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IEEE International Conference on Bioinformatics and Biomedicine

作者： Dreycey Albin Dan Nasko R. A. Leo Elworth Jacob Lu Advait Balaji Christian Diaz Nidhi Shah Jeremy Selengut Chris Hulme-Lowe Pravin Muthu Gene Godbold Mikael Lindvall Madeline Diep Adam Porter Mihai Pop Krista Ternus Todd J. Treangen Program in Systems Synthetic and Physical Biology Rice University Center for Bioinformatics and Computational Biology University of Maryland Department of Computer Science Rice University Signature Science LLC Fraunhofer Center for Experimental Software Engineering Department of Computer Science University of Maryland

ISBN: (纸本)9781728118680

Rapid advancements in synthetic biology and nucleic acid synthesis, in particular concerns about its intentional or accidental misuse, call for more sophisticated screening tools to identify genes of interest within short sequence fragments. One major gap in predicting genes of concern is the inadequacy of current tools and ontologies to describe the specific biological processes of pathogenic proteins. The objective of this work is to design software that sensitively assigns taxonomic classifications, functional annotations, and biological processes of interest to short nucleotide sequences of unknown origin (50bp-1,000bp). The overarching goal is to perform sensitive characterization of short sequences and highlight specific pathogenic biological processes of interest (BPoIs). The SeqScreen software executes these tasks in analytical workflows with Nextflow and outputs results in a tab-delimited report. Local and global alignments differentiate hits to taxonomically-related sequences from similar but unrelated sequences, and an ensemble approach leverages multiple tools and databases to assign a variety of functional terms to each query sequence. Final biological process assessments are made from the predicted functional annotations, which leverage information in pre-existing databases, as well as new custom biocurations. Machine learning models predict each biological process of interest on large protein databases before incorporation into the SeqScreen framework to streamline computational efficiency, ensure reproducible results, allow for version control, and facilitate the review of the automated predictions by expert biocurators. The SeqScreen source code is available at .

关键词： Biological Process interest Base Sequence Nucleic acids Version control protein database Database synthetic biology ANNOTATIONS Leverage

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Mechanical regulation of actin network dynamics in migrating cells

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Journal of Biomechanical science and engineering 2010年第3期5卷 186-207页

作者： Okeyo, Kennedy Omondi Adachi, Taiji Hojo, Masaki Department of Mechanical Engineering and Science Kyoto University Yoshida-Honmachi Sakyo Kyoto 606-8501 Japan Computational Cell Biomechanics Team VCAD System Research Program RIKEN 2-1 Wako 351-0198 Japan

Cell migration is fundamental to various physiological processes, including metastasis, wound healing and tissue development. The complex processes involved in cell migration;polymerization, adhesion, and retraction, are mediated by highly orchestrated structure-function interactions that occur within the actin cytoskeletal structure. Thus understanding how migrating cells regulate the global dynamics of their cytoskeletal components, which result from rather localized protein-protein interactions, is fundamental to elucidating the mechanisms of cell motility. The objective of this review is to explore the mechanical regulation of actin network dynamics in migrating cells, and to discuss its regulatory role in cell migration. Specifically, we examine the various mechanical forces involved in cell migration, and how they couple with biomechanical factors to spatiotemporally regulate the dynamics of the actin cytoskeleton during cell motility. Two aspects of actin network dynamics are addressed, namely, network turnover by polymerization and depolymerization, and network flow resulting from actomyosin activity. We begin by highlighting the fundamental features of actin network dynamics in migrating cells. We then examine the coupling relationship between actin network flow and traction forces, as well as the mechanism underlying the regulation of traction forces by actin network flow. Finally, we integrate the various motility processes into a mechanical pathway in order to elucidate the importance of mechanical regulation of actin network dynamics to cell migration. © 2010 by JSME.

关键词： Cells

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COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

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Computers, Materials & Continua 2021年第7期68卷 391-407页

作者： Aqib Ali Wali Khan Mashwani Samreen Naeem Muhammad Irfan Uddin Wiyada Kumam Poom Kumam Hussam Alrabaiah Farrukh Jamal Christophe Chesneau Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Program in Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiThanyaburi12110Thailand Departments of Mathematics Faculty of ScienceCenter of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research LaboratoryRoom SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand Department of Medical Research China Medical University HospitalTaichung40402Taiwan College of Engineering Al Ain UniversityAl Ain64141United Arab Emirates Department of Mathematics Tafila Technical UniversityTafila66110Jordan Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan 11Department of MathematicsUniversitéde CaenLMNOCaen14032France

The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning *** dataset contains data from patients who are prone to the *** contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as *** the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ***,12 optimized statistical features are selected via the chi-square feature reduction *** the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features *** is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed *** a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.

