检索结果-内蒙古大学图书馆

Probabilistic genotype-phenotype maps reveal mutational robustness of RNA folding, spin glasses, and quantum circuits

引用

Physical Review Research 2025年第1期7卷 013118-013118页

作者： Anna Sappington Vaibhav Mohanty Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Harvard-MIT Program in Health Sciences and Technology Harvard Medical School Boston Massachusetts 02115 USA and Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Harvard/MIT MD-PhD Program Harvard Medical School Boston Massachusetts 02115 USA and Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Chemistry and Chemical Biology Harvard University Cambridge Massachusetts 02138 USA

Recent studies of genotype-phenotype maps have reported universally enhanced phenotypic robustness to genotype mutations, a feature essential to evolution. Virtually all of these studies make a simplifying assumption that each genotype—represented as a sequence—maps deterministically to a single phenotype, such as a discrete structure. Here we introduce probabilistic genotype-phenotype (PrGP) maps, where each genotype maps to a vector of phenotype probabilities, as a more realistic and universal language for investigating robustness in a variety of physical, biological, and computational systems. We study three model systems to show that PrGP maps offer a generalized framework which can handle uncertainty emerging from various physical sources: (1) thermal fluctuation in RNA folding, (2) external field disorder in the spin-glass ground state search problem, and (3) superposition and entanglement in quantum circuits, which are realized experimentally on IBM quantum computers. In all three cases, we observe a biphasic robustness scaling which is enhanced relative to random expectation for more frequent phenotypes and approaches random expectation for less frequent phenotypes. We derive an analytical theory for the behavior of PrGP robustness, and we demonstrate that the theory is highly predictive of empirical robustness.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Lyra: An Efficient and Expressive Subquadratic Architecture for Modeling Biological Sequences

arXiv

引用

arXiv 2025年

作者： Ramesh, Krithik Siddiqui, Sameed M. Gu, Albert Mitzenmacher, Michael D. Sabeti, Pardis C. Broad Institute of MIT and Harvard CambridgeMA02142 United States Massachusetts Institute of Technology CambridgeMA02139 United States Computational and Systems Biology Program Massachusetts Institute of Technology CambridgeMA02139 United States Machine Learning Department Carnegie Mellon University PittsburgPA15213 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Organismic and Evolutionary Biology Harvard University CambridgeMA02138 United States Department of Immunology and Infectious Diseases Harvard T.H. Chan School of Public Health Harvard University BostonMA02115 United States Howard Hughes Medical Institute Chevy ChaseMD20815 United States

Deep learning architectures such as convolutional neural networks and Transformers have revolutionized biological sequence modeling, with recent advances driven by scaling up foundation and task-specific models. The computational resources and large datasets required, however, limit their applicability in biological contexts. We introduce Lyra, a subquadratic architecture for sequence modeling, grounded in the biological framework of epistasis for understanding sequence-to-function relationships. Mathematically, we demonstrate that state space models efficiently capture global epistatic interactions and combine them with projected gated convolutions for modeling local relationships. We demonstrate that Lyra is performant across over 100 wide-ranging biological tasks, achieving state-of-the-art (SOTA) performance in many key areas, including protein fitness landscape prediction, biophysical property prediction (e.g. disordered protein region functions) peptide engineering applications (e.g. antibody binding, cell-penetrating peptide prediction), RNA structure analysis, RNA function prediction, and CRISPR guide design. It achieves this with orders-of-magnitude improvements in inference speed and reduction in parameters (up to 120,000-fold in our tests) compared to recent biology foundation models. Using Lyra, we were able to train and run every task in this study on two or fewer GPUs in under two hours, democratizing access to biological sequence modeling at SOTA performance, with potential applications to many fields. © 2025, CC BY-NC-ND.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

