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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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CGM-Freq: A Python Library for Frequency Domain Analysis of Continuous Glucose Monitoring Data

CGM-Freq: A Python Library for Frequency Domain Analysis of ...

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2024 IEEE-EMBS International Conference on Biomedical and health Informatics, BHI 2024

作者： Healey, Elizabeth Kohane, Isaac Harvard-MIT Program in Health Sciences and Technology CambridgeMA United States

ISBN: (纸本)9798350351552

In recent years, the number of patients using continuous glucose monitoring (CGM) has increased. In addition to helping patients manage their disease, CGM produces time series data that can be used for integration in control algorithms, predictive models, and for retrospective analyses. Through feature extraction, many digital biomarkers can be derived from CGM. In this work, we provide a tool to extract features derived from the frequency domain. We first introduce a novel open-source Python library, CGM-Freq, for the analysis of CGM data in the frequency domain. We then test the library on real data. This work provides an open-source tool to further investigate the frequency domain of CGM signals. © 2024 IEEE.

关键词： Disease control

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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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Raman spectroscopic investigation of gramicidin A′ conformations

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Science 1974年第4151期185卷 616-618页

作者： Rothschild, Kenneth J. Stanley, H. Eugene Harvard-MIT Program in Health Sciences and Technology Department of Physics Massachusetts Institute of Technology Cambridge MA 02139 United States

Gramicidin A′ is believed to form transmembrane channels in lipid bilayers and biological membranes. The first Roman spectroscopic study of gramicidin A′ is presented. Evidence is found for two types of conformation. One type is found in the powder and has a Raman spectrum similar to that of model polypeptides with β hydrogen bonding. The second type is found when gramicidin A′ is dissolved in dimethyl sulfoxide.

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Transcriptional network classifiers

Transcriptional network classifiers

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作者： Chang, Hsun-Hsien Ramoni, Marco F. Childrens' Hospital Informatics Program Harvard-MIT Division of Health Sciences and Technology Harvard Medical School Boston Massachusetts United States

Background: Gene interactions play a central role in transcriptional networks. Many studies have performed genome-wide expression analysis to reconstruct regulatory networks to investigate disease processes. Since biological processes are outcomes of regulatory gene interactions, this paper develops a system biology approach to infer function-dependent transcriptional networks modulating phenotypic traits, which serve as a classifier to identify tissue states. Due to gene interactions taken into account in the analysis, we can achieve higher classification accuracy than existing methods. Results: Our system biology approach is carried out by the Bayesian networks framework. The algorithm consists of two steps: gene filtering by Bayes factor followed by collinearity elimination via network learning. We validate our approach with two clinical data. In the study of lung cancer subtypes discrimination, we obtain a 25-gene classifier from 111 training samples, and the test on 422 independent samples achieves 95% classification accuracy. In the study of thoracic aortic aneurysm (TAA) diagnosis, 61 samples determine a 34-gene classifier, whose diagnosis accuracy on 33 independent samples achieves 82%. The performance comparisons with three other popular methods, PCA/LDA, PAM, and Weighted Voting, confirm that our approach yields superior classification accuracy and a more compact signature. Conclusions: The system biology approach presented in this paper is able to infer function-dependent transcriptional networks, which in turn can classify biological samples with high accuracy. The validation of our classifier using clinical data demonstrates the promising value of our proposed approach for disease diagnosis. © 2009 Chang and Ramoni;licensee BioMed Central Ltd.

关键词： Bayesian networks

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Model-Based Insulin Sensitivity and Beta-Cell Function Estimation from Daily Continuous Glucose Monitoring 46

Model-Based Insulin Sensitivity and Beta-Cell Function Estim...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Healey, Elizabeth Kohane, Isaac Harvard-MIT Program in Health Sciences and Technology CambridgeMA United States Harvard Medical School Department of Biomedical Informatics BostonMA United States

ISBN: (纸本)9798350371499

Type 2 diabetes (T2D) is a prevalent chronic illness with many different options for treatment management. Continuous glucose monitors (CGM) offer physiological data that clinicians can access when making treatment decisions. However, the utility of CGM in management of T2D remains an active area of research. In our work, we demonstrate the feasibility of exploiting raw daily CGM data to estimate the physiological parameters of insulin sensitivity and beta-cell function that correlate with estimates derived from laboratory findings. We use a peak extraction algorithm to extract peaks from daily CGM data and implement a model-based approach to infer physiological parameters. We demonstrate that the inferred parameter estimates of insulin sensitivity and beta-cell function correlate to the ground truth measurements as determined by an oral glucose tolerance test (OGTT). © 2024 IEEE.

关键词： Physiological models

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The mars gravity biosatellite: Atmospheric reconditioning strategies for extended-duration rodent life support

The mars gravity biosatellite: Atmospheric reconditioning st...

