Nature computational science最新文献

Mapping the gene space at single-cell resolution with gene signal pattern analysis

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Nature computational science Pub Date : 2024-12-20 DOI: 10.1038/s43588-024-00734-0

Aarthi Venkat, Sam Leone, Scott E. Youlten, Eric Fagerberg, John Attanasio, Nikhil S. Joshi, Michael Perlmutter, Smita Krishnaswamy

{"title":"Mapping the gene space at single-cell resolution with gene signal pattern analysis","authors":"Aarthi Venkat, Sam Leone, Scott E. Youlten, Eric Fagerberg, John Attanasio, Nikhil S. Joshi, Michael Perlmutter, Smita Krishnaswamy","doi":"10.1038/s43588-024-00734-0","DOIUrl":"10.1038/s43588-024-00734-0","url":null,"abstract":"In single-cell sequencing analysis, several computational methods have been developed to map the cellular state space, but little has been done to map or create embeddings of the gene space. Here we formulate the gene embedding problem, design tasks with simulated single-cell data to evaluate representations, and establish ten relevant baselines. We then present a graph signal processing approach, called gene signal pattern analysis (GSPA), that learns rich gene representations from single-cell data using a dictionary of diffusion wavelets on the cell–cell graph. GSPA enables characterization of genes based on their patterning and localization on the cellular manifold. We motivate and demonstrate the efficacy of GSPA as a framework for diverse biological tasks, such as capturing gene co-expression modules, condition-specific enrichment and perturbation-specific gene–gene interactions. Then we showcase the broad utility of gene representations derived from GSPA, including for cell–cell communication (GSPA-LR), spatial transcriptomics (GSPA-multimodal) and patient response (GSPA-Pt) analysis. This work presents a graph signal processing method, gene signal pattern analysis, to embed gene signals from single-cell sequencing data. In diverse experimental set-ups and case studies, GSPA establishes a gene-based framework for single-cell analysis.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"955-977"},"PeriodicalIF":12.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Cover runners-up of 2024

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Nature computational science Pub Date : 2024-12-20 DOI: 10.1038/s43588-024-00758-6

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Simulation and assimilation of the digital human brain

IF 12

Nature computational science Pub Date : 2024-12-19 DOI: 10.1038/s43588-024-00731-3

Wenlian Lu, Xin Du, Jiexiang Wang, Longbin Zeng, Leijun Ye, Shitong Xiang, Qibao Zheng, Jie Zhang, Ningsheng Xu, Jianfeng Feng, the DTB Consortium

{"title":"Simulation and assimilation of the digital human brain","authors":"Wenlian Lu, Xin Du, Jiexiang Wang, Longbin Zeng, Leijun Ye, Shitong Xiang, Qibao Zheng, Jie Zhang, Ningsheng Xu, Jianfeng Feng, the DTB Consortium","doi":"10.1038/s43588-024-00731-3","DOIUrl":"10.1038/s43588-024-00731-3","url":null,"abstract":"Here we present the Digital Brain (DB)—a platform for simulating spiking neuronal networks at the large neuron scale of the human brain on the basis of personalized magnetic resonance imaging data and biological constraints. The DB aims to reproduce both the resting state and certain aspects of the action of the human brain. An architecture with up to 86 billion neurons and 14,012 GPUs—including a two-level routing scheme between GPUs to accelerate spike transmission in up to 47.8 trillion neuronal synapses—was implemented as part of the simulations. We show that the DB can reproduce blood-oxygen-level-dependent signals of the resting state of the human brain with a high correlation coefficient, as well as interact with its perceptual input, as demonstrated in a visual task. These results indicate the feasibility of implementing a digital representation of the human brain, which can open the door to a broad range of potential applications. The Digital Brain platform is capable of simulating spiking neuronal networks at the neuronal scale of the human brain. The platform is used to reproduce blood-oxygen-level-dependent signals in both the resting state and action, thereby predicting the visual evaluation scores.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"890-898"},"PeriodicalIF":12.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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On the path toward brain-scale simulations

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Nature computational science Pub Date : 2024-12-19 DOI: 10.1038/s43588-024-00743-z

Felix Wang, James B. Aimone

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Spatial modeling algorithms for reactions and transport in biological cells.