AI-driven 3D Spatial Transcriptomics

arXiv

引用

arXiv 2025年

作者： Almagro-Pérez, Cristina Song, Andrew H. Weishaupt, Luca Kim, Ahrong Jaume, Guillaume Williamson, Drew F.K. Hemker, Konstantin Lu, Ming Y. Singh, Kritika Chen, Bowen Le, Long Phi Baras, Alexander S. Jiang, Sizun Bashashati, Ali Liu, Jonathan T.C. Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Department of Computer Science & Technology University of Cambridge Cambridge United Kingdom Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Rutgers Robert Wood Johnson Medical School New BrunswickNJ United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Broad Institute of Harvard and MIT CambridgeMA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Pathology Dana Farber Cancer Institute BostonMA United States Department of Pathology and Laboratory Medicine University of British Columbia VancouverBC Canada School of Biomedical Engineering University of British Columbia VancouverBC Canada Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications1. However, most spatial transcriptomics (ST) approaches remain limited to two-dimensional (2D) sections of tissue2–4. Although current 3D ST methods hold promise, they typically require extensive tissue sectioning, are complex, are not compatible with non-destructive 3D tissue imaging technologies, and often lack scalability5–8. Here, we present VOlumetrically Resolved Transcriptomics EXpression (VORTEX), an AI framework that leverages 3D tissue morphology and minimal 2D ST to predict volumetric 3D ST. By pretraining on diverse 3D morphology–transcriptomic pairs from heterogeneous tissue samples and then fine-tuning on minimal 2D ST data from a specific volume of interest, VORTEX learns both generic tissue-related and sample-specific morphological correlates of gene expression. This approach enables dense, high-throughput, and fast 3D ST, scaling seamlessly to large tissue volumes far beyond the reach of existing 3D ST techniques. By offering a cost-effective and minimally destructive route to obtaining volumetric molecular insights, we anticipate that VORTEX will accelerate biomarker discovery and our understanding of morphomolecular associations and cell states in complex tissues. Interactive 3D ST volumes can be viewed at https://***/. © 2025, CC BY-NC-ND.

关键词： Vortex flow

来源：评论

学校读者我要写书评

暂无评论

Self-assembling protein nanoparticles for cytosolic delivery of nucleic acids and proteins

引用

Nature Biotechnology 2025年 1-14页

作者： Feyisayo Eweje Vanessa Ibrahim Aram Shajii Michelle L. Walsh Kiran Ahmad Assma Alrefai Dominie Miyasato Hyunok Ham Kaicheng Li Carolyn A. Haller Jiaxuan Chen Elliot L. Chaikof Jessie R. Davis David R. Liu Michael Roehrl Department of Surgery Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA Harvard and MIT Division of Health Science and Technology Massachusetts Institute of Technology Cambridge MA USA Harvard/MIT MD–PhD Program Boston MA USA Wyss Institute of Biologically Inspired Engineering Harvard University Boston MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Howard Hughes Medical Institute Harvard University Cambridge MA USA Merkin Institute of Transformative Technologies in Healthcare Broad Institute of MIT and Harvard Cambridge MA USA Department of Pathology Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA

Intracellular delivery of biomacromolecules is hampered by low efficiency and cytotoxicity. Here we report the development of elastin-based nanoparticles for therapeutic delivery (ENTER), a recombinant elastin-like polypeptide (ELP)-based delivery system for effective cytosolic delivery of biomacromolecules in vitro and in vivo. Through iterative design, we developed fourth-generation ELPs fused to cationic endosomal escape peptides (EEPs) that self-assemble into pH-responsive micellar nanoparticles and enable cytosolic entry of cargo following endocytic uptake. In silico screening of α-helical peptide libraries led to the discovery of an EEP (EEP13) with 48% improved protein delivery efficiency versus a benchmark peptide. Our lead ELP–EEP13 showed similar or superior performance compared to lipid-based transfection reagents in the delivery of mRNA-encoded, DNA-encoded and protein-form Cre recombinase and CRISPR gene editors as well as short interfering RNAs to multiple cell lines and primary cell types. Intranasal administration of ELP–EEP13 combined with Cre protein achieved efficient editing of lung epithelial cells in reporter mice.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Regulatory T cells constrain T cells of shared specificity to enforce tolerance during infection

引用

Science (New York, N.Y.) 2025年第6740期387卷 eadk3248页

作者： Klawon, David E J Pagane, Nicole Walker, Matthew T. Ganci, Nicole K. Miller, Christine H. Gai, Eric Rodriguez, Donald M. Ryan-Payseur, Bridgett K. Duncombe, Ryan K. Adams, Erin J. Maienschein-Cline, Mark Freitag, Nancy E. Germain, Ronald N. Wong, Harikesh S. Savage, Peter A. Department of Pathology University of Chicago Chicago IL United States Ragon Institute of Mass General MIT and Harvard Cambridge MA United States Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge MA United States Interdisciplinary Scientist Training Program University of Chicago Chicago IL United States Department of Microbiology and Immunology University of Illinois Chicago Chicago IL United States Department of Biochemistry and Molecular Biology University of Chicago Chicago IL United States Research Informatics Core Research Resources Center University of Illinois Chicago Chicago IL United States Department of Pharmaceutical Sciences University of Illinois Chicago Chicago IL United States Lymphocyte Biology Section Laboratory of Immune System Biology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD United States Department of Biology Massachusetts Institute of Technology Cambridge MA United States

