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Computational model

Liliana Ironi, Ettore Lanzarone
Computational and mathematical models have significantly contributed to the rapid progress in the study of gene regulatory networks (GRN), but researchers still lack a reliable model-based framework for computer-aided analysis and design. Such tool should both reveal the relation between network structure and dynamics and find parameter values and/or constraints that enable the simulated dynamics to reproduce specific behaviors. This paper addresses these issues and proposes a computational framework that facilitates network analysis or design...
June 19, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Guoxian Yu, Guangyuan Fu, Jun Wang, Yingwen Zhao
A remaining key challenge of modern biology is annotating the functional roles of proteins. Various computational models have been proposed for this challenge. Most of them assume the annotations of annotated proteins are complete. But in fact, many of them are incomplete. We proposed a method called NewGOA to predict new Gene Ontology (GO) annotations for incompletely annotated proteins and for completely un-annotated ones. NewGOA employs a hybrid graph, composed of two types of nodes (proteins and GO terms), to encode interactions between proteins, hierarchical relationships between terms and available annotations of proteins...
June 15, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Reuben A Farrugia, Christine Guillemot
Most face super-resolution methods assume that low- and high-resolution manifolds have similar local geometrical structure, hence learn local models on the low-resolution manifold (e.g. sparse or locally linear embedding models), which are then applied on the high- resolution manifold. However, the low-resolution manifold is distorted by the one-to-many relationship between low- and high- resolution patches. This paper presents the Linear Model of Coupled Sparse Support (LM-CSS) method which learns linear models based on the local geometrical structure on the high-resolution manifold rather than on the low-resolution manifold...
June 19, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Kyong Hwan Jin, Michael T McCann, Emmanuel Froustey, Michael Unser
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise nonlinearity) when the normal operator ( H*H where H* is the adjoint of the forward imaging operator, H ) of the forward model is a convolution...
June 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Tingting Mu, Yannis John Goulermas, Sophia Ananiadou
A typical objective of data visualization is to generate low-dimensional plots that maximally convey the information within the data. The visualization output should help the user to not only identify the local neighborhood structure of individual samples, but also obtain a global view of the relative positioning and separation between cohorts. Here, we propose a very novel visualization framework designed to satisfy these needs. By incorporating additional cohort positioning and discriminative constraints into local neighbor preservation models through the use of computed cohort prototypes, effective control over the arrangements and proximities of data cohorts can be obtained...
June 15, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Yoann Blache, Mickael Begon
Little information is available in the existing literature regarding the influence of the scapular kinematic estimate method on musculoskeletal analysis. This study aimed to assess the propagation of errors due to the method used for scapular kinematics reconstruction in the workflow of musculoskeletal modeling (joint kinematics, joint torques, muscle force and joint reaction force) in shoulder and upper-limb movements. Two participants performed functional (arm elevation and rotation), daily life (eating and reaching pants pockets) and sports movements (a simulated throwing maneuver)...
June 15, 2017: IEEE Transactions on Bio-medical Engineering
Krzesimir Ciura, Mariusz Belka, Piotr Kawczak, Tomasz Bączek, Michał J Markuszewski, Joanna Nowakowska
The objective of this paper is to build QSRR/QSAR model for predicting the blood-brain barrier (BBB) permeability. The obtained models are based on salting-out thin layer chromatography (SOTLC) constants and calculated molecular descriptors. Among chromatographic methods SOTLC was chosen, since the mobile phases are free of organic solvent. As consequences, there are less toxic, and have lower environmental impact compared to classical reserved phases liquid chromatography (RPLC). During the study three stationary phase silica gel, cellulose plates and neutral aluminum oxide were examined...
June 3, 2017: Journal of Pharmaceutical and Biomedical Analysis
Hans Kainz, Christopher P Carty, Sheanna Maine, Henry P J Walsh, David G Lloyd, Luca Modenese
Joint kinematics can be calculated by Direct Kinematics (DK), which is used in most clinical gait laboratories, or Inverse Kinematics (IK), which is mainly used for musculoskeletal research. In both approaches, joint centre locations are required to compute joint angles. The hip joint centre (HJC) in DK models can be estimated using predictive or functional methods, while in IK models can be obtained by scaling generic models. The aim of the current study was to systematically investigate the impact of HJC location errors on lower limb joint kinematics of a clinical population using DK and IK approaches...
