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Unexpected failure rates for modular assembly of engineered zinc fingers (vol 5, pg 374, 2008)

NATURE METHODS 2008年第6期5卷 575-575页

作者： Ramirez, Cherie L. Foley, Jonathan E. Wright, David A. Mueller-Lerch, Felix Rahman, Shamim H. Cornu, Tatjana I. Winfrey, Ronnie J. Sander, Jeffry D. Fu, Fengli Townsend, Jeffrey A. Cathomen, Toni Voytas, Daniel F. Joung, J. Keith These authors contributed equally to this work. These authors contributed equally to this work. Department of Genetics Development and Cell Biology Iowa State University 1035A Roy J. Carver Co-Lab Ames Iowa 50011 USA. Charité Medical School Institute of Virology Hindenburgdamm 27 D-12203 Berlin Germany. Charité Medical School Institute of Virology Hindenburgdamm 27 D-12203 Berlin Germany. Charité Medical School Institute of Virology Hindenburgdamm 27 D-12203 Berlin Germany. Department of Genetics Development and Cell Biology Iowa State University 1035A Roy J. Carver Co-Lab Ames Iowa 50011 USA. Interdepartmental Graduate Program in Bioinformatics and Computational Biology 2114 Molecular Biology Building Iowa State University Ames Iowa 50011 USA. Interdepartmental Graduate Program in Bioinformatics and Computational Biology 2114 Molecular Biology Building Iowa State University Ames Iowa 50011 USA. Department of Genetics Development and Cell Biology Iowa State University 1035A Roy J. Carver Co-Lab Ames Iowa 50011 USA. Charité Medical School Institute of Virology Hindenburgdamm 27 D-12203 Berlin Germany. Department of Genetics Development and Cell Biology Iowa State University 1035A Roy J. Carver Co-Lab Ames Iowa 50011 USA. Department of Pathology Harvard Medical School Boston Massachusetts 02115 USA.

A letter to the editor is presented in response to an article on zinc fingers in a previous issue.

关键词： LETTERS to the editor ZINC

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Multistate modeling and simulation for regulatory networks 10

Multistate modeling and simulation for regulatory networks

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Simulation Winter Conference

作者： Zhen Liu Umme Juka Mobassera Clifford A. Shaffer Layne T. Watson Yang Cao Department of Computer Science Virginia Technology Blacksburg VA USA Program in Genetics Bioinformatics and Computational Biology Virginia Technology Blacksburg VA USA Department of Computer Science Department of Mathematics Virginia Technology Blacksburg VA USA

ISBN: (纸本)9781424498642

Many protein regulatory models contain chemical species best represented as having multiple states. Such models stem from the potential for multiple levels of phosphorylation or from the formation of multiprotein complexes. We seek to support such models by augmenting an existing modeling and simulation system. Interactions between multistate species can lead to a combinatorial explosion in the potential state space. This creates a challenge when using Gillespie's stochastic simulation algorithm (SSA). Both the network-free algorithm (NFA) and various rules-based methods have been proposed to more efficiently simulate such models. We show how to further improve NFA to integrate population-based and particle-based features. We then present a population-based scheme for the stochastic simulation of rule-based models. A complexity analysis is presented comparing the proposed simulation methods. We present numerical experiments for two sample models that demonstrate the power of the proposed simulation methods.

关键词： Mathematical model Biological system modeling Numerical models Chemicals Stochastic processes Equations Proteins

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Bayesian Spectral Graph Denoising with Smoothness Prior

Bayesian Spectral Graph Denoising with Smoothness Prior

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Annual Conference on Information Sciences and Systems (CISS)

作者： Sam Leone Xingzhi Sun Michael Perlmutter Smita Krishnaswamy Program for Applied Mathematics Yale University Department of Computer Science Yale University Department of Mathematics Boise State University Department of Genetics Yale School of Medicine Wu Tsai Institute Yale University FAIR Meta AI Computational Biology and Bioinformatics Program Yale University

Here we consider the problem of denoising features associated to complex data, modeled as signals on a graph, via a smoothness prior. This is motivated in part by settings such as single-cell RNA where the data is very high-dimensional, but its structure can be captured via an affinity graph. This allows us to utilize ideas from graph signal processing. In particular, we present algorithms for the cases where the signal is perturbed by Gaussian noise, dropout, and uniformly distributed noise. The signals are assumed to follow a prior distribution defined in the frequency domain which favors signals which are smooth across the edges of the graph. By pairing this prior distribution with our three models of noise generation, we propose Maximum A Posteriori (M.A.P.) estimates of the true signal in the presence of noisy data and provide algorithms for computing the M.A.P. Finally, we demonstrate the algorithms’ ability to effectively restore signals from white noise on image data and from severe dropout in single-cell RNA sequence data.

