50 results on '"Sindi, Suzanne S."'
Search Results
2. Modeling and Global Sensitivity Analysis of Strategies to Mitigate Covid-19 Transmission on a Structured College Campus
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Zhao, Lihong, Santiago, Fabian, Rutter, Erica M., Khatri, Shilpa, and Sindi, Suzanne S.
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- 2023
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3. Mathematical Modeling for Women’s Health
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Ford Versypt, Ashlee N., Segal, Rebecca A., and Sindi, Suzanne S.
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Dynamical Systems ,Parameter estimation ,Differential equations ,Optimization ,Data science ,thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAX Computational biology / bioinformatics ,thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling ,thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBJ Maths for engineers - Abstract
The purpose of this work is to focus mathematical modeling on issues affecting women’s health. Working in six collaborative teams, researchers developed new mathematical models to address questions in a range of application areas. Topics include HIV, oral contraceptives, blood clotting, breast cancer, neonatal respiration, and outbreak forecasting. The work is the result of newly formed collaborative groups begun during the Collaborative Workshop for Women in Mathematical Biology hosted by the Institute of Mathematics and its Applications and UnitedHealth Group Optum in June 2022. This is an open access book.
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- 2024
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4. Conservative finite volume method on deforming geometries: The case of protein aggregation in dividing yeast cells
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Heydari, A. Ali, Sindi, Suzanne S., and Theillard, Maxime
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- 2022
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5. Multiple aspects of amyloid dynamics in vivo integrate to establish prion variant dominance in yeast.
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Norton, Jennifer, Seah, Nicole, Santiago, Fabian, Sindi, Suzanne S., and Serio, Tricia R.
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PROTEIN conformation ,PROTEIN-protein interactions ,AMYLOID ,ZYGOTES ,SOCIAL dominance - Abstract
Prion variants are self-perpetuating conformers of a single protein that assemble into amyloid fibers and confer unique phenotypic states. Multiple prion variants can arise, particularly in response to changing environments, and interact within an organism. These interactions are often competitive, with one variant establishing phenotypic dominance over the others. This dominance has been linked to the competition for non-prion state protein, which must be converted to the prion state via a nucleated polymerization mechanism. However, the intrinsic rates of conversion, determined by the conformation of the variant, cannot explain prion variant dominance, suggesting a more complex interaction. Using the yeast prion system [PSI
+ ], we have determined the mechanism of dominance of the [PSI+ ]Strong variant over the [PSI+ ]Weak variant in vivo. When mixed by mating, phenotypic dominance is established in zygotes, but the two variants persist and co-exist in the lineage descended from this cell. [PSI+ ]Strong propagons, the heritable unit, are amplified at the expense of [PSI+ ]Weak propagons, through the efficient conversion of soluble Sup35 protein, as revealed by fluorescence photobleaching experiments employing variantspecific mutants of Sup35. This competition, however, is highly sensitive to the fragmentation of [PSI+ ]Strong amyloid fibers, with even transient inhibition of the fragmentation catalyst Hsp104 promoting amplification of [PSI+ ]Weak propagons. Reducing the number of [PSI+]Strong propagons prior to mating, similarly promotes [PSI+ ]Weak amplification and conversion of soluble Sup35, indicating that template number and conversion efficiency combine to determine dominance. Thus, prion variant dominance is not an absolute hierarchy but rather an outcome arising from the dynamic interplay between unique protein conformations and their interactions with distinct cellular proteostatic niches. [ABSTRACT FROM AUTHOR]- Published
- 2024
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6. A mathematical model of coagulation under flow identifies factor V as a modifier of thrombin generation in hemophilia A
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Link, Kathryn G., Stobb, Michael T., Sorrells, Matthew G., Bortot, Maria, Ruegg, Katherine, Manco‐Johnson, Marilyn J., Di Paola, Jorge A., Sindi, Suzanne S., Fogelson, Aaron L., Leiderman, Karin, and Neeves, Keith B.
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- 2020
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7. Nucleation seed size determines amyloid clearance and establishes a barrier to prion appearance in yeast
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Villali, Janice, Dark, Jason, Brechtel, Teal M., Pei, Fen, Sindi, Suzanne S., and Serio, Tricia R.