June 4, 2017: Gait & Posture
Catherine R von Reyn, Aljoscha Nern, W Ryan Williamson, Patrick Breads, Ming Wu, Shigehiro Namiki, Gwyneth M Card
Animals rely on dedicated sensory circuits to extract and encode environmental features. How individual neurons integrate and translate these features into behavioral responses remains a major question. Here, we identify a visual projection neuron type that conveys predator approach information to the Drosophila giant fiber (GF) escape circuit. Genetic removal of this input during looming stimuli reveals that it encodes angular expansion velocity, whereas other input cell type(s) encode angular size. Motor program selection and timing emerge from linear integration of these two features within the GF...
June 21, 2017: Neuron
Alizée P Lehoux, Pamela Faure, François Lafolie, Stéphane Rodts, Denis Courtier-Murias, Philippe Coussot, Eric Michel
Colloidal particles can act as vectors of adsorbed pollutants in the subsurface, or be themselves pollutants. They can reach the aquifer and impair groundwater quality. The mechanisms of colloid transport and deposition are often studied in columns filled with saturated porous media. Time-lapse profiles of colloid concentration inside the columns have occasionally been derived from magnetic resonance imaging (MRI) data recorded in transport experiments. These profiles are valuable, in addition to particle breakthrough curves (BTCs), for testing and improving colloid transport models...
June 14, 2017: Water Research
José Pedro Cerón-Carrasco, José Ruiz, Consuelo Vicente, Concepcion de Haro, Delia Bautista, Jose Zuniga, Alberto Requena
In this work, we use DFT-based methods to simulate the chemical structures, optical properties and interaction with DNA of a recently synthesized chelated CˆN 9-aminoacridine arene Ru (II) anticancer agent and two new closely related Rh(III) and Ir(III) complexes using DFT-based methods. Four chemical models and a number of theoretical approaches, which representatively include the PBE0, B97D, ωB97X, ωB97X-D, M06, and M06-L density functionals and the LANL2DZ, def2-SVP, def2- TZVP basis sets, are tested...
June 22, 2017: Journal of Chemical Theory and Computation
Petar I Petrov, Stefano Mandija, Iris E C Sommer, Cornelis A T van den Berg, Sebastiaan F W Neggers
BACKGROUND: Despite TMS wide adoption, its spatial and temporal patterns of neuronal effects are not well understood. Although progress has been made in predicting induced currents in the brain using realistic finite element models (FEM), there is little consensus on how a magnetic field of a typical TMS coil should be modeled. Empirical validation of such models is limited and subject to several limitations. METHODS: We evaluate and empirically validate models of a figure-of-eight TMS coil that are commonly used in published modeling studies, of increasing complexity: simple circular coil model; coil with in-plane spiral winding turns; and finally one with stacked spiral winding turns...
2017: PloS One
Baoshan Xu, Hua Li, John M Perry, Vijay Pratap Singh, Jay Unruh, Zulin Yu, Musinu Zakari, William McDowell, Linheng Li, Jennifer L Gerton
Ribosomal DNA is one of the most variable regions in the human genome with respect to copy number. Despite the importance of rDNA for cellular function, we know virtually nothing about what governs its copy number, stability, and sequence in the mammalian genome due to challenges associated with mapping and analysis. We applied computational and droplet digital PCR approaches to measure rDNA copy number in normal and cancer states in human and mouse genomes. We find that copy number and sequence can change in cancer genomes...
