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Interplay between morphology and competition in two-dimensional colony expansion

Physical Review E 2023年第3期108卷 L032301-L032301页

作者： Daniel W. Swartz Hyunseok Lee Mehran Kardar Kirill S. Korolev Department of Physics Graduate Program in Bioinformatics and Biological Design Center Boston University Boston Massachusetts 02215 USA

In growing populations, the fate of mutations depends on their competitive ability against the ancestor and their ability to colonize new territory. Here we present a theory that integrates both aspects of mutant fitness by coupling the classic description of one-dimensional competition (Fisher equation) to the minimal model of front shape (Kardar-Parisi-Zhang equation). We solve these equations and find three regimes, which are controlled solely by the expansion rates, solely by the competitive abilities, or by both. Collectively, our results provide a simple framework to study spatial competition.

关键词： Ecological population dynamics Front propagation Surface growth Coarse graining

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Evaluation of label dependency for the prediction of HLA genes 13

Evaluation of label dependency for the prediction of HLA gen...

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2013 4th ACM Conference on bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013

作者： Paunić, Vanja Steinbach, Michael Madbouly, Abeer Kumar, Vipin Dept. of Computer Science University of Minnesota Minneapolis MN 55455 United States Bioinformatics Research National Marrow Donor Program Minneapolis MN 55413 United States

ISBN: (纸本)9781450324342

The Human Leukocyte Antigen (HLA) gene system plays a crucial role in hematopoietic stem cell transplantation, where patients and donors are matched with respect to their HLA genes in order to maximize the chances of a successful transplant. It is the most polymorphic region of the human genome with some of the strongest associations with autoimmune, infectious, and inammatory diseases. The availability of HLA data is, therefore, of high importance to clinicians and researchers. However, due to its high polymorphism, obtaining it is time- And cost-prohibitive. We previously described a method for the prediction of HLA genes from widely available Single Nucleotide Polymorphism (SNP) data. In this paper we show that using HLA gene dependency information improves prediction performance on multiple real-world data sets. More specifically, we propose and evaluate different approaches for integrating HLA gene dependency into the prediction process. The results from experiments on two real data sets show that adding dependency information is a valuable asset for HLA gene prediction, particularly for smaller data sets. Copyright © 2007 by the Association for Computing Machinery.

关键词： Forecasting

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Amb-EM: A SNP-based prediction of HLA alleles using ambiguous HLA Data 14

Amb-EM: A SNP-based prediction of HLA alleles using ambiguou...

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5th ACM Conference on bioinformatics, Computational Biology, and Health Informatics, ACM BCB 2014

作者： Paunić, Vanja Steinbach, Michael Madbouly, Abeer Kumar, Vipin Dept. of Computer Science University of Minnesota MinneapolisMN55455 United States National Marrow Donor Program Bioinformatics Research MinneapolisMN55413 United States

ISBN: (纸本)9781450328944

The Human Leukocyte Antigen (HLA) genes are some of the most studied genes on the genome. This is due to their importance in bone marrow and solid organ transplantation, as well as their strong associations with many autoimmune, infectious, and inammatory diseases. As such, they can be a highly valuable asset to clinicians and researchers for elucidating biological mechanism that may drive those diseases. The extraordinary genetic polymorphism that exists in this region makes it very challenging to type. Therefore, several approaches were proposed for prediction of HLA genes from widely available genome-wide single nucleotide polymorphism (SNP) data sets in the attempt to reduce cost and utilize existing data. These methods use SNPs and highresolution training HLA data to build models for prediction of HLA genes in new samples. However, most of the existing HLA data sets are not available in high-resolution (exact allele assignment) but contain allelic ambiguities (inexact allele assignments). This is a result of existing typing methodologies not always being able to distinguish between several possible alleles at a given gene and produce ambiguous allele as a result. Current approaches for prediction of HLA genes from SNP data do not accommodate learning from ambiguous HLA data and, as such, miss the potential for an increased sample size and consequently improvements in prediction performance. In this paper, we propose Amb-EM, a novel algorithm for SNP-based prediction of HLA genes that utilizes ambiguities in the HLA data and predicts highresolution alleles using ambiguous HLA alleles for building the model. Additionally, we measure the impact that the uncertainty in the training data has on the prediction accuracy, and evaluate it on a real world data set. Our results show that the prediction from ambiguous HLA data outperforms the alternative approach which first imputes the ambiguous data into high-resolution HLA alleles and uses it to build the model. Copyrig

关键词： Forecasting

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Cell-cycle phenotyping with conditional random fields: A case study in Saccharomyces cerevisiae

Cell-cycle phenotyping with conditional random fields: A cas...

