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Local and global measures of the shear moduli of jammed disk packings

Physical Review E 2023年第5期107卷 054903-054903页

作者： Shiyun Zhang Weiwei Jin Dong Wang Ding Xu Jerry Zhang Mark D. Shattuck Corey S. O'Hern Department of Physics University of Science and Technology of China Hefei Anhui 230026 China Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

Strain-controlled isotropic compression gives rise to jammed packings of repulsive, frictionless disks with either positive or negative global shear moduli. We carry out computational studies to understand the contributions of the negative shear moduli to the mechanical response of jammed disk packings. We first decompose the ensemble-averaged, global shear modulus as 〈G〉=(1−F−)〈G+〉+F−〈G−〉, where F− is the fraction of jammed packings with negative shear moduli and 〈G+〉 and 〈G−〉 are the average values from packings with positive and negative moduli, respectively. We show that 〈G+〉 and 〈|G−|〉 obey different power-law scaling relations above and below pN2∼1. For pN2>1, both 〈G+〉N and 〈|G−|〉N∼(pN2)β, where β∼0.5 for repulsive linear spring interactions. Despite this, 〈G〉N∼(pN2)β′ with β′≳0.5 due to the contributions from packings with negative shear moduli. We show further that the probability distribution of global shear moduli P(G) collapses at fixed pN2 and different values of p and N. We calculate analytically that P(G) is a Γ distribution in the pN2≪1 limit. As pN2 increases, the skewness of P(G) decreases and P(G) becomes a skew-normal distribution with negative skewness in the pN2≫1 limit. We also partition jammed disk packings into subsystems using Delaunay triangulation of the disk centers to calculate local shear moduli. We show that the local shear moduli defined from groups of adjacent triangles can be negative even when G>0. The spatial correlation function of local shear moduli C(r⃗) displays weak correlations for pnsub2<10−2, where nsub is the number of particles within each subsystem. However, C(r⃗) begins to develop long-ranged spatial correlations with fourfold angular symmetry for pnsub2≳10−2.

关键词： Jamming Packing & jamming problems Granular materials

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A meta-analysis of Boolean network models reveals design principles of gene regulatory networks

arXiv

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arXiv 2020年

作者： Kadelka, Claus Butrie, Taras-Michael Hilton, Evan Kinseth, Jack Schmidt, Addison Serdarevic, Haris Department of Mathematics Iowa State University AmesIA50011 United States Department of Aerospace Engineering Iowa State University AmesIA50011 United States Department of Computer Science Iowa State University AmesIA50011 United States Bioinformatics and Computational Biology Program Iowa State University AmesIA50011 United States

Gene regulatory networks (GRNs) play a central role in cellular decision-making. Understanding their structure and how it impacts their dynamics constitutes thus a fundamental biological question. GRNs are frequently modeled as Boolean networks, which are intuitive, simple to describe, and can yield qualitative results even when data is sparse. We assembled the largest repository of expert-curated Boolean GRN models. A meta-analysis of this diverse set of models reveals several design principles. GRNs exhibit more canalization, redundancy and stable dynamics than expected. Moreover, they are enriched for certain recurring network motifs. This raises the important question why evolution favors these design mechanisms. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision making

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Ciwars: A Web Server for Antibiotic Resistance Surveillance Using Longitudinal Metagenomic Data

SSRN

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SSRN 2024年

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech BlacksburgVA24060 United States Fralin Life Science Institute Virginia Tech BlacksburgVA24060 United States Department of Civil and Environmental Engineering Virginia Tech BlacksburgVA24060 United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech BlacksburgVA24060 United States

The rise of antibiotic resistance (AR) poses substantial threats to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate continuous AR monitoring through longitudinal metagenomic data are lacking. To address this, we introduce Cyberinfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive ARG and taxonomic profiling, along with detection of anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and demonstrates its potential for informing effective and timely interventions to mitigate the spread and transmission of AR. CIWARS is broadly applicable to longitudinal metagenomic data from any environment and thus could be a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use when it is needed. The web server is freely available at https://***/. © 2024, The Authors. All rights reserved.

关键词： Gene encoding

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A FLOW ARTIST FOR HIGH-DIMENSIONAL CELLULAR DATA

arXiv

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arXiv 2023年

作者： MacDonald, Kincaid Bhaskar, Dhananjay Thampakkul, Guy Nguyen, Nhi Zhang, Joia Perlmutter, Michael Adelstein, Ian Krishnaswamy, Smita Department of Mathematics Yale University United States Department of Genetics Yale School of Medicine United States Department of Computer Science Yale University United States Department of Mathematics Pomona College United States Applied Mathematics Program Yale University United States Department of Statistics University of Washington United States Department of Mathematics Boise State University United States Computational Biology and Bioinformatics Program Yale University United States

We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapshots of dynamic entities are measured, including in high-throughput biology such as single-cell transcriptomics. Existing embedding techniques either do not utilize velocity information or embed the coordinates and velocities independently, i.e., they either impose velocities on top of an existing point embedding or embed points within a prescribed vector field. Here we present FlowArtist, a neural network that embeds points while jointly learning a vector field around the points. The combination allows FlowArtist to better separate and visualize velocity-informed structures. Our results, on toy datasets and single-cell RNA velocity data, illustrate the value of utilizing coordinate and velocity information in tandem for embedding and visualizing high-dimensional data. © 2023, CC BY.