June 2017: PLoS Genetics
HaDi MaBouDi, Hideaki Shimazaki, Martin Giurfa, Lars Chittka
The honeybee olfactory system is a well-established model for understanding functional mechanisms of learning and memory. Olfactory stimuli are first processed in the antennal lobe, and then transferred to the mushroom body and lateral horn through dual pathways termed medial and lateral antennal lobe tracts (m-ALT and l-ALT). Recent studies reported that honeybees can perform elemental learning by associating an odour with a reward signal even after lesions in m-ALT or blocking the mushroom bodies. To test the hypothesis that the lateral pathway (l-ALT) is sufficient for elemental learning, we modelled local computation within glomeruli in antennal lobes with axons of projection neurons connecting to a decision neuron (LHN) in the lateral horn...
June 2017: PLoS Computational Biology
Rasmus Magnusson, Guido Pio Mariotti, Mattias Köpsén, William Lövfors, Danuta R Gawel, Rebecka Jörnsten, Jörg Linde, Torbjörn Nordling, Elin Nyman, Sylvie Schulze, Colm E Nestor, Huan Zhang, Gunnar Cedersund, Mikael Benson, Andreas Tjärnberg, Mika Gustafsson
Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets...
June 22, 2017: PLoS Computational Biology
Jeremy Schofield
An adaptive procedure is introduced to construct smooth analytical profiles of the free energy along a reaction coordinate using sampled data from multiple biased simulations. The procedure is based upon identifying problematic regions encountered in maximum likelihood estimators of the profile where there are statistically relevant discrepancies between the empirical and parameterized cumulative distribution functions and preferentially improving the construction of the parametric profile in these regions...
June 22, 2017: Journal of Physical Chemistry. B
Giovanni Barcaro, Susanna Monti, Luca Sementa, Vincenzo Carravetta
A novel computational approach, based on classical reactive molecular dynamics simulations (RMD) and quantum chemistry (QC) global energy optimizations, is proposed for modelling large Si nanoparticles. The force field parameters, which can describe bond breaking and formation, are derived by reproducing energetic and structural properties of a set of Si clusters increasing in size. These reference models are obtained through a new protocol based on a joint high temperature RMD/low temperature Basin Hopping QC search...
June 22, 2017: Journal of Chemical Theory and Computation
Anthony C Schramm, Glen M Hocky, Gregory A Voth, Laurent Blanchoin, Jean-Louis Martiel, Enrique M De La Cruz
Computational and structural studies have been indispensable in investigating the molecular origins of actin filament mechanical properties and modulation by the regulatory severing protein cofilin. All-atom molecular dynamics simulations of cofilactin filament structures determined by electron cryomicroscopy reveal how cofilin enhances the bending and twisting compliance of actin filaments. Continuum mechanics models suggest that buckled cofilactin filaments localize elastic energy at boundaries between bare and cofilin-decorated segments because of their nonuniform elasticity, thereby accelerating filament severing...
June 20, 2017: Biophysical Journal
Mee Y Shelley, Myvizhi Esai Selvan, Jun Zhao, Volodymyr Babin, Chenyi Liao, Jianing Li, John Clarence Shelley
We introduce a new mixed resolution, all-atom/coarse-grained approach (AACG) for modeling peptides in aqueous solution and apply it to characterizing the aggregation of melittin. All of the atoms in peptidic components are represented while a single site is used for each water molecule. With the full flexibility of the peptide retained, our AACG method achieves speedups by a factor of 3-4 for CPU time reduction and another factor of roughly 7 for diffusion. An Ewald treatment permits the inclusion of long-range electrostatic interactions...
June 21, 2017: Journal of Chemical Theory and Computation
Rafael S Oliveira, Bernardo M Rocha, Denise Burgarelli, Wagner Meira, Christakis Constantinides, Rodrigo Weber Dos Santos
The use of computer models as a tool for the study and understanding of the complex phenomena of cardiac electrophysiology has attained increased importance nowadays. At the same time, the increased complexity of the biophysical processes translates into complex computational and mathematical models. In order to speed up cardiac simulations and to allow more precise and realistic uses, two different techniques have been traditionally exploited: parallel computing and sophisticated numerical methods. In this work, we combine a modern parallel computing technique based on multicore and graphics processing units (GPUs), and a sophisticated numerical method based on a new space-time adaptive algorithm...
June 21, 2017: International Journal for Numerical Methods in Biomedical Engineering
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