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- 2020
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8. Computationally Driven Discovery in Coagulation
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Link, Kathryn G., Stobb, Michael T., Monroe, Dougald M., Fogelson, Aaron L., Neeves, Keith B., Sindi, Suzanne S., and Leiderman, Karin
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- 2020
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9. Generalizing a mathematical model of prion aggregation allows strain coexistence and co-stability by including a novel misfolded species
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Lemarre, Paul, Pujo-Menjouet, Laurent, and Sindi, Suzanne S.
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- 2019
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10. Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention
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Davalos, Oscar A., Heydari, A. Ali, Fertig, Elana J., Sindi, Suzanne S., and Hoyer, Katrina K.
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Article - Abstract
A limitation of current deep learning (DL) approaches for single-cell RNA sequencing (scRNAseq) analysis is the lack of interpretability. Moreover, existing pipelines are designed and trained for specific tasks used disjointly for different stages of analysis. We present scANNA, a novel interpretable DL model for scR-NAseq studies that leverages neural attention to learn gene associations. After training, the learned gene importance (interpretability) is used to perform downstream analyses (e.g., global marker selection and cell-type classification) without retraining. ScANNA’s performance is comparable to or better than state-of-the-art methods designed and trained for specific standard scRNAseq analyses even though scANNA was not trained for these tasks explicitly. ScANNA enables researchers to discover meaningful results without extensive prior knowledge or training separate task-specific models, saving time and enhancing scRNAseq analyses.
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- 2023
11. On analytical and numerical approaches to division and label structured population models
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Banks, H.T., Flores, Kevin B., and Sindi, Suzanne S.
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- 2016
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12. Inferring gene regulatory networks using transcriptional profiles as dynamical attractors.
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Li, Ruihao, Rozum, Jordan C., Quail, Morgan M., Qasim, Mohammad N., Sindi, Suzanne S., Nobile, Clarissa J., Albert, Réka, and Hernday, Aaron D.
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GENE regulatory networks ,GENE expression ,ORDINARY differential equations ,BOOLEAN networks ,DNA-binding proteins ,POLYMER networks ,DNA data banks - Abstract
Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to "static" transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach (named EA) that integrates kinetic transcription data and the theory of attractor matching to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, none of which incorporate kinetic transcriptional data, in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in Saccharomyces cerevisiae. Moreover, we demonstrate the potential of our method to predict unknown transcriptional profiles that would be produced upon genetic perturbation of the GRN governing a two-state cellular phenotypic switch in Candida albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation; the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that can accurately describe the structure and dynamics of the in vivo GRN. Author summary: The establishment of distinct transcriptional programs, where specific sets of genes are activated or repressed, is fundamental to all forms of life. Sequence-specific DNA-binding proteins, often referred to as regulatory transcription factors, form interconnected gene regulatory networks (GRNs) which underlie the establishment and maintenance of specific transcriptional programs. Since their discovery, many modeling approaches have sought to understand the structure and regulatory behaviors of these GRNs. The field of GRN inference uses experimental measurements of transcript abundance to predict how regulatory transcription factors interact with their downstream target genes to establish specific transcriptional programs. However, most prior approaches have been limited by the exclusive use of "static" or steady-state measurements. We have developed a unique approach which incorporates dynamic transcriptional data into a sophisticated ordinary differential equation model to infer GRN structures that give rise to distinct transcriptional programs. Our model not only outperforms six other leading models, it also is capable of accurately predicting how changes in GRN structure will impact the resulting transcriptional programs. These notable features of our model, in conjunction with experimental validation of our predictions in real-world scenarios, contribute to an advancement in the field of gene regulatory network inference. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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13. A study in nucleated polymerization models of protein aggregation
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Davis, Jason K. and Sindi, Suzanne S.
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- 2015
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14. A CRUMP-MODE-JAGERS BRANCHING PROCESS MODEL OF PRION LOSS IN YEAST
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OLOFSSON, PETER and SINDI, SUZANNE S.