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IEEE International Symposium on Biomedical Imaging

作者： Michael B. Mayhew Alexander J. Hartemink Program in Computational Biology & Bioinformatics Duke University Durham NC USA Department of Computer Science Program in Computational Biology & Bioinformatics Duke University Durham NC USA

ISBN: (纸本)9781467364560

High-resolution, multimodal microscopy grants an intimate view of the inner workings of cells. Complex processes like cell division can be monitored with microscope images, assuming identification of cells and their cell-cycle markers: cellular structures indicative of cell-cycle progress. Here, we explore how spatial relationships between these markers can facilitate their identification. We grew and synchronized Saccharomyces cerevisiae cell cultures and then acquired multimodal image data as the cells proceeded through the cell cycle. We trained a conditional random field model to capture pixel-level spatial relationships among three different cell-cycle markers observable in our images. We observed good predictive performance of this pixel-level model on three held-out test images, and performance improved when we used marker-level information from our training data to prune model predictions. Our results support the use of conditional random fields in bioimage labeling and encourage the use of as much multiscale information as available in training data when identifying cell-cycle markers.

关键词： Labeling Predictive models Biological system modeling Training data Microscopy Data models

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TCRα/ß Depleted Mobilized Pbscs without Calcineurin Inhibitors in Patients with Fanconi Anemia

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Transplantation and Cellular Therapy 2025年第2期31卷 S64-S65页

作者： Margaret L MacMillan Qing Cao Martin Felices Megan Larson David H. McKenna Meera Srikanthan John E. Wagner Pediatrics Blood and Marrow Transplantation & Cellular Therapy Program University of Minnesota Minneapolis MN Biostatistics and Bioinformatics Masonic Cancer Center University of Minnesota Minneapolis MN Division of Hematology Oncology and Transplantation Department of Medicine University of Minnesota Minneapolis MN University of Minnesota Minneapolis MN Department of Laboratory Medicine and Pathology University of Minnesota Minneapolis MN

Background We performed a single center phase II prospective clinical trial (NCT03579875) to test the hypothesis that TCRα/ß depletion would sufficiently deplete GVHD causing α/ß T cells to eliminate the need for prolonged immunosuppression after PBSC transplantation in patients with FA. Methods Patients received conditioning regimen (MacMillan, Blood 2015) with fludarabine, cyclophosphamide and methylprednisolone alone if the patient had marrow failure only and an HLA matched sibling donor (n=2), or with total body irradiation with thymic shielding (n=12) or busulfan (n=17) if there was clonal disease, or if a haploidentical related or HLA matched or mismatched unrelated donor. All but the first patient received one dose of rituximab on day-1 for B cell depletion. Mycophenolate mofetil was given after transplant if the TCRα/ß dose ≥2 × 10 5 TCR α/β T cells/kg recipient (n=4). Mass cytometry (CyTOF) studies included targeting markers for delineating naïve, effector and effector memory CD4 cells and naïve, stem memory, central memory and effector memory CD8 cells, as well as T regulatory, B and γδ T cell subpopulations. Results 31 patients with FA, median 9.5 years (range 1.7-43.8) were enrolled. Patients had marrow failure (n=18), MDS (n=4), relapsed MDS (n=1), APML (n=1), ALL (n=1), immune deficiency (n=1), BRCA2 with clonal abnormality (n=2) or pre-emptively for BRCA2 (n=3), with 2 patients having had a prior transplant. Neutrophil engraftment occurred in all patients, median 9 days (Figure A). Platelet recovery occurred in 30 patients, median 15 days. To date, 3 patients developed grade II-IV acute GVHD. No patient has developed chronic GVHD requiring systemic therapy (Figure B). Probability of survival at 2 years is 90%, including 5/6 BRCA2 patients and all 7 adult patients (Figure C). By 1 year, only 6 patients developed viral infections requiring systemic therapy. Immune recovery in 15 patients was compared to 10 historical FA patients similarly transplanted e

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Double negative T cells,a potential biomarker for systemic lupus erythematosus

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Precision Clinical Medicine 2020年第1期3卷 34-43页

作者： Jessy J.Alexander Alexander Jacob Anthony Chang Richard J.Quigg James N.Jarvis Department of Pathology University of ChicagoChicagoIL 60637USA Departments of Medicine PediatricsJacobs School of Medicine and Biomedical Sciences and GeneticsGenomics&Bioinformatics ProgramUniversity at BuffaloBuffaloNY 14203USA