During infections, CD4+ Foxp3+ regulatory T (Treg) cells must control autoreactive CD4+ conventional T (Tconv) cell responses against self-peptide antigens while permitting those against pathogen-derived "nonself" peptides. We defined the basis of this selectivity using mice in which Treg cells reactive to a single prostate-specific self-peptide were selectively depleted. We found that self-peptide-specific Treg cells were dispensable for the control of Tconv cells of matched specificity at homeostasis. However, they were required to control such Tconv cells and prevent autoimmunity toward the prostate after exposure to elevated self-peptide during infection. Notably, the Treg cell response to self-peptide did not affect protective Tconv cell responses to a pathogen-derived peptide. Thus, self-peptide-specific Treg cells promoted self-nonself discrimination during infection by selectively controlling Tconv cells of shared self-specificity.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Robustness and stability of spin-glass ground states to perturbed interactions

引用

Physical Review E 2023年第1期107卷 014126-014126页

作者： Vaibhav Mohanty Ard A. Louis Rudolf Peierls Centre for Theoretical Physics University of Oxford Oxford OX1 3NP United Kingdom MD-PhD Program and Program in Health Sciences and Technology Harvard Medical School Boston Massachusetts 02125 USA and Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

Across many problems in science and engineering, it is important to consider how much the output of a given system changes due to perturbations of the input. Here, we investigate the glassy phase of ±J spin glasses at zero temperature by calculating the robustness of the ground states to flips in the sign of single interactions. For random graphs and the Sherrington-Kirkpatrick model, we find relatively large sets of bond configurations that generate the same ground state. These sets can themselves be analyzed as subgraphs of the interaction domain, and we compute many of their topological properties. In particular, we find that the robustness, equivalent to the average degree, of these subgraphs is much higher than one would expect from a random model. Most notably, it scales in the same logarithmic way with the size of the subgraph as has been found in genotype-phenotype maps for RNA secondary structure folding, protein quaternary structure, gene regulatory networks, as well as for models for genetic programming. The similarity between these disparate systems suggests that this scaling may have a more universal origin.

关键词： Degree correlations Mutation Network structure Scaling laws of complex systems Real world networks Spin glasses

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Restoration of tumour-growth suppression in vivo via systemic nanoparticle-mediated delivery of PTENmRNA

引用

Nature biomedical engineering 2025年第2期9卷 284页

作者： Mohammad Ariful Islam Yingjie Xu Wei Tao Jessalyn M Ubellacker Michael Lim Daniel Aum Gha Young Lee Kun Zhou Harshal Zope Mikyung Yu Wuji Cao James Trevor Oswald Meshkat Dinarvand Morteza Mahmoudi Robert Langer Philip W Kantoff Omid C Farokhzad Bruce R Zetter Jinjun Shi Center for Nanomedicine and Department of Anesthesiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Oncology Division Immunomic Therapeutics Inc. Rockville MD USA. Vascular Biology Program Boston Children's Hospital Harvard Medical School Boston MA USA. Hematology Division Brigham and Women's Hospital Boston MA USA. Department of Medicine Harvard Medical School Boston MA USA. Nanotechnology Engineering Program University of Waterloo Waterloo Ontario Canada. David H. Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge MA USA. Institute of Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA. Department of Medicine Memorial Sloan Kettering Cancer Center New York NY USA. Center for Nanomedicine and Department of Anesthesiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. ofarokhzad@bwh.harvard.edu. King Abdulaziz University Jeddah Saudi Arabia. ofarokhzad@bwh.harvard.edu. Vascular Biology Program Boston Children's Hospital Harvard Medical School Boston MA USA. bruce.zetter@childrens.harvard.edu. Center for Nanomedicine and Department of Anesthesiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. jshi@bwh.harvard.edu.

来源：评论

学校读者我要写书评

暂无评论

Integrating Autoimmune Risk Loci with Gene-Expression Data Identifies Specific Pathogenic Immune Cell Subsets (vol 89, 496, 2011)

引用

AMERICAN JOURNAL OF HUMAN GENETICS 2011年第5期89卷 682-682页

作者： Hu, Xinli Kim, Hyun Stahl, Eli Plenge, Robert Daly, Mark Raychaudhuri, Soumya Division of Genetics Brigham and Women's Hospital Harvard Medical School Boston MA 02115 USA Division of Rheumatology Immunology and Allergy Brigham and Women's Hospital Harvard Medical School Boston MA 02115 USA Medical and Population Genetics Group Broad Institute Cambridge MA 02142 USA Health Science and Technology MD Program Harvard University and Massachusetts Institute of Technology Boston MA 02115 USA Analytic and Translational Genetics Unit Massachusetts General Hospital Harvard Medical School Boston MA 02214 USA Partners HealthCare Center for Personalized Genetic Medicine Boston MA 02115 USA