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Distinct adipose progenitor cells emerging with age drive active adipogenesis

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Science (New York, N.Y.) 2025年第6745期388卷 eadj0430页

作者： Wang, Guan Li, Gaoyan Song, Anying Zhao, Yutian Yu, Jiayu Wang, Yifan Dai, Wenting Salas, Martha Qin, Hanjun Medrano, Leonard Dow, Joan Li, Aimin Armstrong, Brian Fueger, Patrick T. Yu, Hua Zhu, Yi Shao, Mengle Wu, Xiwei Jiang, Lei Campisi, Judith Yang, Xia Wang, Qiong A. Department of Molecular and Cellular Endocrinology Arthur Riggs Diabetes and Metabolism Research Institute City of Hope Medical Center Duarte CA USA Department of Integrative Biology and Physiology University of California Los Angeles CA United States Light Microscopy Core City of Hope Medical Center Duarte CA USA The Integrative Genomics Core City of Hope Medical Center Duarte CA USA Division of Developmental and Translational Diabetes and Endocrinology Research City of Hope Medical Center Duarte CA USA Comprehensive Metabolic Phenotyping Core City of Hope Medical Center Duarte CA USA Pathology Core of Shared Resources City of Hope Medical Center Duarte CA USA Comprehensive Cancer Center Beckman Research Institute City of Hope Medical Center Duarte CA USA Children's Nutrition Research Center Department of Pediatrics Baylor College of Medicine Houston TX United States Key Laboratory of Immune Response and Immunotherapy Shanghai Institute of Immunity and Infection Chinese Academy of Sciences Shanghai China Buck Institute for Research on Aging Novato CA United States Bioinformatics Interdepartmental Program University of California Los Angeles CA United States Institute for Quantitative and Computational Biosciences University of California Los Angeles CA United States Molecular and Medical Pharmacology University of California Los Angeles CA United States

Starting at middle age, adults often suffer from visceral adiposity and associated adverse metabolic disorders. Lineage tracing in mice revealed that adipose progenitor cells (APCs) in visceral fat undergo extensive adipogenesis during middle age. Thus, despite the low turnover rate of adipocytes in young adults, adipogenesis is unlocked during middle age. Transplantations quantitatively showed that APCs in middle-aged mice exhibited high adipogenic capacity cell-autonomously. Single-cell RNA sequencing identified a distinct APC population, the committed preadipocyte, age-enriched (CP-A), emerging at this age. CP-As demonstrated elevated proliferation and adipogenesis activity. Pharmacological and genetic manipulations indicated that leukemia inhibitory factor receptor signaling was indispensable for CP-A adipogenesis and visceral fat expansion. These findings uncover a fundamental mechanism of age-dependent adipose remodeling, offering critical insights into age-related metabolic diseases.

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Diversity and Evolution of RNA Recognition Motifs: A Case Study on the Utility of RdRp in the phylum Duplornaviricota

Diversity and Evolution of RNA Recognition Motifs: A Case St...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Mikyung Je Myeongji Cho Hyeon S. Son Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul National University Seoul Republic of Korea Lab. of Computational Biology & Bioinformatics Graduate School of Public Health Seoul National University Seoul Republic of Korea Institute of Health and Environment Seoul National University Seoul Republic of Korea

Viruses possess specific conserved regions known as RNA recognition motifs, which are shared within taxonomic groups. Applying these to the Baltimore virus classification system, there are genes/proteins that serve as biomarkers for each of the seven groups. Mutations in conserved sequence regions can have a significant impact on the function of a virus, especially if these conserved regions are part of a major protein. In this study, we analyzed the RdRp gene, known to be a representative RRM of RNA viruses, from viruses of five families (Alternaviridae, Chrysoviridae, Totiviridae, Sedoreoviridae, and Spinareoviridae) within the phylum Duplornaviricota. Data were retrieved from the NCBI non-redundant (nr) database (https://***/protein) applying a cut-off of 300 amino acids in length. RdRp protein sequences were aligned using PROMALS3D, and PhyML (v3.0) was used for phylogenetic analysis. In the next step, we utilized the officially provided ICTV taxonomy and bioinformatics tools to assign the phylogenetic position of RdRp. Although this analysis focused solely on RdRp, it can be concluded that using this gene as a marker may be sufficient for taxonomic assignment. Subsequent analyses will target DNA viruses and RT viruses to perform a more comprehensive assessment. This extensive analysis aims to validate the usefulness of marker genes in the overall viral taxonomy.