关键词： COVID-19 machine learning fuzzy c-mean deep learning J4

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Numerical study on platelet adhesion to vessel walls using the kinetic Monte Carlo method

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Journal of Biomechanical science and engineering 2012年第2期7卷 275-283页

作者： Shiozaki, Seiji Ishikawa, Kenichi L. Takagi, Shu Computational Science Research Program RIKEN 2-1 Hirosawa Wako-shi Saitama 351-0198 Japan School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan

The interaction between platelets and vessel walls in the primary aggregation of platelets was investigated using kinetic Monte Carlo simulation for the multiscale simulation of thrombus formation. The kinetic Monte Carlo lattice model of a platelet surface with glycoprotein Ibα (GPIbα) localization region was simulated by considering 3 types of events: GPIbα diffusion, GPIbα-von Willebrand factor (vWF) bond formation, and breakage. The formation and breakage model of GPIbα-vWF bonds were constructed to reproduce experimental results. Next, the adhesion force between a platelet and vessel wall was evaluated, and the contribution of GPIbα localization in platelet adhesion was investigated. The adhesion force curves with respect to the distance between the platelet and vessel wall were convex upward. The results showed that when the bond formation probability between GPIbα and vWF was small, the localization of GPIbα had a large effect on the adhesion force between the platelet and the vessel wall. © 2012 by JSME.

关键词： Glycoproteins

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Randomizing hypergraphs preserving degree correlation and local clustering

arXiv

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arXiv 2021年

作者： Nakajima, Kazuki Shudo, Kazuyuki Masuda, Naoki Department of Mathematical and Computing Science Tokyo Institute of Technology Japan Department of Mathematics State University of New York Buffalo United States Computational and Data-Enabled Science and Engineering Program State University of New York Buffalo United States Faculty of Science and Engineering Waseda University Japan

Many complex systems involve direct interactions among more than two entities and can be represented by hypergraphs, in which hyperedges encode higher-order interactions among an arbitrary number of nodes. To analyze structures and dynamics of given hypergraphs, a solid practice is to compare them with those for randomized hypergraphs that preserve some specific properties of the original hypergraphs. In the present study, we propose a family of such reference models for hypergraphs, called the hyper dk-series, by extending the so-called dk-series for dyadic networks to the case of hypergraphs. The hyper dk-series preserves up to the individual node’s degree, node’s degree correlation, node’s redundancy coefficient, and/or the hyperedge’s size depending on the parameter values. Furthermore, we numerically find that higher-order hyper dk-series more accurately preserves the shortest path length and degree distribution of the one-mode projection of the original hypergraph, which the method does not intend to preserve. We also apply the hyper dk-series to numerical simulations of epidemic spreading and evolutionary game dynamics on empirical social hypergraphs. We find that the hyperedge’s size affects these dynamics more than any of the node’s properties and that the node’s degree correlation and redundancy in the empirical hypergraphs promote cooperation. Copyright © 2021, The Authors. All rights reserved.

关键词： Redundancy

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Opinion dynamics on tie-decay networks

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Physical Review Research 2021年第2期3卷 023249-023249页

作者： Kashin Sugishita Mason A. Porter Mariano Beguerisse-Díaz Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA and Faculty of Science and Engineering Waseda University 169-8555 Tokyo Japan

In social networks, interaction patterns typically change over time. We study opinion dynamics on tie-decay networks in which tie strength increases instantaneously when there is an interaction and decays exponentially between interactions. Specifically, we formulate continuous-time Laplacian dynamics and a discrete-time DeGroot model of opinion dynamics on these tie-decay networks, and we carry out numerical computations for the continuous-time Laplacian dynamics. We examine the speed of convergence by studying the spectral gaps of combinatorial Laplacian matrices of tie-decay networks. First, we compare the spectral gaps of the Laplacian matrices of tie-decay networks that we construct from empirical data with the spectral gaps for corresponding randomized and aggregate networks. We find that the spectral gaps for the empirical networks tend to be smaller than those for the randomized and aggregate networks. Second, we study the spectral gap as a function of the tie-decay rate and time. Intuitively, we expect small tie-decay rates to lead to fast convergence because the influence of each interaction between two nodes lasts longer for smaller decay rates. Moreover, as time progresses and more interactions occur, we expect eventual convergence. However, we demonstrate that the spectral gap need not decrease monotonically with respect to the decay rate or increase monotonically with respect to time. Our results highlight the importance of the interplay between the times that edges strengthen and decay in temporal networks.

