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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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Metabolic energy expenditure for time-varying isometric forces

arXiv

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arXiv 2024年

作者： Muralidhar, Sriram Sekaripuram Heitman, Kristen Walcott, Samuel C. Srinivasan, Manoj Mechanical and Aerospace Engineering The Ohio State University ColumbusOH43201 United States School of Health and Rehabilitation Sciences The Ohio State University ColumbusOH43201 United States Mathematical Sciences and Bioinformatics and Computational Biology Worcester Polytechnic Institute WorcesterMA01609 United States Program in Biophysics The Ohio State University ColumbusOH43201 United States

Muscles consume metabolic energy (ATP) to produce force. A mathematical model for energy expen- diture can be useful in estimating real-time costs of movements or to predict energy optimal movements. Metabolic cost models developed so far have predominantly aimed at dynamic movement tasks, where me- chanical work dominates. Further, while it is known that both force magnitude and rate of change of force (force rate) affect metabolic cost, it is not known how these terms interact, or if the force rate dependence can be a consequence of the force dependence. Here, we performed extensive human subject experiments, involving each subject over 5 hours of metabolic trials, which systematically changed the mean forces and forces rates so as to characterize a holistic relation for metabolic cost based on both force and force rate - or analogously, torque and torque rate. Our experiments involved humans producing symmetric or asym- metric sinusoidal forces with different means, amplitudes, frequencies, and rise and fall periods. We showed that the metabolic cost can be well-approximated by a sum of power law functions of torque and torque rate. We found that the metabolic cost scales non-linearly with joint torque (with exponent 1 = 1.36) and non-linearly with torque rate (with exponent 2 = 2.5). Surprisingly, the data suggested that the cost was roughly four times higher for decreasing the torque than increasing, mirroring the analogous ratio between the cost of positive and negative work. Using these metabolic cost relations, we show that if the metabolic cost scales with particular exponents with muscle force and force rates, the same exponents will be observed in multi-joint tasks with multiple muscles. Our new metabolic cost model involving both force and force rate will potentially allow better predictions of energy optimal movements and thus inform wearable robot design and analysis. © 2024, CC BY.

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Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks

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

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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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Pseudo-perplexity in One Fell Swoop for Protein Fitness Estimation

arXiv

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arXiv 2024年

作者： Kantroo, Pranav Wagner, Günter P. Machta, Benjamin B. Computational Biology and Bioinformatics Program Yale University New HavenCT06520 United States Emeritus Department of Ecology and Evolutionary Biology Yale University New HavenCT06520 United States Department of Evolutionary Biology University of Vienna Djerassi Platz 1 ViennaA-1030 Austria Hagler Institute for Advanced Studies Texas A&M College StationTX77843 United States Department of Physics Yale University New HavenCT06520 United States Quantitative Biology Institute Yale University New HavenCT06520 United States

Protein language models trained on the masked language modeling objective learn to predict the identity of hidden amino acid residues within a sequence using the remaining observable sequence as context. They do so by embedding the residues into a high dimensional space that encapsulates the relevant contextual cues. These embedding vectors serve as an informative context-sensitive representation that not only aids with the defined training objective, but can also be used for other tasks by downstream models. We propose a scheme to use the embeddings of an unmasked sequence to estimate the corresponding masked probability vectors for all the positions in a single forward pass through the language model. This One Fell Swoop (OFS) approach allows us to efficiently estimate the pseudo-perplexity of the sequence, a measure of the model's uncertainty in its predictions, that can also serve as a fitness estimate. We find that ESM2 OFS pseudo-perplexity performs nearly as well as the true pseudo-perplexity at fitness estimation, and more notably it defines a new state of the art on the ProteinGym Indels benchmark. The strong performance of the fitness measure prompted us to investigate if it could be used to detect the elevated stability reported in reconstructed ancestral sequences. We find that this measure ranks ancestral reconstructions as more fit than extant sequences. Finally, we show that the computational efficiency of the technique allows for the use of Monte Carlo methods that can rapidly explore functional sequence space. © 2024, CC BY.

