Nature computational science最新文献_第4页

Spatial modeling algorithms for reactions and transport in biological cells 生物细胞中反应和运输的空间建模算法。

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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":"10.1038/s43588-024-00745-x","url":null,"abstract":"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. Spatial Modeling Algorithms for Reactions and Transport (SMART) is a software package that allows users to simulate spatially resolved biochemical signaling networks within realistic geometries of cells and organelles.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 1","pages":"76-89"},"PeriodicalIF":12.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11774757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866530","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}

引用次数: 0

Predicting emergence of crystals from amorphous precursors with deep learning potentials 预测具有深度学习潜力的非晶态前体晶体的出现。

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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

{"title":"Predicting emergence of crystals from amorphous precursors with deep learning potentials","authors":"Muratahan Aykol, Amil Merchant, Simon Batzner, Jennifer N. Wei, Ekin Dogus Cubuk","doi":"10.1038/s43588-024-00752-y","DOIUrl":"10.1038/s43588-024-00752-y","url":null,"abstract":"Crystallization of amorphous precursors into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to the synthesis and development of new materials in the laboratory. Reliably predicting the outcome of such a process would enable new research directions in these areas, but has remained beyond the reach of molecular modeling or ab initio methods. Here we show that candidates for the crystallization products of amorphous precursors can be predicted in many inorganic systems by sampling the local structural motifs at the atomistic level using universal deep learning interatomic potentials. We show that this approach identifies, with high accuracy, the most likely crystal structures of the polymorphs that initially nucleate from amorphous precursors, across a diverse set of material systems, including polymorphic oxides, nitrides, carbides, fluorides, chlorides, chalcogenides and metal alloys. This study introduces a2c, a computational method that leverages machine learning and atomistic simulations to predict the most likely crystallization products upon annealing of amorphous precursors. The a2c tool was demonstrated on a variety of materials, including oxides, nitrides and metallic glasses, and can assist researchers in discovering synthesis pathways for materials design.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 2","pages":"105-111"},"PeriodicalIF":12.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00752-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857211","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}

引用次数: 0

An integrative data-driven model simulating C. elegans brain, body and environment interactions 模拟秀丽隐杆线虫大脑、身体和环境相互作用的综合数据驱动模型。

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}

引用次数: 0

A simulated C. elegans with biophysically detailed neurons and muscle dynamics 模拟秀丽隐杆线虫的生物物理细节神经元和肌肉动力学。

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

引用次数: 0

Author Correction: Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS 作者更正：使用MMIDAS对单细胞数据集进行离散细胞类型和连续类型特异性变异性的联合推断。

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

引用次数: 0

Generative language models exhibit social identity biases 生成语言模型表现出社会身份偏见。

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

{"title":"Generative language models exhibit social identity biases","authors":"Tiancheng Hu, Yara Kyrychenko, Steve Rathje, Nigel Collier, Sander van der Linden, Jon Roozenbeek","doi":"10.1038/s43588-024-00741-1","DOIUrl":"10.1038/s43588-024-00741-1","url":null,"abstract":"Social identity biases, particularly the tendency to favor one’s own group (ingroup solidarity) and derogate other groups (outgroup hostility), are deeply rooted in human psychology and social behavior. However, it is unknown if such biases are also present in artificial intelligence systems. Here we show that large language models (LLMs) exhibit patterns of social identity bias, similarly to humans. By administering sentence completion prompts to 77 different LLMs (for instance, ‘We are…’), we demonstrate that nearly all base models and some instruction-tuned and preference-tuned models display clear ingroup favoritism and outgroup derogation. These biases manifest both in controlled experimental settings and in naturalistic human–LLM conversations. However, we find that careful curation of training data and specialized fine-tuning can substantially reduce bias levels. These findings have important implications for developing more equitable artificial intelligence systems and highlight the urgent need to understand how human–LLM interactions might reinforce existing social biases. Researchers show that large language models exhibit social identity biases similar to humans, having favoritism toward ingroups and hostility toward outgroups. These biases persist across models, training data and real-world human–LLM conversations.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 1","pages":"65-75"},"PeriodicalIF":12.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11774750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820326","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}

引用次数: 0

Effective quantum error correction by AI 人工智能有效的量子纠错。

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Nature computational science Pub Date : 2024-12-11 DOI: 10.1038/s43588-024-00755-9

Jie Pan

引用次数: 0

A simulated annealing algorithm for randomizing weighted networks 随机加权网络的模拟退火算法。

IF 12

Nature computational science Pub Date : 2024-12-10 DOI: 10.1038/s43588-024-00735-z