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- 2014
15. Efficient n-gram analysis in R with cmscu
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Vinson, David W., Davis, Jason K., Sindi, Suzanne S., and Dale, Rick
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- 2016
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16. Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow
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Qasim, Mohammad N, Valle Arevalo, Ashley, Paropkari, Akshay D, Ennis, Craig L, Sindi, Suzanne S, Nobile, Clarissa J, and Hernday, Aaron D
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Data Analysis ,1.1 Normal biological development and functioning ,Human Genome ,High-Throughput Nucleotide Sequencing ,DNA ,Endonucleases ,Article ,Workflow ,Vaccine Related ,Infectious Diseases ,Underpinning research ,Candida albicans ,Genetics ,2.2 Factors relating to the physical environment ,Psychology ,Humans ,Cognitive Sciences ,Generic health relevance ,Biochemistry and Cell Biology ,Aetiology ,Biotechnology ,Transcription Factors - Abstract
Regulatory transcription factors control many important biological processes, including cellular differentiation, responses to environmental perturbations and stresses, and host-pathogen interactions. Determining the genome-wide binding of regulatory transcription factors to DNA is essential to understanding the function of transcription factors in these often complex biological processes. Cleavage under targets and release using nuclease (CUT&RUN) is a modern method for genome-wide mapping of in vivo protein-DNA binding interactions that is an attractive alternative to the traditional and widely used chromatin immunoprecipitation followed by sequencing (ChIP-seq) method. CUT&RUN is amenable to a higher-throughput experimental setup and has a substantially higher dynamic range with lower per-sample sequencing costs than ChIP-seq. Here, a comprehensive CUT&RUN protocol and accompanying data analysis workflow tailored for genome-wide analysis of transcription factor-DNA binding interactions in the human fungal pathogen Candida albicans are described. This detailed protocol includes all necessary experimental procedures, from epitope tagging of transcription factor-coding genes to library preparation for sequencing; additionally, it includes a customized computational workflow for CUT&RUN data analysis.
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- 2022
17. A mathematical model of the dynamics of prion aggregates with chaperone-mediated fragmentation
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Davis, Jason K. and Sindi, Suzanne S.
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- 2016
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18. A Computational Workflow for Analysis of 3′ Tag‐Seq Data.
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Paropkari, Akshay D., Bapat, Priyanka S., Sindi, Suzanne S., and Nobile, Clarissa J.
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- 2023
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19. A Size Threshold Limits Prion Transmission and Establishes Phenotypic Diversity
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Derdowski, Aaron, Sindi, Suzanne S., Klaips, Courtney L., DiSalvo, Susanne, and Serio, Tricia R.
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- 2010
20. ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders.
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Heydari, A Ali, Davalos, Oscar A, Zhao, Lihong, Hoyer, Katrina K, and Sindi, Suzanne S
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AUTOMATIC identification ,DATA augmentation ,RNA sequencing ,GENE expression - Abstract
Motivation Single-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps or identifying rare subpopulations. However, a critical complication remains: the low number of single-cell observations due to limitations by rarity of subpopulation, tissue degradation or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this work, we present Automated Cell-Type-informed Introspective Variational Autoencoder (ACTIVA): a novel framework for generating realistic synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information. Within a single framework, ACTIVA can enlarge existing datasets and generate specific subpopulations on demand, as opposed to two separate models [such as single-cell GAN (scGAN) and conditional scGAN (cscGAN)]. Data generation and augmentation with ACTIVA can enhance scRNAseq pipelines and analysis, such as benchmarking new algorithms, studying the accuracy of classifiers and detecting marker genes. ACTIVA will facilitate analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies. Results We train and evaluate models on multiple public scRNAseq datasets. In comparison to GAN-based models (scGAN and cscGAN), we demonstrate that ACTIVA generates cells that are more realistic and harder for classifiers to identify as synthetic which also have better pair-wise correlation between genes. Data augmentation with ACTIVA significantly improves classification of rare subtypes (more than 45% improvement compared with not augmenting and 4% better than cscGAN) all while reducing run-time by an order of magnitude in comparison to both models. Availability and implementation The codes and datasets are hosted on Zenodo (https://doi.org/10.5281/zenodo.5879639). Tutorials are available at https://github.com/SindiLab/ACTIVA. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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21. A field guide to cultivating computational biology.