关键词： Embeddings

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High fitness paths can connect proteins with low sequence overlap

arXiv

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arXiv 2024年

The structure and function of a protein are determined by its amino acid sequence. While random mutations change a protein’s sequence, evolutionary forces shape its structural fold and biological activity. Studies have shown that neutral networks can connect a local region of sequence space by single residue mutations that preserve viability. However, the larger-scale connectedness of protein morphospace remains poorly understood. Recent advances in artificial intelligence have enabled us to computationally predict a protein’s structure and quantify its functional plausibility. Here we build on these tools to develop an algorithm that generates viable paths between distantly related extant protein pairs. The intermediate sequences in these paths differ by single residue changes over subsequent steps – substitutions, insertions and deletions are admissible moves. Their fitness is evaluated using the protein language model ESM2, and maintained as high as possible subject to the constraints of the traversal. We document the qualitative variation across paths generated between progressively divergent protein pairs, some of which do not even acquire the same structural fold. The ease of interpolating between two sequences could be used as a proxy for the likelihood of homology between them. © 2024, CC BY.

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Continuous Spatiotemporal Transformers

arXiv

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arXiv 2023年

作者： de Oliveira Fonseca, Antonio Henrique Zappala, Emanuele Caro, Josue Ortega van Dijk, David Interdepartmental Neuroscience Program Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Neuroscience Yale University New HavenCT United States Wu Tsai Institute Yale University New HavenCT United States Yale University New HavenCT United States Interdepartmental Program in Computational Biology & Bioinformatics Yale University New HavenCT United States

Modeling spatiotemporal dynamical systems is a fundamental challenge in machine learning. Transformer models have been very successful in NLP and computer vision where they provide interpretable representations of data. However, a limitation of transformers in modeling continuous dynamical systems is that they are fundamentally discrete time and space models and thus have no guarantees regarding continuous sampling. To address this challenge, we present the Continuous Spatiotemporal Transformer (CST), a new transformer architecture that is designed for modeling of continuous systems. This new framework guarantees a continuous and smooth output via optimization in Sobolev space. We benchmark CST against traditional transformers as well as other spatiotemporal dynamics modeling methods and achieve superior performance in a number of tasks on synthetic and real systems, including learning brain dynamics from calcium imaging data. © 2023, CC BY-NC-SA.

关键词： Sobolev spaces

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

arXiv

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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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From mechanism to medicine: The progress and potential of epigenetics in osteoarthritis

Osteoarthritis and Cartilage Open

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Osteoarthritis and Cartilage Open 2025年第3期7卷 100621-100621页

作者： Roberts, Jack B. Rockel, Jason S. Mulders, Rick Capellini, Terence D. Appleton, C. Thomas Phanstiel, Douglas H. Lories, Rik Geurts, Jeroen Ali, Shabana Amanda Bhutani, Nidhi Stone, Laura Cruz-Almeida, Yenisel Jurisica, Igor Boer, Cindy G. Ramos, Yolande F.M. Rice, Sarah J. Kapoor, Mohit Biosciences Institute Newcastle University Newcastle upon Tyne United Kingdom Division of Orthopedics Osteoarthritis Research Program Schroeder Arthritis Institute University Health Network and Krembil Research Institute University Health Network Toronto ON Canada Department of Medical Biophysics and Computer Science University of Toronto Toronto ON Canada Department of Biomedical Data Sciences Section Molecular Epidemiology Leiden University Medical Center Leiden Netherlands Department of Human Evolutionary Biology Harvard University Cambridge MA United States Bone and Joint Institute and Department of Medicine Schulich School of Medicine and Dentistry Western University London Canada Curriculum in Bioinformatics and Computational Biology University of North Carolina Chapel Hill NC United States Laboratory of Tissue Homeostasis and Disease Skeletal Biology and Engineering Research Centre Department of Development and Regeneration KU Leuven Leuven Belgium Division of Rheumatology University Hospitals Leuven Leuven Belgium Rheumatology Department of Musculoskeletal Medicine Lausanne University Hospital and University of Lausanne (CHUV-UNIL) Lausanne Switzerland Bone and Joint Center Henry Ford Health + Michigan State University Health Sciences Detroit MI United States Department of Orthopaedic Surgery Stanford School of Medicine Stanford CA United States Department of Anesthesiology School of Medicine University of Minnesota Minneapolis MN United States Pain Research and Intervention Center of Excellence (PRICE) at the University of Florida Gainesville FL United States Department of Internal Medicine Genomics Medicine Center Erasmus MC University Medical Center Rotterdam Netherlands Department of Laboratory Medicine and Pathobiology University of Toronto Toronto ON Canada Department of Surgery University of Toronto Toronto ON Canada