Although genome-wide association studies have implicated many individual loci in complex diseases, identifying the exact causal alleles and the cell types within which they act remains greatly challenging. To ultimately understand disease mechanism, researchers must carefully conceive functional studies in relevant pathogenic cell types to demonstrate the cellular impact of disease-associated genetic variants. This challenge is highlighted in autoimmune diseases, such as rheumatoid arthritis, where any of a broad range of immunological cell types might potentially be impacted by genetic variation to cause disease. To this end, we developed a statistical approach to identify potentially pathogenic cell types in autoimmune diseases by using a gene-expression data set of 223 murine-sorted immune cells from the Immunological Genome Consortium. We found enrichment of transitional B cell genes in systemic lupus erythematosus (p = 5.9 × 10?6) and epithelial-associated stimulated dendritic cell genes in Crohn disease (p = 1.6 × 10?5). Finally, we demonstrated enrichment of CD4+ effector memory T cell genes within rheumatoid arthritis loci (p < 10?6). To further validate the role of CD4+ effector memory T cells within rheumatoid arthritis, we identified 436 loci that were not yet known to be associated with the disease but that had a statistically suggestive association in a recent genome-wide association study (GWAS) meta-analysis (pGWAS < 0.001). Even among these putative loci, we noted a significant enrichment for genes specifically expressed in CD4+ effector memory T cells (p = 1.25 × 10?4). These cell types are primary candidates for future functional studies to reveal the role of risk alleles in autoimmunity. Our approach has application in other phenotypes, outside of autoimmunity, where many loci have been discovered and high-quality cell-type-specific gene expression is available.

关键词： Autoimmunity CD4 Antigens Rheumatoid Arthritis cell type Genome-Wide Association Study T-Lymphocytes Autoimmune Disease

来源：评论

学校读者我要写书评

暂无评论

Adversarial attacks against medical deep learning systems

arXiv

引用

arXiv 2018年

作者： Finlayson, Samuel G. Chung, Hyung Won Kohane, Isaac S. Beam, Andrew L. Department of Systems Biology and MD-PhD program Harvard Medical School MIT BostonMA Massachusetts Institute of Technology CambridgeMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States

The discovery of adversarial examples has raised concerns about the practical deployment of deep learning systems. In this paper, we demonstrate that adversarial examples are capable of manipulating deep learning systems across three clinical domains. For each of our representative medical deep learning classifiers, both white and black box attacks were highly successful. Our models are representative of the current state of the art in medical computer vision and, in some cases, directly reflect architectures already seeing deployment in real world clinical settings. In addition to the technical contribution of our paper, we synthesize a large body of knowledge about the healthcare system to argue that medicine may be uniquely susceptible to adversarial attacks, both in terms of monetary incentives and technical vulnerability. To this end, we outline the healthcare economy and the incentives it creates for fraud and provide concrete examples of how and why such attacks could be realistically carried out. We urge practitioners to be aware of current vulnerabilities when deploying deep learning systems in clinical settings, and encourage the machine learning community to further investigate the domain-specific characteristics of medical learning systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Health care

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Genotype-Phenotype Maps Reveal Mutational Robustness of RNA Folding, Spin Glasses, and Quantum Circuits

arXiv

引用

arXiv 2023年

作者： Sappington, Anna Mohanty, Vaibhav Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Program for Health Sciences and Technology Harvard Medical School BostonMA02115 United States Massachusetts Institute of Technology CambridgeMA02139 United States Harvard/MIT MD-PhD Program Harvard Medical School BostonMA02115 United States Department of Chemistry and Chemical Biology Harvard University CambridgeMA02138 United States

Recent studies of genotype-phenotype (GP) maps have reported universally enhanced phenotypic robustness to genotype mutations, a feature essential to evolution. Virtually all of these studies make a simplifying assumption that each genotype—represented as a sequence—maps deterministically to a single phenotype, such as a discrete structure. Here, we introduce probabilistic genotype-phenotype (PrGP) maps, where each genotype maps to a vector of phenotype probabilities, as a more realistic and universal language for investigating robustness in a variety of physical, biological, and computational systems. We study three model systems to show that PrGP maps offer a generalized framework which can handle uncertainty emerging from various physical sources: (1) thermal fluctuation in RNA folding, (2) external field disorder in spin glass ground state finding, and (3) superposition and entanglement in quantum circuits, which are realized experimentally on IBM quantum computers. In all three cases, we observe a novel biphasic robustness scaling which is enhanced relative to random expectation for more frequent phenotypes and approaches random expectation for less frequent phenotypes. We derive an analytical theory for the behavior of PrGP robustness, and we demonstrate that the theory is highly predictive of empirical robustness. © 2023, CC BY.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：