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Way, Gregory P., Greene, Casey S., Carninci, Piero, Carvalho, Benilton S., de Hoon, Michiel, Finley, Stacey, Gosline, Sara J. C., Lȇ Cao, Kim-Anh, Lee, Jerry S. H., Marchionni, Luigi, Robine, Nicolas, Sindi, Suzanne S., Theis, Fabian J., Yang, Jean Y. H., Carpenter, Anne E., and Fertig, Elana J.
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COMPUTATIONAL biology ,CAREER development ,SCIENTIFIC knowledge ,OCCUPATIONAL achievement ,BIOLOGISTS - Abstract
Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science. Do you want to attract computational biologists to your project or to your department? Despite the major contributions of computational biology, those attempting to bridge the interdisciplinary gap often languish in career advancement, publication, and grant review. Here, sixteen computational biologists around the globe present "A field guide to cultivating computational biology," focusing on solutions. [ABSTRACT FROM AUTHOR]
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- 2021
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22. Identification of polymorphic inversions from genotypes
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Cáceres Alejandro, Sindi Suzanne S, Raphael Benjamin J, Cáceres Mario, and González Juan R
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Computer applications to medicine. Medical informatics ,R858-859.7 ,Biology (General) ,QH301-705.5 - Abstract
Abstract Background Polymorphic inversions are a source of genetic variability with a direct impact on recombination frequencies. Given the difficulty of their experimental study, computational methods have been developed to infer their existence in a large number of individuals using genome-wide data of nucleotide variation. Methods based on haplotype tagging of known inversions attempt to classify individuals as having a normal or inverted allele. Other methods that measure differences between linkage disequilibrium attempt to identify regions with inversions but unable to classify subjects accurately, an essential requirement for association studies. Results We present a novel method to both identify polymorphic inversions from genome-wide genotype data and classify individuals as containing a normal or inverted allele. Our method, a generalization of a published method for haplotype data 1, utilizes linkage between groups of SNPs to partition a set of individuals into normal and inverted subpopulations. We employ a sliding window scan to identify regions likely to have an inversion, and accumulation of evidence from neighboring SNPs is used to accurately determine the inversion status of each subject. Further, our approach detects inversions directly from genotype data, thus increasing its usability to current genome-wide association studies (GWAS). Conclusions We demonstrate the accuracy of our method to detect inversions and classify individuals on principled-simulated genotypes, produced by the evolution of an inversion event within a coalescent model 2. We applied our method to real genotype data from HapMap Phase III to characterize the inversion status of two known inversions within the regions 17q21 and 8p23 across 1184 individuals. Finally, we scan the full genomes of the European Origin (CEU) and Yoruba (YRI) HapMap samples. We find population-based evidence for 9 out of 15 well-established autosomic inversions, and for 52 regions previously predicted by independent experimental methods in ten (9+1) individuals 34. We provide efficient implementations of both genotype and haplotype methods as a unified R package inveRsion.
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- 2012
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23. The Art and Science of Building a Computational Model to Understand Hemostasis.
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Leiderman, Karin, Sindi, Suzanne S., Monroe, Dougald M., Fogelson, Aaron L., and Neeves, Keith B.
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HEMOSTASIS , *BLOOD flow , *BLOOD coagulation , *THROMBOSIS , *THROMBIN - Abstract
Computational models of various facets of hemostasis and thrombosis have increased substantially in the last decade. These models have the potential to make predictions that can uncover new mechanisms within the complex dynamics of thrombus formation. However, these predictions are only as good as the data and assumptions they are built upon, and therefore model building requires intimate coupling with experiments. The objective of this article is to guide the reader through how a computational model is built and how it can inform and be refined by experiments. This is accomplished by answering six questions facing the model builder: (1) Why make a model? (2) What kind of model should be built? (3) How is the model built? (4) Is the model a "good" model? (5) Do we believe the model? (6) Is the model useful? These questions are answered in the context of a model of thrombus formation that has been successfully applied to understanding the interplay between blood flow, platelet deposition, and coagulation and in identifying potential modifiers of thrombin generation in hemophilia A. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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24. Computationally Driven Discovery in Coagulation.
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Link, Kathryn G., Stobb, Michael T., Monroe, Dougald M., Fogelson, Aaron L., Neeves, Keith B., Sindi, Suzanne S., and Leiderman, Karin
- Published
- 2021
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25. Mathematical Modeling of Prion Disease
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Sindi, Suzanne S.