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37th International Conference on Environmental Systems, ICES 2007

作者： Fulford-Jones, Thaddeus R. F. Grosse, Emily B. Heafitz, Andrew M. Li, Richard Hoffman, Jeffrey A. Massachusetts Institute of Technology Graduate Education Medical Sciences Program Harvard-MIT Division of Health Sciences and Technology 70 Vassar Street Cambridge MA 02139 United States Massachusetts Institute of Technology United States

We present results which verify the design parameters and suggest performance capabilities/limitations of the Mars Gravity Biosatellite's proposed atmospherics control subassembly. Using a combination of benchtop prototype testing and analytic techniques, we derive control requirements for ammonia. Further, we demonstrate the dehumidification performance of our proposed partial gravity condensing heat exchanger. Ammonia production is of particular concern in rodent habitats. The contaminant is released following chemical degradation of liquid waste products. The rate of production is linked to humidity levels and to the design of habitat modules in terms of bedding substrate, air flow rates, choice of structural materials, and other complex factors. Ammonia buildup can rapidly lead to rodent health concerns and can negatively impact scientific return. We present experimental data on long-term ammonia production in our novel flight-ready habitat modules, and we use this information to justify our on-orbit ammonia control strategy. A recent reanalysis of the atmospherics subassembly led to exploration of custom-designed low-power condensing heat exchanger options for control of atmospheric humidity. We show experimental and analytic data that suggests the feasibility of this approach within certain control envelopes in a partial gravity environment. A side-by-side comparison with silica gel desiccant, the baseline alternative, identifies the merits of a hybrid approach. The aforementioned results are employed to present a systems engineering analysis for filter sizing and condensing heat exchanger power based on ammonia production rates and active dehumidification results. Copyright © 2007 SAE International.

关键词： Ammonia

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Contemporary Topics in Molecular Immunology 1

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1000年

作者： H. N. Eisen R. A. Reisfeld

ISBN: (数字)9781468481426

ISBN: (纸本)9781468481440

Immunochemistry, recently rechristened molecular immunology, has been pre occupied throughout its long history with the structure and function of antibodies and the specificity of antibody-antigen reactions. With the recent X-ray diffrac of several crystallized immunoglobulin (Ig) fragments and a whole tion analyses Ig molecule, the three-dimensional structure of antibodies and their ligand combining sites has been realized, marking the concluding stages of a phase of immunological research that can be traced back at least 75 years. At the same time chemically minded immunologists have been moving in new directions. A substantial beginning in one direction has been made with the purification of messenger RNAs (mRNAs) for Ig chains. Hybridization of these RNAs (or their DNA copies made with the enzyme reverse transcriptase) to cell DNA is beginning to provide convincing estimates of the number of germ-line Ig genes. And some hybridization studies have already yielded suggestive evidence for translocation of V and C genes from separate to contiguous positions in DNA isolated from cells at different stages of differentiation. Moreover, in vitro trans lation of Ig mRNAs has revealed a remarkably hydrophobic stretch of about 20 amino acids at the N-terminus of the nascent Ig chain. This extra piece is absent in the Ig extracted from or secreted by plasma cells, presumably because it is rapidly cleaved from the "preimmunoglobulin" chain within the cell, but the extra piece probably plays a key role in directing the synthesis of prelg to the cell's secretory pathway.

关键词： Immunology

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Inferring nonstationary gene networks from temporal gene expression data

Inferring nonstationary gene networks from temporal gene exp...

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Workshop on Signal Processing Systems

作者： Chang, Hsun-Hsien Smith, Jonathan J. Ramoni, Marco F. Children's Hospital Informatics Program Harvard-MIT Division of Health Sciences and Technology Harvard Medical School Boston MA 02115 United States Department of Mathematics Massachusetts Institute of Technology Cambridge MA 02142 United States

ISBN: (纸本)9781424489336

Reverse-engineering transcriptional networks from longitudinal expression profiles is a crucial step towards the study of gene regulatory mechanisms. Genes dynamically orchestrate to each other, the stationarity assumption made by existing methods of transcriptional interaction inference is no longer adequate. As such, we need a new approach to handle the nonstationary behavior in gene expression. On the other hand, microarrays for human studies are equipped with a large number of probe sets, leading the inference of dynamic networks to a computationally intensive task. Hence, there is a need to design the inference algorithm in a tractable manner. This paper develops a Bayesian network approach to inferring the nonstationary transcriptional interactions. The applications of our approach to a clinical study of mechanical periodontal therapy demonstrates a significant improvement over stationary models. Our nonstationary network model also explains the anti-inflammatory effect of mechanical periodontal therapy. ©2010 IEEE.

关键词： Bayesian networks

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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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