关键词： RNA

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Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks

Modeling Global Dynamics from Local Snapshots with Deep Gene...

引用

International Conference on Sampling Theory and Applications (SampTA)

作者： Scott Gigante David van Dijk Kevin R. Moon Alexander Strzalkowski Guy Wolf Smita Krishnaswamy Computational Biology and Bioinformatics Program Yale University New Haven CT USA Department of Genetics Yale University New Haven CT USA Department of Mathematics and Statistics Utah State University Logan UT USA Department of Computer Science Princeton University Princeton NJ USA Department of Mathematics and Statistics Université de Montréal Montéal QC Canada

ISBN: (数字)9781728137414

ISBN: (纸本)9781728137421

Complex high dimensional stochastic dynamic systems arise in many applications in the natural sciences and especially biology. However, while these systems are difficult to describe analytically, "snapshot" measurements that sample the output of the system are often available. In order to model the dynamics of such systems given snapshot data, or local transitions, we present a deep neural network framework we call Dynamics Modeling Network or DyMoN. DyMoN is a neural network framework trained as a deep generative Markov model whose next state is a probability distribution based on the current state. DyMoN is trained using samples of current and next-state pairs, and thus does not require longitudinal measurements. We show the advantage of DyMoN over shallow models such as Kalman filters and hidden Markov models, and other deep models such as recurrent neural networks in its ability to embody the dynamics (which can be studied via perturbation of the neural network) and generate longitudinal hypothetical trajectories. We perform three case studies in which we apply DyMoN to different types of biological systems and extract features of the dynamics in each case by examining the learned model.

关键词： Hidden Markov models Trajectory Neural networks Biological system modeling Data models Markov processes

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Estimating Causal Effects using a Multi-task Deep Ensemble

arXiv

引用

arXiv 2023年

作者： Jiang, Ziyang Hou, Zhuoran Liu, Yiling Ren, Yiman Li, Keyu Carlson, David Department of Civil and Environmental Engineering Duke University DurhamNC United States Department of Biostatistics and Bioinformatics Duke University School of Medicine DurhamNC United States Program in Computational Biology and Bioinformatics Duke University School of Medicine DurhamNC United States Department of Economics University of Michigan Ross School of Business Ann ArborMI United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Computer Science Duke University DurhamNC United States

A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a novel framework that learns both shared and group-specific information from the study population. We provide proofs demonstrating equivalency of CDME to a multi-task Gaussian process (GP) with a coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks. © 2023, CC BY.

关键词： Data handling

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Identifying the minimal sets of distance restraints for FRET-assisted protein structural modeling

arXiv

引用

arXiv 2024年

作者： Liu, Zhuoyi Grigas, Alex T. Sumner, Jacob Knab, Edward Davis, Caitlin M. O’Hern, Corey S. Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Proteins naturally occur in crowded cellular environments and interact with other proteins, nucleic acids, and organelles. Since most previous experimental protein structure determination techniques require that proteins occur in idealized, non-physiological environments, the effects of realistic cellular environments on protein structure are largely unexplored. Recently, Förster resonance energy transfer (FRET) has been shown to be an effective experimental method for investigating protein structure in vivo. Inter-residue distances measured in vivo can be incorporated as restraints in molecular dynamics (MD) simulations to model protein structural dynamics in vivo. Since most FRET studies only obtain inter-residue separations for a small number of amino acid pairs, it is important to determine the minimum number of restraints in the MD simulations that are required to achieve a given root-mean-square deviation (RMSD) from the experimental structural ensemble. Further, what is the optimal method for selecting these inter-residue restraints? Here, we implement several methods for selecting the most important FRET pairs and determine the number of pairs Nr that are needed to induce conformational changes in proteins between two experimentally determined structures. We find that enforcing only a small fraction of restraints, Nr/N 0.08, where N is the number of amino acids, can induce the conformational changes. These results establish the efficacy of FRET-assisted MD simulations for atomic scale structural modeling of proteins in vivo. © 2024, CC BY.

