactions in Systems with Nitroxide-Based Radicals." International Journal of Molecular Sciences 20, no. 19 (September 24, 2019): 4733. http://dx.doi.org/10.3390/ijms20194733.
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Nitroxide-based radicals, having the advantage of firm chemical stability, are usable as probes in the detection of nanoscale details in the chemical environment of various multi-component systems, based on subtle variations in their electron paramagnetic resonance spectra. We propose a systematic walk through the vast area of problems and inquires that are implied by the rationalization of solvent effects on the spectral parameters, by first-principle methods of structural chemistry. Our approach consists of using state-of-the-art procedures, like Density Functional Theory (DFT), on properly designed systems, kept at the border of idealization and chemical realism. Thus, we investigate the case of real solvent molecules intervening in different configurations between two radical molecules, in comparison with radicals taken in vacuum or having the solvent that is treated by surrogate models, such as polarization continuum approximation. In this work, we selected the dichloromethane as solvent and the prototype radicals abbreviated TEMPO ((2,2,6,6-Tetramethylpiperidin-1-yl) oxyl). In another branch of the work, we check the interaction of radicals with large toroidal molecules, β-cyclodextrin, and cucurbit[6]uril, modeling the interaction energy profile at encapsulation. The drawn synoptic view offers valuable rationales for understanding spectroscopy and energetics of nitroxide radicals in various environments, which are specific to soft chemistry.
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Huang, Lei, David Brunell, Clifford Stephan, James Mancuso, Xiaohui Yu, Bin He, Timothy C. Thompson, et al. "Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction." Bioinformatics 35, no. 19 (February 15, 2019): 3709–17. http://dx.doi.org/10.1093/bioinformatics/btz109.
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Abstract Motivation Drug combinations that simultaneously suppress multiple cancer driver signaling pathways increase therapeutic options and may reduce drug resistance. We have developed a computational systems biology tool, DrugComboExplorer, to identify driver signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded in vast amounts of available pharmacogenomics and omics data. Results This tool generates driver signaling networks by processing DNA sequencing, gene copy number, DNA methylation and RNA-seq data from individual cancer patients using an integrated pipeline of algorithms, including bootstrap aggregating-based Markov random field, weighted co-expression network analysis and supervised regulatory network learning. It uses a systems pharmacology approach to infer the combinatorial drug efficacies and synergy mechanisms through drug functional module-induced regulation of target expression analysis. Application of our tool on diffuse large B-cell lymphoma and prostate cancer demonstrated how synergistic drug combinations can be discovered to inhibit multiple driver signaling pathways. Compared with existing computational approaches, DrugComboExplorer had higher prediction accuracy based on in vitro experimental validation and probability concordance index. These results demonstrate that our network-based drug efficacy screening approach can reliably prioritize synergistic drug combinations for cancer and uncover potential mechanisms of drug synergy, warranting further studies in individual cancer patients to derive personalized treatment plans. Availability and implementation DrugComboExplorer is available at https://github.com/Roosevelt-PKU/drugcombinationprediction. Supplementary information Supplementary data are available at Bioinformatics online.
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Martina Perez, Simon, Heba Sailem, and Ruth E. Baker. "Efficient Bayesian inference for mechanistic modelling with high-throughput data." PLOS Computational Biology 18, no. 6 (June 21, 2022): e1010191. http://dx.doi.org/10.1371/journal.pcbi.1010191.
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Bayesian methods are routinely used to combine experimental data with detailed mathematical models to obtain insights into physical phenomena. However, the computational cost of Bayesian computation with detailed models has been a notorious problem. Moreover, while high-throughput data presents opportunities to calibrate sophisticated models, comparing large amounts of data with model simulations quickly becomes computationally prohibitive. Inspired by the method of Stochastic Gradient Descent, we propose a minibatch approach to approximate Bayesian computation. Through a case study of a high-throughput imaging scratch assay experiment, we show that reliable inference can be performed at a fraction of the computational cost of a traditional Bayesian inference scheme. By applying a detailed mathematical model of single cell motility, proliferation and death to a data set of 118 gene knockdowns, we characterise functional subgroups of gene knockdowns, each displaying its own typical combination of local cell density-dependent and -independent motility and proliferation patterns. By comparing these patterns to experimental measurements of cell counts and wound closure, we find that density-dependent interactions play a crucial role in the process of wound healing.
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Hrivnák, Tomáš, Miroslav Medveď, Wojciech Bartkowiak, and Robert Zaleśny. "Hyperpolarizabilities of Push–Pull Chromophores in Solution: Interplay between Electronic and Vibrational Contributions." Molecules 27, no. 24 (December 9, 2022): 8738. http://dx.doi.org/10.3390/molecules27248738.