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Medical / Infectious Diseases - Abstract
The prion hypothesis, once a heretical violation of the central dogma of molecular biology, has become an accepted mechanism used to explain a host of progressive neurodegenerative diseases in mammals and heritable phenotypes in yeast. From the beginning, mathematical models have been an essential tool in studying prion and other protein misfolding/aggregation processes. In this work, we review some of the major mathematical studies that have contributed to our understanding of prion disease and discuss trends in current and future studies.
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- 2017
26. A unifying model for the propagation of prion proteins in yeast brings insight into the [PSI+] prion.
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Lemarre, Paul, Pujo-Menjouet, Laurent, and Sindi, Suzanne S.
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PRIONS ,IMPULSIVE differential equations ,CHEMICAL processes ,YEAST ,GUANIDINIUM chlorides - Abstract
The use of yeast systems to study the propagation of prions and amyloids has emerged as a crucial aspect of the global endeavor to understand those mechanisms. Yeast prion systems are intrinsically multi-scale: the molecular chemical processes are indeed coupled to the cellular processes of cell growth and division to influence phenotypical traits, observable at the scale of colonies. We introduce a novel modeling framework to tackle this difficulty using impulsive differential equations. We apply this approach to the [PSI
+ ] yeast prion, which is associated with the misconformation and aggregation of Sup35. We build a model that reproduces and unifies previously conflicting experimental observations on [PSI+ ] and thus sheds light onto characteristics of the intracellular molecular processes driving aggregate replication. In particular our model uncovers a kinetic barrier for aggregate replication at low densities, meaning the change between prion or prion-free phenotype is a bi-stable transition. This result is based on the study of prion curing experiments, as well as the phenomenon of colony sectoring, a phenotype which is often ignored in experimental assays and has never been modeled. Furthermore, our results provide further insight into the effect of guanidine hydrochloride (GdnHCl) on Sup35 aggregates. To qualitatively reproduce the GdnHCl curing experiment, aggregate replication must not be completely inhibited, which suggests the existence of a mechanism different than Hsp104-mediated fragmentation. Those results are promising for further development of the [PSI+ ] model, but also for extending the use of this novel framework to other yeast prion or amyloid systems. Author summary: In the study of yeast prions, mathematical modeling is a powerful tool, in particular when it comes to facing the difficulties of multi-scale systems. In this study, we introduce a mathematical framework for investigating this problem in a unifying way. We focus on the yeast prion [PSI+ ] and present a simple molecular scheme for prion replication and a model of yeast budding. In order to qualitatively reproduce experiments, we need to introduce a non-linear mechanism in the molecular rates. This transforms the intracellular system into a bi-stable switch and allows for curing to occur, which is a crucial phenomenon for the study of yeast prions. To the best of our knowledge, no model in the literature includes such a mechanism, at least not explicitly. We also describe the GdnHCl curing experiment, and the propagon counting procedure. Reproducing this result requires challenging hypotheses that are commonly accepted, and our interpretation gives a new perspective on the concept of propagon. This study may be considered as a good example of how mathematical modeling can bring valuable insight into biological concepts and observations. [ABSTRACT FROM AUTHOR]- Published
- 2020
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27. A local and global sensitivity analysis of a mathematical model of coagulation and platelet deposition under flow.