关键词： Network structure Social networks Consensus & non-consensus models

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Flow Matching for Accelerated Simulation of Atomic Transport in Materials

arXiv

引用

arXiv 2024年

作者： Nam, Juno Liu, Sulin Winter, Gavin Jun, KyuJung Yang, Soojung Gómez-Bombarelli, Rafael Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Computational and Systems Biology Program Massachusetts Institute of Technology CambridgeMA02139 United States

We introduce LiFlow, a generative framework to accelerate molecular dynamics (MD) simulations for crystalline materials that formulates the task as conditional generation of atomic displacements. The model uses flow matching, with a Propagator submodel to generate atomic displacements and a Corrector to locally correct unphysical geometries, and incorporates an adaptive prior based on the Maxwell–Boltzmann distribution to account for chemical and thermal conditions. We benchmark LiFlow on a dataset comprising 25-ps trajectories of lithium diffusion across 4,186 solid-state electrolyte (SSE) candidates at four temperatures. The model obtains a consistent Spearman rank correlation of 0.7–0.8 for lithium mean squared displacement (MSD) predictions on unseen compositions. Furthermore, LiFlow generalizes from short training trajectories to larger supercells and longer simulations while maintaining high accuracy. With speed-ups of up to 600,000× compared to first-principles methods, LiFlow enables scalable simulations at significantly larger length and time scales. Copyright © 2024, The Authors. All rights reserved.

关键词： Molecular dynamics

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Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network

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Physical Review Fluids 2019年第10期4卷 104605-104605页

作者： Chenyue Xie Ke Li Chao Ma Jianchun Wang Shenzhen Key Laboratory of Complex Aerospace Flows Center for Complex Flows and Soft Matter Research Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen 518055 People's Republic of China Institute of Computational Mathematics and Scientific Engineering Computing Chinese Academy of Sciences Beijing 100190 People's Republic of China The Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

In this paper, the subgrid-scale (SGS) force and the divergence of SGS heat flux of compressible isotropic turbulence are modeled directly by an artificial neural network (ANN), which serves as a data-driven SGS modeling tool for large-eddy simulations (LESs). The unclosed SGS force and divergence of SGS heat flux are modeled based on the local stencil geometry with Galilean invariance. The input features include the first-order and second-order derivatives of filtered velocity and temperature, filtered density, and its first-order derivative. It is shown that the proposed ANN-F7 model shows an advantage over the gradient model in the a priori test. Specifically, the ANN-F7 model gives larger correlation coefficients and smaller relative errors than the gradient model. In an a posteriori analysis, the ANN-F7 model performs better than the dynamic Smagorinsky model (DSM) and dynamic mixed model (DMM) in the prediction of the statistical properties of flow fields at the Taylor microscale Reynolds number Reλ ranging from 180 to 250. The DSM and DMM models lead to the typical tilted spectral distribution of velocity, where low wave numbers are too energy rich, while those near the cutoff are damped too strongly. In contrast, it is shown that the velocity spectrum predicted by the ANN-F7 model almost overlaps with the filtered direct numerical simulation data. Besides, the ANN-F7 model reconstructs the probability density functions of SGS force and divergence of SGS heat flux much better than the DSM and DMM models. An artificial neural network with reasonable physical input features can deepen our understanding of turbulence modeling.

关键词： Compressible flows Turbulence Artificial neural networks

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Metapopulation models imply non-Poissonian statistics of interevent times

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Physical Review Research 2022年第1期4卷 013050-013050页

作者： Elohim Fonseca dos Reis Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo 14260-2900 New York USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo 14260-5030 New York USA Faculty of Science and Engineering Waseda University Tokyo 169-8555 Japan

Interevent times in temporal contact data from humans and animals typically obey heavy-tailed distributions, which impacts contagion and other dynamical processes on networks. We theoretically show that distributions of interevent times heavier-tailed than exponential distributions are a consequence of the most basic metapopulation model used in epidemiology and ecology, in which individuals move from one patch to another according to the simple random walk. Our results hold true irrespective of the network structure and also for more realistic mobility rules such as high-order random walks and the recurrent mobility patterns used for modeling human dynamics.

关键词： Continuous-time random walk Population dynamics Networks & random structures Social networks Diffusion & random walks Markovian processes Non-Markovian processes

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High-order continuum kinetic method for modeling plasma dynamics in phase space

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AIP Conference Proceedings 2014年第1期1639卷 146-149页

作者： G. V. Vogman P. Colella U. Shumlak Applied Science & Technology Program University of California Berkeley Berkeley CA 94720 USA Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA and Department of Electrical Engineering & Computer Science University of California Berkeley CA 94720 USA Aerospace & Energetic Research Program University of Washington Seattle WA 98195 USA

Continuum methods offer a high-fidelity means of simulating plasma kinetics. While computationally intensive, these methods are advantageous because they can be cast in conservation-law form, are not susceptible to noise, and can be implemented using high-order numerical methods. Advances in continuum method capabilities for modeling kinetic phenomena in plasmas require the development of validation tools in higher dimensional phase space and an ability to handle non-cartesian geometries. To that end, a new benchmark for validating Vlasov-Poisson simulations in 3D (x,vx,vy) is presented [1]. The benchmark is based on the Dory-Guest-Harris instability and is successfully used to validate a continuum finite volume algorithm. To address challenges associated with non-cartesian geometries, unique features of cylindrical phase space coordinates are described. Preliminary results of continuum kinetic simulations in 4D (r,z,vr,vz) phase space are presented.

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