Systemic lupus erythematosus(SLE)is an autoimmune disease that is a challenge to diagnose and *** is an urgent need for biomarkers to help define organ involvement,and more effective therapies.A unique population of T cells,the CD3^(+)CD4^(−)CD8^(−)(DNeg)cells,is significantly increased in lupus *** cases(53%)of pediatric SLE patients had elevated DNeg cells in their peripheral blood,which correlated with kidney function(R^(2)=0.54).Significant infiltration of DNeg cells was observed in both adult and pediatric lupus kidneys by *** the first time,this study provides direct evidence that DNeg cells facilitate kidney injury in preclinical 8-week-old MRL/lpr lupus *** lupus mice,the increase in DNeg cells tracked with worsening disease and correlated with kidney function(R^(2)=0.85).Our results show that DNeg cells per se can cause kidney dysfunction,increase in number with increase in disease pathology,and could serve as a potential biomarker.

关键词： CD3^(+)CD4^(−)CD8^(−)T cells glomerulonephritis inflammation lupus

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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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2023 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2023

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

ISBN: (纸本)9798350337488

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. © 2023 IEEE.

关键词： classification marker gene RdRp RNA recognition motif taxonomy virus

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Circuits of the cell

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Drug Discovery Today: BIOSILICO 2004年第3期2卷 120-126页

作者： Mellor, Joseph C. Delisi, Charles Bioinformatics Graduate Program Boston University Boston MA 02215 44 Cummington Street United States Department of Biomedical Engineering Boston University Boston MA 02215 44 Cummington Street United States

Networks of gene regulation control many aspects of cellular biology, ranging from development and differentiation to the cell cycle and apoptosis. However, the precise control of genetic regulation is poorly understood for whole cells and large systems. Technical achievements in the genome-wide measurement of DNA, mRNA and proteins - and their assorted interactions - have enabled systematic approaches to regulation in molecular biology, which promise to shed new light on the complex architecture of gene regulatory networks. Detailed knowledge of such systems is crucial for our understanding of molecular phenotypes;similarly, our ability to treat and cure many diseases will depend on our understanding of molecular disease at its regulatory and mechanistic roots.

关键词： bioinformatics biological networks genetic networks interactions regulatory models

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MetNetAPI: A flexible method to access and manipulate biological network data from MetNet

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BMC Research Notes 2010年第1期3卷 1-9页

作者： Sucaet, Yves Wurtele, Eve Syrkin Department of Genetics Development and Cell Biology Iowa State University Ames IA 50011 United States Interdepartmental Program in Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States

Background. Convenient programmatic access to different biological databases allows automated integration of scientific knowledge. Many databases support a function to download files or data snapshots, or a webservice that offers "live" data. However, the functionality that a database offers cannot be represented in a static data download file, and webservices may consume considerable computational resources from the host server. Results. MetNetAPI is a versatile Application programming Interface (API) to the MetNetDB database. It abstracts, captures and retains operations away from a biological network repository and website. A range of database functions, previously only available online, can be immediately (and independently from the website) applied to a dataset of interest. Data is available in four layers: molecular entities, localized entities (linked to a specific organelle), interactions, and pathways. Navigation between these layers is intuitive (e.g. one can request the molecular entities in a pathway, as well as request in what pathways a specific entity participates). Data retrieval can be customized: Network objects allow the construction of new and integration of existing pathways and interactions, which can be uploaded back to our server. In contrast to webservices, the computational demand on the host server is limited to processing data-related queries only. Conclusions. An API provides several advantages to a systems biology software platform. MetNetAPI illustrates an interface with a central repository of data that represents the complex interrelationships of a metabolic and regulatory network. As an alternative to data-dumps and webservices, it allows access to a current and "live" database and exposes analytical functions to application developers. Yet it only requires limited resources on the server-side (thin server/fat client setup). The API is available for Java, *** and R programming environments and offers flexible query and bro

关键词： Application program Interface Molecular Entity Biological Database Command Line Interface Network Object

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Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

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BMC Systems Biology 2014年第1期8卷 1-19页

作者： Christadore, Lisa M. Pham, Lisa Kolaczyk, Eric D. Schaus, Scott E. Department of Chemistry Boston University Boston MA United States Program in Bioinformatics and Mathematics Boston University Boston MA United States Department of Mathematics and Statistics Boston University Boston MA United States

Background: Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. Results: S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug's transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions - exposure time and concentration and (ii) Network training conditions - training compendium modifications. Two analyses of SSEM-Lasso output - gene set and single gene - were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. Conclusions: This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved. © 2014 Chri

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