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Dynamic mechanical finite element model of biological cells for studying cellular pattern formation

Dynamic mechanical finite element model of biological cells ...

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Annual International Conference of the IEEE Engineering in Medicine and biology Society

作者： Jieling Zhao Hammad Naveed Sema Kachalo Youfang Cao Wei Tian Jie Liang Bioinformatics Program Department of Broengineering University of Illinois at Chicago CAS-MPG partner Institute for Computational Biology Intel Graphics Unit Intel Corporation Department of Bioengineering University of Illinois at Chicago Faculty of Department of Bioengineering University of Illinois at Chicago

ISBN: (纸本)9781457702150

Understanding the geometric, topologic, and mechanical properties of cells and their interactions is critical for studying tissue pattern formation and organ development. computational model and tools for simulating cell pattern formation have broad implications in studying embryogenesis, blood-vessel development, tissue regeneration, and tumor growth. Although a number of cell modeling methods exist, they do not simultaneously account for detailed cellular shapes as well as dynamic changes in cell geometry and topology. Here we describe a dynamic finite element cell model (dFEMC) for studying populations of cells and tissue development. By incorporating details of cell shape, cell growth and shrinkage, cell birth and death, cell division and fusion, our method can model realistically a variety problems of cell pattern formation. We give two examples of applying our method to the study of cell fusion and cell apoptosis. The dFEMC model developed here provides a general computational framework for studying dynamics pattern formation of tissue.

关键词： Pattern formation embryo pattern formation cytology Mechanical properties Histogenic Process Cell Fusion Organogenesis

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'Isotopo' a database application for facile analysis and management of mass isotopomer data

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Database : the journal of biological databases and curation 2014年第2014期2014卷 1-8页

作者： Ahmed, Zeeshan Zeeshan, Saman Huber, Claudia Hensel, Michael Schomburg, Dietmar Münch, Richard Eylert, Eva Eisenreich, Wolfgang Dandekar, Thomas Department of Bioinformatics Biocenter University of Würzburg Am Hubland Department of Neurobiology and Genetics Biocenter University of Wuerzburg Am Hubland Institute of Molecular and Translational Therapeutic Strategies Hannover Medical School Lehrstuhl für Biochemie Center of Isotopologue Profiling Technische Universität München Division of Microbiology University of Osnabrück Department of Bioinformatics and Biochemistry Technical University Braunschweig Institute for Microbiology Biozentrum Technical University Braunschweig Germany and Computational biology and structures program European Molecular Biology Laboratory Germany Department of Bioinformatics Biocenter University of Würzburg Am Hubland Department of Neurobiology and Genetics Biocenter University of Wuerzburg Am Hubland Institute of Molecular and Translational Therapeutic Strategies Hannover Medical School Lehrstuhl für Biochemie Center of Isotopologue Profiling Technische Universität München Division of Microbiology University of Osnabrück Department of Bioinformatics and Biochemistry Technical University Braunschweig Institute for Microbiology Biozentrum Technical University Braunschweig Germany and Computational biology and structures program European Molecular Biology Laboratory 97074 Wuerzburg 97074 Wuerzburg Carl-Neuberg-Str. 1 Lichtenbergstraße 4 Barbarastraße 11 Gebäude 36 49076 Osnabrück Langer Kamp 19B 2. Obergeschoss Spielmannstraße 7 Meyerhofstr. 1 97074 Wuerzburg 97074 Wuerzburg Carl-Neuberg-Str. 1 Lichtenbergstraße 4 Barbarastraße 11 Gebäude 36 49076 Osnabrück Langer Kamp 19B 2. Obergeschoss Spielmannstraße 7 Braunschweig 38106 Hanover Germany Department of Bioinformatics Biocenter University of Würzburg Am Hubland Department of Neurobiology and Genetics Biocenter University of Wuerzburg Am Hubland Institute of Molecular and Translational Therapeutic Strategies Hannover Medical School Lehrstuhl für Biochemie Center of Isotopologue Profiling Technische Univer