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Link, Kathryn G., Stobb, Michael T., Di Paola, Jorge, Neeves, Keith B., Fogelson, Aaron L., Sindi, Suzanne S., and Leiderman, Karin
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BLOOD coagulation ,BLOOD platelets ,BLOOD coagulation factors ,HEMOSTATICS ,HEMORRHAGE ,MATHEMATICAL models - Abstract
The hemostatic response involves blood coagulation and platelet aggregation to stop blood loss from an injured blood vessel. The complexity of these processes make it difficult to intuit the overall hemostatic response without quantitative methods. Mathematical models aim to address this challenge but are often accompanied by numerous parameters choices and thus need to be analyzed for sensitivity to such choices. Here we use local and global sensitivity analyses to study a model of coagulation and platelet deposition under flow. To relate with clinical assays, we measured the sensitivity of three specific thrombin metrics: lag time, maximum relative rate of generation, and final concentration after 20 minutes. In addition, we varied parameters of three different classes: plasma protein levels, kinetic rate constants, and platelet characteristics. In terms of an overall ranking of the model’s sensitivities, we found that the local and global methods provided similar information. Our local analysis, in agreement with previous findings, shows that varying parameters within 50-150% of baseline values, in a one-at-a-time (OAT) fashion, always leads to significant thrombin generation in 20 minutes. Our global analysis gave a different and novel result highlighting groups of parameters, still varying within the normal 50-150%, that produced little or no thrombin in 20 minutes. Variations in either plasma levels or platelet characteristics, using either OAT or simultaneous variations, always led to strong thrombin production and overall, relatively low output variance. Simultaneous variation in kinetics rate constants or in a subset of all three parameter classes led to the highest overall output variance, incorporating instances with little to no thrombin production. The global analysis revealed multiple parameter interactions in the lag time and final concentration leading to relatively high variance; high variance was also observed in the thrombin generation rate, but parameters attributed to that variance acted independently and additively. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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28. Estimating the rate of prion aggregate amplification in yeast with a generation and structured population model.
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Banks, H. T., Flores, Kevin B., Langlois, Christine R., Serio, Tricia R., and Sindi, Suzanne S.
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PRION diseases ,GENE amplification ,YEAST ,MATHEMATICAL models of population ,INVERSE problems ,PARTIAL differential equations - Abstract
Prions are a special class of proteins capable of adopting multiple (misfolded) conformations, some of which have been associated with fatal diseases in mammals such as bovine spongiform encephalopathy or Creutzfeldt–Jakob Disease. Prion diseases, like protein misfolding diseases in general, are caused by the formation and amplification of ordered aggregates of proteins called amyloids. While such diseases in mammals can take decades to form, yeast have a variety of prion phenotypes that occur over a few hours, making this system an ideal model for protein misfolding disease in general. Most experimental assays of colonies with yeast prions provide steady-state population observations which complicate the inference of biochemical parameters both by the inability to directly measure aggregate amplification and by obscuring heterogeneity between cells. We develop a mathematical and inverse problem formulation to determine the amplification rate with prion aggregates from single-cell measurements observed in propagon amplification experiments. We demonstrate the ability of our formulation to determine heterogeneous amplification rates on simulated and experimental data. Our results show that aggregate amplification rates for two prion variants are strongly bimodal, suggesting that the generational structure in the yeast population impacts the ability of prion aggregates to amplify. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
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29. A dominant-negative mutant inhibits multiple prion variants through a common mechanism.
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Pei, Fen, DiSalvo, Susanne, Sindi, Suzanne S., and Serio, Tricia R.
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PRIONS ,PROTEIN conformation ,AMYLOID ,MATERIAL plasticity ,IMMUNOSPECIFICITY - Abstract
Prions adopt alternative, self-replicating protein conformations and thereby determine novel phenotypes that are often irreversible. Nevertheless, dominant-negative prion mutants can revert phenotypes associated with some conformations. These observations suggest that, while intervention is possible, distinct inhibitors must be developed to overcome the conformational plasticity of prions. To understand the basis of this specificity, we determined the impact of the G58D mutant of the Sup35 prion on three of its conformational variants, which form amyloids in S. cerevisiae. G58D had been previously proposed to have unique effects on these variants, but our studies suggest a common mechanism. All variants, including those reported to be resistant, are inhibited by G58D but at distinct doses. G58D lowers the kinetic stability of the associated amyloid, enhancing its fragmentation by molecular chaperones, promoting Sup35 resolubilization, and leading to amyloid clearance particularly in daughter cells. Reducing the availability or activity of the chaperone Hsp104, even transiently, reverses curing. Thus, the specificity of inhibition is determined by the sensitivity of variants to the mutant dosage rather than mode of action, challenging the view that a unique inhibitor must be developed to combat each variant. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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30. Distinct Prion Domain Sequences Ensure Efficient Amyloid Propagation by Promoting Chaperone Binding or Processing In Vivo.
- Author
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Langlois, Christine R., Pei, Fen, Sindi, Suzanne S., and Serio, Tricia R.