IF 12

Nature computational science Pub Date : 2024-12-19 DOI: 10.1038/s43588-024-00745-x

Emmet A Francis, Justin G Laughlin, Jørgen S Dokken, Henrik N T Finsberg, Christopher T Lee, Marie E Rognes, Padmini Rangamani

{"title":"Spatial modeling algorithms for reactions and transport in biological cells.","authors":"Emmet A Francis, Justin G Laughlin, Jørgen S Dokken, Henrik N T Finsberg, Christopher T Lee, Marie E Rognes, Padmini Rangamani","doi":"10.1038/s43588-024-00745-x","DOIUrl":"https://doi.org/10.1038/s43588-024-00745-x","url":null,"abstract":"<p><p>Biological cells rely on precise spatiotemporal coordination of biochemical reactions to control their functions. Such cell signaling networks have been a common focus for mathematical models, but they remain challenging to simulate, particularly in realistic cell geometries. Here we present Spatial Modeling Algorithms for Reactions and Transport (SMART), a software package that takes in high-level user specifications about cell signaling networks and then assembles and solves the associated mathematical systems. SMART uses state-of-the-art finite element analysis, via the FEniCS Project software, to efficiently and accurately resolve cell signaling events over discretized cellular and subcellular geometries. We demonstrate its application to several different biological systems, including yes-associated protein (YAP)/PDZ-binding motif (TAZ) mechanotransduction, calcium signaling in neurons and cardiomyocytes, and ATP generation in mitochondria. Throughout, we utilize experimentally derived realistic cellular geometries represented by well-conditioned tetrahedral meshes. These scenarios demonstrate the applicability, flexibility, accuracy and efficiency of SMART across a range of temporal and spatial scales.</p>","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting emergence of crystals from amorphous precursors with deep learning potentials.

IF 12

Nature computational science Pub Date : 2024-12-18 DOI: 10.1038/s43588-024-00752-y

Muratahan Aykol, Amil Merchant, Simon Batzner, Jennifer N Wei, Ekin Dogus Cubuk

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An integrative data-driven model simulating C. elegans brain, body and environment interactions

IF 12

Nature computational science Pub Date : 2024-12-16 DOI: 10.1038/s43588-024-00738-w

Mengdi Zhao, Ning Wang, Xinrui Jiang, Xiaoyang Ma, Haixin Ma, Gan He, Kai Du, Lei Ma, Tiejun Huang

{"title":"An integrative data-driven model simulating C. elegans brain, body and environment interactions","authors":"Mengdi Zhao, Ning Wang, Xinrui Jiang, Xiaoyang Ma, Haixin Ma, Gan He, Kai Du, Lei Ma, Tiejun Huang","doi":"10.1038/s43588-024-00738-w","DOIUrl":"10.1038/s43588-024-00738-w","url":null,"abstract":"The behavior of an organism is influenced by the complex interplay between its brain, body and environment. Existing data-driven models focus on either the brain or the body–environment. Here we present BAAIWorm, an integrative data-driven model of Caenorhabditis elegans, which consists of two submodels: the brain model and the body–environment model. The brain model was built by multicompartment models with realistic morphology, connectome and neural population dynamics based on experimental data. Simultaneously, the body–environment model used a lifelike body and a three-dimensional physical environment. Through the closed-loop interaction between the two submodels, BAAIWorm reproduced the realistic zigzag movement toward attractors observed in C. elegans. Leveraging this model, we investigated the impact of neural system structure on both neural activities and behaviors. Consequently, BAAIWorm can enhance our understanding of how the brain controls the body to interact with its surrounding environment. BAAIWorm is an integrative data-driven model of C. elegans that simulates interactions between the brain, body and environment. The biophysically detailed neuronal model is capable of replicating the zigzag movement observed in this species.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"978-990"},"PeriodicalIF":12.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00738-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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A simulated C. elegans with biophysically detailed neurons and muscle dynamics

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Nature computational science Pub Date : 2024-12-16 DOI: 10.1038/s43588-024-00740-2

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Author Correction: Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS

IF 12

Nature computational science Pub Date : 2024-12-12 DOI: 10.1038/s43588-024-00759-5

Yeganeh Marghi, Rohan Gala, Fahimeh Baftizadeh, Uygar Sümbül

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Generative language models exhibit social identity biases.

IF 12

Nature computational science Pub Date : 2024-12-12 DOI: 10.1038/s43588-024-00741-1

Tiancheng Hu, Yara Kyrychenko, Steve Rathje, Nigel Collier, Sander van der Linden, Jon Roozenbeek

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