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Contemporary design of new organic non-linear optical (NLO) materials relies to a large extent on the understanding of molecular and electronic structure–property relationships revealed during the years by available computational approaches. The progress in theory—hand-in-hand with experiment—has enabled us to identify and analyze various physical aspects affecting the NLO responses, such as the environmental effects, molecular vibrations, frequency dispersion, and system dynamics. Although it is nowadays possible to reliably address these effects separately, the studies analyzing their mutual interplay are still very limited. Here, we employ density functional theory (DFT) methods in combination with an implicit solvent model to examine the solvent effects on the electronic and harmonic as well as anharmonic vibrational contributions to the static first hyperpolarizability of a series of push–pull α,ω-diphenylpolyene oligomers, which were experimentally shown to exhibit notable second-order NLO responses. We demonstrate that the magnitudes of both vibrational and electronic contributions being comparable in the gas phase significantly increase in solvents, and the enhancement can be, in some cases, as large as three- or even four-fold. The electrical and mechanical anharmonic contributions are not negligible but cancel each other out to a large extent. The computed dynamic solute NLO properties of the studied systems are shown to be in a fair agreement with those derived from experimentally measured electric-field-induced second-harmonic generation (EFISHG) signals. Our results substantiate the necessity to consider concomitantly both solvation and vibrational effects in modeling static NLO properties of solvated systems.
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D’Aloia, Alessia, Federica Arrigoni, Renata Tisi, Alessandro Palmioli, Michela Ceriani, Valentina Artusa, Cristina Airoldi, Giuseppe Zampella, Barbara Costa, and Laura Cipolla. "Synthesis, Molecular Modeling and Biological Evaluation of Metabolically Stable Analogues of the Endogenous Fatty Acid Amide Palmitoylethanolamide." International Journal of Molecular Sciences 21, no. 23 (November 28, 2020): 9074. http://dx.doi.org/10.3390/ijms21239074.
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Palmitoylethanolamide (PEA) belongs to the class of N-acylethanolamine and is an endogenous lipid potentially useful in a wide range of therapeutic areas; products containing PEA are licensed for use in humans as a nutraceutical, a food supplement, or food for medical purposes for its analgesic and anti-inflammatory properties demonstrating efficacy and tolerability. However, the exogenously administered PEA is rapidly inactivated; in this process, fatty acid amide hydrolase (FAAH) plays a key role both in hepatic metabolism and in intracellular degradation. So, the aim of the present study was the design and synthesis of PEA analogues that are more resistant to FAAH-mediated hydrolysis. A small library of PEA analogues was designed and tested by molecular docking and density functional theory calculations to find the more stable analogue. The computational investigation identified RePEA as the best candidate in terms of both synthetic accessibility and metabolic stability to FAAH-mediated hydrolysis. The selected compound was synthesized and assayed ex vivo to monitor FAAH-mediated hydrolysis and to confirm its anti-inflammatory properties. 1H-NMR spectroscopy performed on membrane samples containing FAAH in integral membrane protein demonstrated that RePEA is not processed by FAAH, in contrast with PEA. Moreover, RePEA retains PEA’s ability to inhibit LPS-induced cytokine release in both murine N9 microglial cells and human PMA-THP-1 cells.
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Duggins, Peter, and Chris Eliasmith. "Constructing functional models from biophysically-detailed neurons." PLOS Computational Biology 18, no. 9 (September 8, 2022): e1010461. http://dx.doi.org/10.1371/journal.pcbi.1010461.
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Improving biological plausibility and functional capacity are two important goals for brain models that connect low-level neural details to high-level behavioral phenomena. We develop a method called “oracle-supervised Neural Engineering Framework” (osNEF) to train biologically-detailed spiking neural networks that realize a variety of cognitively-relevant dynamical systems. Specifically, we train networks to perform computations that are commonly found in cognitive systems (communication, multiplication, harmonic oscillation, and gated working memory) using four distinct neuron models (leaky-integrate-and-fire neurons, Izhikevich neurons, 4-dimensional nonlinear point neurons, and 4-compartment, 6-ion-channel layer-V pyramidal cell reconstructions) connected with various synaptic models (current-based synapses, conductance-based synapses, and voltage-gated synapses). We show that osNEF networks exhibit the target dynamics by accounting for nonlinearities present within the neuron models: performance is comparable across all four systems and all four neuron models, with variance proportional to task and neuron model complexity. We also apply osNEF to build a model of working memory that performs a delayed response task using a combination of pyramidal cells and inhibitory interneurons connected with NMDA and GABA synapses. The baseline performance and forgetting rate of the model are consistent with animal data from delayed match-to-sample tasks (DMTST): we observe a baseline performance of 95% and exponential forgetting with time constant τ = 8.5s, while a recent meta-analysis of DMTST performance across species observed baseline performances of 58 − 99% and exponential forgetting with time constants of τ = 2.4 − 71s. These results demonstrate that osNEF can train functional brain models using biologically-detailed components and open new avenues for investigating the relationship between biophysical mechanisms and functional capabilities.
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Huang, Yuanhao, Bingjiang Wang, and Jie Liu. "NucleoMap: A computational tool for identifying nucleosomes in ultra-high resolution contact maps." PLOS Computational Biology 18, no. 7 (July 14, 2022): e1010265. http://dx.doi.org/10.1371/journal.pcbi.1010265.