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AMYLOID ,MOLECULAR chaperones ,PRIONS ,GLYCOPROTEINS - Abstract
Prions are a group of proteins that can adopt a spectrum of metastable conformations in vivo. These alternative states change protein function and are self-replicating and transmissible, creating protein-based elements of inheritance and infectivity. Prion conformational flexibility is encoded in the amino acid composition and sequence of the protein, which dictate its ability not only to form an ordered aggregate known as amyloid but also to maintain and transmit this structure in vivo. But, while we can effectively predict amyloid propensity in vitro, the mechanism by which sequence elements promote prion propagation in vivo remains unclear. In yeast, propagation of the [PSI
+ ] prion, the amyloid form of the Sup35 protein, has been linked to an oligopeptide repeat region of the protein. Here, we demonstrate that this region is composed of separable functional elements, the repeats themselves and a repeat proximal region, which are both required for efficient prion propagation. Changes in the numbers of these elements do not alter the physical properties of Sup35 amyloid, but their presence promotes amyloid fragmentation, and therefore maintenance, by molecular chaperones. Rather than acting redundantly, our observations suggest that these sequence elements make complementary contributions to prion propagation, with the repeat proximal region promoting chaperone binding to and the repeats promoting chaperone processing of Sup35 amyloid. [ABSTRACT FROM AUTHOR]- Published
- 2016
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31. Culturomics as a data playground for tests of selection: Mathematical approaches to detecting selection in word use.
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Sindi, Suzanne S. and Dale, Rick
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BIOLOGICAL evolution , *GENETIC drift , *NATURAL selection , *WORD frequency , *CULTURAL adaptation - Abstract
In biological evolution traits may rise and fall in frequency due to genetic drift, where variant frequencies change by chance, or by selection where advantageous variants will rise in frequency. The neutral model of evolution, first developed by Kimura in the 1960s, has become the standard against which selection is detected. While the balance between these two important forces – drift and selection – has been well established in biology there are other domains where the contribution of these processes is still coming together. Although the idea of natural selection has been applied to the cultural domain since the time of Darwin, it has proven more challenging to positively identify cultural traits under selection both because of a lack of established tests for selection and a lack of large cultural data sets. However, in recent years with the accumulation of large cultural data sets many cultural features from pre-history pottery to modern baby names have been shown to evolve according to the neutral theory. But there is accumulating empirical evidence from cultural processes suggesting that the neutral theory alone cannot account for all features of the data. As such, there has been a renewed interest in determining whether there is selection amidst drift. Here we analyze a subset English word frequencies, and determine whether frequency change reveals processes of selection. Inspired by the Moran and Wright–Fisher models in population genetics, we developed a neutral model of word frequency variation to assess when linguistic data appears to depart from neutral evolution. As such, our model represents a possible “test for selection” in the linguistic domain. We explore how the distribution of word use has changed for sets of words in English for more than 100 years (1901–2008) as expressed in vocabulary usage in published books, made available by Google Ngram. When comparing empirical word frequency changes to our neutral model we find pervasive and systematic departures from neutrality. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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32. Identification and Frequency Estimation of Inversion Polymorphisms from Haplotype Data.
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Sindi, Suzanne S. and Raphael, Benjamin J.
- Abstract
Structural rearrangements, including copy-number alterations and inversions, are increasingly recognized as an important contributor to human genetic variation. Copy number variants are readily measured via array-based techniques like comparative genomic hybridization, but copy-neutral variants such as inversion polymorphisms remain difficult to identify without whole genome sequencing. We introduce a method to identify inversion polymorphisms and estimate their frequency in a population using readily available single nucleotide polymorphism (SNP) data. Our method uses a probabilistic model to describe a population as a mixture of forward and inverted chromosomes and identifies putative inversions by characteristic differences in haplotype frequencies around inversion breakpoints. On simulated data, our method accurately predicts inversions with frequencies as low as 25% in the population and reliably estimates inversion frequencies over a wide range. On the human HapMap Phase 2 data, we predict between 88 and 142 inversion polymorphisms with frequency ranging from 20 to 92 percent. Many of these correspond to known inversions or have other evidence supporting them, and the predicted inversion frequencies largely agree with the limited information presently available. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
33. An integrative probabilistic model for identification of structural variation in sequencing data.
- Author
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Sindi, Suzanne S., önal, Selim, Peng, Luke C., Hsin-Ta Wu, and Raphael, Benjamin J.