关键词： Conformations

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Core packing of well-defined x-ray and NMR structures is the same

arXiv

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arXiv 2022年

作者： Grigas, Alex T. Liu, Zhuoyi Regan, Lynne O’Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Centre for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh United Kingdom Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Numerous studies have investigated the differences and similarities between protein structures determined by solution NMR spectroscopy and those determined by x-ray crystallography. A fundamental question is whether any observed differences are due to differing methodologies, or to differences in the behavior of proteins in solution versus in the crystalline state. Here, we compare the properties of the hydrophobic cores of high-resolution protein crystal structures and those in NMR structures, determined using increasing numbers and types of restraints. Prior studies have reported that many NMR structures have denser cores compared to those of high-resolution x-ray crystal structures. Our current work investigates this result in more detail, and finds that these NMR structures tend to violate basic features of protein stereochemistry, such as small non-bonded atomic overlaps and few Ramachandran and side chain dihedral angle outliers. We find that NMR structures solved with more restraints, and which do not significantly violate stereochemistry, have hydrophobic cores that have a similar size and packing fraction as their counterparts determined by x-ray crystallography at high-resolution. These results lead us to conclude that, at least regarding the core packing properties, high-quality structures determined by NMR and x-ray crystallography are the same, and the differences reported earlier are most likely a consequence of methodology, rather than fundamental differences between the protein in the two different environments. Significance: Dense packing of hydrophobic residues is key to protein structure and stability. Previously, it has been noted that structures solved by NMR spectroscopy have denser cores than x-ray crystal structures. Here, we calculate the core size and packing fraction of NMR structures with experimental restraints in the PDB. Their cores are typically smaller, but denser. However, NMR structures with accurate stereochemistry possess core pa

关键词： X ray crystallography

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Modeling global dynamics from local snapshots with deep generative neural networks

arXiv

引用

arXiv 2018年

作者： Gigante, Scott Van Dijk, David Moon, Kevin R. Strzalkowski, Alexander Wolf, Guy Krishnaswamy, Smita Computational Biology and Bioinformatics Program Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Mathematics and Statistics Utah State University LoganUT United States Department of Computer Science Princeton University PrincetonNJ United States Department of Mathematics and Statistics Universitéde Montreacuteal MontéalQC Canada

Complex high dimensional stochastic dynamic systems arise in many applications in the natural sciences and especially biology. However, while these systems are difficult to describe analytically, "snapshot" measurements that sample the output of the system are often available. In order to model the dynamics of such systems given snapshot data, or local transitions, we present a deep neural network framework we call Dynamics Modeling Network or DyMoN. DyMoN is a neural network framework trained as a deep generative Markov model whose next state is a probability distribution based on the current state. DyMoN is trained using samples of current and nextstate pairs, and thus does not require longitudinal measurements. We show the advantage of DyMoN over shallow models such as Kalman filters and hidden Markov models, and other deep models such as recurrent neural networks in its ability to embody the dynamics (which can be studied via perturbation of the neural network) and generate longitudinal hypothetical trajectories. We perform three case studies in which we apply DyMoN to different types of biological systems and extract features of the dynamics in each case by examining the learned model. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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General framework for the mechanical response of metallic glasses during strain-rate-dependent uniaxial compression

引用

Physical Review Materials 2023年第10期7卷 105604-105604页

作者： Weiwei Jin Amit Datye Udo D. Schwarz Mark D. Shattuck Corey S. O'Hern Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 USA Department of Chemical and Environmental Engineering Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

Experimental data on compressive strength σmax versus strain rate ɛ̇eng for metallic glasses undergoing uniaxial compression show varying strain rate sensitivity. For some metallic glasses, σmax decreases with increasing ɛ̇eng, while for others, σmax increases with increasing ɛ̇eng, and for certain alloys σmax versus ɛ̇eng is nonmonotonic. To understand their strain rate sensitivity, we conduct molecular dynamics simulations of metallic glasses undergoing uniaxial compression at finite strain rates and coupled to heat baths with a range of temperatures T0 and damping parameters b. In the T0→0 and b→0 limits, we find that the compressive strength σmax versus temperature T obeys a “chevron-shaped” scaling relation. In the low-strain-rate regime, σmax decreases linearly with increasing T, whereas σmax grows as a power law with decreasing T in the high-strain-rate regime. For T0>0, σmax(T) deviates from the scaling curve at low strain rates, but σmax(T) rejoins the scaling curve as the strain rate increases. Enhanced dissipation reduces compression-induced heating, which causes σmax(T) to deviate from the b→0 scaling behavior for intermediate strain rates, but σmax(T) converges to the high-strain-rate power-law scaling behavior at sufficiently high strain rates. Determining σmax(T) as a function of b and T0 provides a general framework for explaining the strain rate sensitivity of metallic glasses under compression.

关键词： Compressive strength Mechanical deformation Amorphous materials Metallic glasses Molecular dynamics

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