UNLABELLED: The composition of stable-isotope labelled isotopologues/isotopomers in metabolic products can be measured by mass spectrometry and supports the analysis of pathways and fluxes. As a prerequisite, the original mass spectra have to be processed, managed and stored to rapidly calculate, analyse and compare isotopomer enrichments to study, for instance, bacterial metabolism in infection. For such applications, we provide here the database application 'Isotopo'. This software package includes (i) a database to store and process isotopomer data, (ii) a parser to upload and translate different data formats for such data and (iii) an improved application to process and convert signal intensities from mass spectra of (13)C-labelled metabolites such as tertbutyldimethylsilyl-derivatives of amino acids. Relative mass intensities and isotopomer distributions are calculated applying a partial least square method with iterative refinement for high precision data. The data output includes formats such as graphs for overall enrichments in amino acids. The package is user-friendly for easy and robust data management of multiple experiments.;AVAILABILITY: The 'Isotopo' software is available at the following web link (section Download): http://***/bioinformatics/isotopo/. The package contains three additional files: software executable setup (installer), one data set file (discussed in this article) and one excel file (which can be used to convert data from excel to '.iso' format). The 'Isotopo' software is compatible only with the Microsoft Windows operating system.;DATABASE URL: http://***/bioinformatics/isotopo/. © The Author(s) 2014. Published by Oxford University Press.

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ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics

arXiv

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arXiv 2024年

作者： Viswanath, Siddharth Bhaskar, Dhananjay Johnson, David R. Rocha, João Felipe Castro, Egbert Grady, Jackson D. Grigas, Alex T. Perlmutter, Michael A. O’Hern, Corey S. Krishnaswamy, Smita Department of Computer Science Yale University United States Department of Genetics Yale University United States Program in Computing Boise State University United States Computational Biology and Bioinformatics Program Yale University United States Department of Mathematics Boise State University United States

Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond scales remains challenging. To address these challenges, we introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE), which leverages the geometric scattering transform alongside transformer-based attention mechanisms to capture protein dynamics from molecular dynamics (MD) simulations. ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs and integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories. Furthermore, ProtSCAPE incorporates a regression head to enforce temporally coherent latent representations. Importantly, we demonstrate that ProtSCAPE generalizes effectively from short to long trajectories and from wild-type to mutant proteins, surpassing traditional approaches by delivering more precise and interpretable upsampling of dynamics. © 2024, CC BY.

关键词： Molecular docking

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Expanding the computational Toolbox for Single-Particle Cryo-Electron Microscopy

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Microscopy and Microanalysis 2019年第S2期25卷 2686页

作者： Bartesaghi, Alberto Department of Computer Science Trinity College of Arts &amp Sciences Department of Biochemistry Duke University School of Medicine Department of Electrical and Computer Engineering Pratt School of Engineering Duke Institute for Brain Sciences Computational Biology and Bioinformatics Program Duke University Durham NC USA

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Graph Fourier MMD for Signals on Graphs

Graph Fourier MMD for Signals on Graphs

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2023 International Conference on Sampling Theory and Applications, SampTA 2023

作者： Leone, Samuel Venkat, Aarthi Huguet, Guillaume Tong, Alexander Wolf, Guy Krishnaswamy, Smita Applied Math Program New HavenCT United States Yale University Computational Biology & Bioinformatics Program New HavenCT United States Université de Montréal Mila - Quebec Ai Institute Dept. of Math. & Stat Montreal Canada Université de Montréal Mila-Quebec Ai Institute Dept. of Comp. Sci. & Oper. Res. Montreal Canada Yale University Department of Computer Science Department of Genetics New HavenCT United States

ISBN: (纸本)9798350328851

While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little attention has been given to computing such distances for distributions on graphs. However, there has been a marked increase in data that either lies on graph (such as protein interaction networks) or can be modeled as a graph (single cell data), particularly in the biomedical sciences. Thus, it becomes important to find ways to compare signals defined on such graphs. Here, we propose Graph Fourier MMD (GFMMD), a novel distance between distributions and signals on graphs. GFMMD is defined via an optimal witness function that is both smooth on the graph and maximizes the difference in expectation between the pair of distributions on the graph. We find an analytical solution to this optimization problem as well as an embedding of distributions that results from this method. We also prove several properties of this method including scale invariance and applicability to disconnected graphs. We showcase it on graph benchmark datasets as well on single cell RNA-sequencing data analysis. In the latter, we use the GFMMD-based gene embeddings to find meaningful gene clusters. We also propose a novel type of score for gene selection called gene localization score which helps select genes for cellular state space characterization. © 2023 IEEE.

关键词： Genes

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