Filip Milisav, Vincent Bazinet, Richard F. Betzel, Bratislav Misic

{"title":"A simulated annealing algorithm for randomizing weighted networks","authors":"Filip Milisav, Vincent Bazinet, Richard F. Betzel, Bratislav Misic","doi":"10.1038/s43588-024-00735-z","DOIUrl":"10.1038/s43588-024-00735-z","url":null,"abstract":"Scientific discovery in connectomics relies on network null models. The prominence of network features is conventionally evaluated against null distributions estimated using randomized networks. Modern imaging technologies provide an increasingly rich array of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here we propose a simulated annealing procedure for generating randomized networks that preserve weighted degree (strength) sequences. We show that the procedure outperforms other rewiring algorithms and generalizes to multiple network formats, including directed and signed networks, as well as diverse real-world networks. Throughout, we use morphospace representation to assess the sampling behavior of the algorithm and the variability of the resulting ensemble. Finally, we show that accurate strength preservation yields different inferences about brain network organization. Collectively, this work provides a simple but powerful method to analyze richly detailed next-generation connectomics datasets. This study proposes an algorithm for generating randomized networks that preserve the weighted degree sequence. The procedure outperforms standard rewiring algorithms and extends to multiple network types, including directed and signed networks.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 1","pages":"48-64"},"PeriodicalIF":12.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11774763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142807370","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}

引用次数: 0

A scalable framework for learning the geometry-dependent solution operators of partial differential equations 一个可扩展的框架，用于学习偏微分方程的几何相关解算子。

IF 12

Nature computational science Pub Date : 2024-12-09 DOI: 10.1038/s43588-024-00732-2

Minglang Yin, Nicolas Charon, Ryan Brody, Lu Lu, Natalia Trayanova, Mauro Maggioni

{"title":"A scalable framework for learning the geometry-dependent solution operators of partial differential equations","authors":"Minglang Yin, Nicolas Charon, Ryan Brody, Lu Lu, Natalia Trayanova, Mauro Maggioni","doi":"10.1038/s43588-024-00732-2","DOIUrl":"10.1038/s43588-024-00732-2","url":null,"abstract":"Solving partial differential equations (PDEs) using numerical methods is a ubiquitous task in engineering and medicine. However, the computational costs can be prohibitively high when many-query evaluations of PDE solutions on multiple geometries are needed. Here we aim to address this challenge by introducing Diffeomorphic Mapping Operator Learning (DIMON), a generic artificial intelligence framework that learns geometry-dependent solution operators of different types of PDE on a variety of geometries. We present several examples to demonstrate the performance, efficiency and scalability of the framework in learning both static and time-dependent PDEs on parameterized and non-parameterized domains; these include solving the Laplace equations, reaction–diffusion equations and a system of multiscale PDEs that characterize the electrical propagation on thousands of personalized heart digital twins. DIMON can reduce the computational costs of solution approximations on multiple geometries from hours to seconds with substantially less computational resources. This work presents an artificial intelligence framework to learn geometry-dependent solution operators of partial differential equations (PDEs). The framework enables scalable and fast approximations of PDE solutions on a variety of 3D geometries.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"928-940"},"PeriodicalIF":12.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00732-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803066","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}

引用次数: 0

Structure-based drug design with equivariant diffusion models 基于结构的药物设计与等变扩散模型。

IF 12

Nature computational science Pub Date : 2024-12-09 DOI: 10.1038/s43588-024-00737-x

Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Jamasb, Ilia Igashov, Weitao Du, Carla Gomes, Tom L. Blundell, Pietro Lio, Max Welling, Michael Bronstein, Bruno Correia

{"title":"Structure-based drug design with equivariant diffusion models","authors":"Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Jamasb, Ilia Igashov, Weitao Du, Carla Gomes, Tom L. Blundell, Pietro Lio, Max Welling, Michael Bronstein, Bruno Correia","doi":"10.1038/s43588-024-00737-x","DOIUrl":"10.1038/s43588-024-00737-x","url":null,"abstract":"Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. Generative SBDD methods leverage structural data of drugs with their protein targets to propose new drug candidates. However, most existing methods focus exclusively on bottom-up de novo design of compounds or tackle other drug development challenges with task-specific models. The latter requires curation of suitable datasets, careful engineering of the models and retraining from scratch for each task. Here we show how a single pretrained diffusion model can be applied to a broader range of problems, such as off-the-shelf property optimization, explicit negative design and partial molecular design with inpainting. We formulate SBDD as a three-dimensional conditional generation problem and present DiffSBDD, an SE(3)-equivariant diffusion model that generates novel ligands conditioned on protein pockets. Furthermore, we show how additional constraints can be used to improve the generated drug candidates according to a variety of computational metrics. This work applies diffusion models to conditional molecule generation and shows how they can be used to tackle various structure-based drug design problems","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"899-909"},"PeriodicalIF":12.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00737-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803183","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}

引用次数: 0