Objective: Osteoarthritis (OA) is a chronic, degenerative disease of the articular joints. The disease presents an enormous clinical and economic burden globally, due in part to the lack of disease modifying therapies. For over a decade, OA researchers have been working to determine epigenetic mechanisms underlying the disease to better understand pathology, identify biomarkers of progression, and pinpoint novel targets for therapeutic intervention. Design: This article presents a summary of the 3rd International Workshop on the Epigenetics of Osteoarthritis held in Toronto, Ontario, Canada, in September 2024. The purpose of this meeting was to gather the international community to discuss the status of OA epigenetic research and share expertise on innovative techniques for future. Results: Since the two previous meetings, there has been increasing adoption of advanced single-cell and spatial sequencing technologies and bioinfomatic analyses. Furthermore, investigations of multiple joint tissues has highlighted the shifting paradigm from OA as a cartilage centric disease to the consideration of all joint tissues. Conclusions: The workshop provided a unique opportunity for early-career researchers to expand their network, and for all participants to discuss new or improved approaches to advance the field, including international consortia and data sharing. The highlights and outcomes from this OA epigenetics workshop are described in this report. © 2025 The Authors

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Estimating Causal Effects using a Multi-task Deep Ensemble

arXiv

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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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Hypostatic jammed packings of frictionless nonspherical particles

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Physical Review E 2018年第1期97卷 012909-012909页

作者： Kyle VanderWerf Weiwei Jin Mark D. Shattuck Corey S. O'Hern Department of Physics Yale University New Haven Connecticut 06520 USA Department of Mechanics and Engineering Science Peking University Beijing 100871 China Department of Mechanical Engineering & 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 Applied Physics Yale University New Haven Connecticut 06520 USA Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

We perform computational studies of static packings of a variety of nonspherical particles including circulo-lines, circulo-polygons, ellipses, asymmetric dimers, dumbbells, and others to determine which shapes form packings with fewer contacts than degrees of freedom (hypostatic packings) and which have equal numbers of contacts and degrees of freedom (isostatic packings), and to understand why hypostatic packings of nonspherical particles can be mechanically stable despite having fewer contacts than that predicted from naive constraint counting. To generate highly accurate force- and torque-balanced packings of circulo-lines and cir-polygons, we developed an interparticle potential that gives continuous forces and torques as a function of the particle coordinates. We show that the packing fraction and coordination number at jamming onset obey a masterlike form for all of the nonspherical particle packings we studied when plotted versus the particle asphericity A, which is proportional to the ratio of the squared perimeter to the area of the particle. Further, the eigenvalue spectra of the dynamical matrix for packings of different particle shapes collapse when plotted at the same A. For hypostatic packings of nonspherical particles, we verify that the number of “quartic” modes along which the potential energy increases as the fourth power of the perturbation amplitude matches the number of missing contacts relative to the isostatic value. We show that the fourth derivatives of the total potential energy in the directions of the quartic modes remain nonzero as the pressure of the packings is decreased to zero. In addition, we calculate the principal curvatures of the inequality constraints for each contact in circulo-line packings and identify specific types of contacts with inequality constraints that possess convex curvature. These contacts can constrain multiple degrees of freedom and allow hypostatic packings of nonspherical particles to be mechanically stable.

关键词： Granular materials Granular packing Jamming

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