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

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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

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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

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AI-driven 3D Spatial Transcriptomics

arXiv

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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

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

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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.

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

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Nature Biotechnology 2025年 1-14页

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

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. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

关键词： Glycoproteins

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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

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

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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.

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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

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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

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Robustness and Stability of Spin Glass Ground States to Perturbed Interactions

arXiv

引用

arXiv 2020年

作者： Mohanty, Vaibhav Louis, Ard A. Rudolf Peierls Centre for Theoretical Physics University of Oxford OxfordOX1 3NP United Kingdom MD-PhD Program and Program in Health Sciences and Technology Harvard Medical School BostonMA02125 United States Massachusetts Institute of Technology CambridgeMA02139 United States

Across many scientific and engineering disciplines, 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. © 2020, CC BY.

关键词： Ground state

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