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Although poorly positioned nucleosomes are ubiquitous in the eukaryotic genome, they are difficult to identify with existing nucleosome identification methods. Recently available enhanced high-throughput chromatin conformation capture techniques such as Micro-C, DNase Hi-C, and Hi-CO characterize nucleosome-level chromatin proximity, probing the positions of mono-nucleosomes and the spacing between nucleosome pairs at the same time, enabling nucleosome profiling in poorly positioned regions. Here we develop a novel computational approach, NucleoMap, to identify nucleosome positioning from ultra-high resolution chromatin contact maps. By integrating nucleosome read density, contact distances, and binding preferences, NucleoMap precisely locates nucleosomes in both prokaryotic and eukaryotic genomes and outperforms existing nucleosome identification methods in both precision and recall. We rigorously characterize genome-wide association in eukaryotes between the spatial organization of mono-nucleosomes and their corresponding histone modifications, protein binding activities, and higher-order chromatin functions. We also find evidence of two tetra-nucleosome folding structures in human embryonic stem cells and analyze their association with multiple structural and functional regions. Based on the identified nucleosomes, nucleosome contact maps are constructed, reflecting the inter-nucleosome distances and preserving the contact distance profiles in original contact maps.
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Anibal, James, Alexandre G. Day, Erol Bahadiroglu, Liam O’Neil, Long Phan, Alec Peltekian, Amir Erez, Mariana Kaplan, Grégoire Altan-Bonnet, and Pankaj Mehta. "HAL-X: Scalable hierarchical clustering for rapid and tunable single-cell analysis." PLOS Computational Biology 18, no. 10 (October 3, 2022): e1010349. http://dx.doi.org/10.1371/journal.pcbi.1010349.
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Data clustering plays a significant role in biomedical sciences, particularly in single-cell data analysis. Researchers use clustering algorithms to group individual cells into populations that can be evaluated across different levels of disease progression, drug response, and other clinical statuses. In many cases, multiple sets of clusters must be generated to assess varying levels of cluster specificity. For example, there are many subtypes of leukocytes (e.g. T cells), whose individual preponderance and phenotype must be assessed for statistical/functional significance. In this report, we introduce a novel hierarchical density clustering algorithm (HAL-x) that uses supervised linkage methods to build a cluster hierarchy on raw single-cell data. With this new approach, HAL-x can quickly predict multiple sets of labels for immense datasets, achieving a considerable improvement in computational efficiency on large datasets compared to existing methods. We also show that cell clusters generated by HAL-x yield near-perfect F1-scores when classifying different clinical statuses based on single-cell profiles. Our hierarchical density clustering algorithm achieves high accuracy in single cell classification in a scalable, tunable and rapid manner.
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Liu, Yi, Kenneth Barr, and John Reinitz. "Fully interpretable deep learning model of transcriptional control." Bioinformatics 36, Supplement_1 (July 1, 2020): i499—i507. http://dx.doi.org/10.1093/bioinformatics/btaa506.
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Abstract Motivation The universal expressibility assumption of Deep Neural Networks (DNNs) is the key motivation behind recent worksin the systems biology community to employDNNs to solve important problems in functional genomics and moleculargenetics. Typically, such investigations have taken a ‘black box’ approach in which the internal structure of themodel used is set purely by machine learning considerations with little consideration of representing the internalstructure of the biological system by the mathematical structure of the DNN. DNNs have not yet been applied to thedetailed modeling of transcriptional control in which mRNA production is controlled by the binding of specific transcriptionfactors to DNA, in part because such models are in part formulated in terms of specific chemical equationsthat appear different in form from those used in neural networks. Results In this paper, we give an example of a DNN whichcan model the detailed control of transcription in a precise and predictive manner. Its internal structure is fully interpretableand is faithful to underlying chemistry of transcription factor binding to DNA. We derive our DNN from asystems biology model that was not previously recognized as having a DNN structure. Although we apply our DNNto data from the early embryo of the fruit fly Drosophila, this system serves as a test bed for analysis of much larger datasets obtained by systems biology studies on a genomic scale. . Availability and implementation The implementation and data for the models used in this paper are in a zip file in the supplementary material. Supplementary information Supplementary data are available at Bioinformatics online.
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Haiman, Zachary B., Daniel C. Zielinski, Yuko Koike, James T. Yurkovich, and Bernhard O. Palsson. "MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics." PLOS Computational Biology 17, no. 1 (January 28, 2021): e1008208. http://dx.doi.org/10.1371/journal.pcbi.1008208.
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Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner: the Systems Biology Markup Language (SBML) simulation engine. Three examples are provided to demonstrate how to use MASSpy: (1) a validation of the MASSpy modeling tool through dynamic simulation of detailed mechanisms of enzyme regulation; (2) a feature demonstration using a workflow for generating ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify biological uncertainty, and (3) a case study in which MASSpy is utilized to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenges that arise in dynamic modeling of metabolic networks, both at small and large scales.