- Published
- 2012
- Full Text
- View/download PDF
34. Prion dynamics and the quest for the genetic determinant in protein-only inheritance
- Author
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Sindi, Suzanne S and Serio, Tricia R
- Subjects
- *
FUNGAL genetics , *PRION diseases , *PROTEINS , *PHENOTYPES , *BIOCHEMISTRY , *MATHEMATICAL models , *COMMUNICABLE diseases - Abstract
According to the prion hypothesis, proteins may act in atypical roles as genetic elements of infectivity and inheritance by undergoing self-replicating changes in physical state. While the preponderance of evidence strongly supports this concept particularly in fungi, the detailed mechanisms by which distinct protein forms specify unique phenotypes are emerging concepts. A particularly active area of investigation is the molecular nature of the heritable species, which has been probed through genetic, biochemical, and cell biological experimentation as well as by mathematical modeling. Here, we suggest that these studies are converging to implicate small aggregates composed of prion-state conformers as the transmissible genetic determinants of protein-based phenotypes. [Copyright &y& Elsevier]
- Published
- 2009
- Full Text
- View/download PDF
35. MultiCellDS : a community-developed standard for curating microenvironment-dependent multicellular data
- Author
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Friedman, Samuel H., Anderson, Alexander R. A., Bortz, David M., Fletcher, Alexander G., Frieboes, Hermann B., Ghaffarizadeh, Ahmadreza, Grimes, David Robert, Hawkins-Daarud, Andrea, Hoehme, Stefan, Juarez, Edwin F., Kesselman, Carl, Merks, Roeland M.H., Mumenthaler, Shannon M., Newton, Paul K., Norton, Kerri-Ann, Rawat, Rishi, Rockne, Russell C., Ruderman, Daniel, Scott, Jacob, Sindi, Suzanne S., Sparks, Jessica L., Swanson, Kristin, Agus, David B., and Macklin, Paul
- Subjects
Metadata ,Data Standard ,World Wide Web ,Multicellular organism ,Data curation ,Cell culture ,Computer science ,Systems biology ,Context (language use) ,Data science ,Discoverability - Abstract
Exchanging and understanding scientific data and their context represents a significant barrier to advancing research, especially with respect to information siloing. Maintaining information provenance and providing data curation and quality control help overcome common concerns and barriers to the effective sharing of scientific data. To address these problems in and the unique challenges of multicellular systems, we assembled a panel composed of investigators from several disciplines to create the MultiCellular Data Standard (MultiCellDS) with a use-case driven development process. The standard includes (1) digital cell lines, which are analogous to traditional biological cell lines, to record metadata, cellular microenvironment, and cellular phenotype variables of a biological cell line, (2) digital snapshots to consistently record simulation, experimental, and clinical data for multicellular systems, and (3) collections that can logically group digital cell lines and snapshots. We have created a MultiCellular DataBase (MultiCellDB) to store digital snapshots and the 200+ digital cell lines we have generated. MultiCellDS, by having a fixed standard, enables discoverability, extensibility, maintainability, searchability, and sustainability of data, creating biological applicability and clinical utility that permits us to identify upcoming challenges to uplift biology and strategies and therapies for improving human health.
36. Initial condition of stochastic self-assembly.
- Author
-
Davis, Jason K. and Sindi, Suzanne S.
- Subjects
- *
NUCLEATION , *NUCLEAR size (Physics) , *PRION diseases - Abstract
The formation of a stable protein aggregate is regarded as the rate limiting step in the establishment of prion diseases. In these systems, once aggregates reach a critical size the growth process accelerates and thus the waiting time until the appearance of the first critically sized aggregate is a key determinant of disease onset. In addition to prion diseases, aggregation and nucleation is a central step of many physical, chemical, and biological process. Previous studies have examined the first-arrival time at a critical nucleus size during homogeneous self-assembly under the assumption that at time t=0 the system was in the all-monomer state. However, in order to compare to in vivo biological experiments where protein constituents inherited by a newly born cell likely contain intermediate aggregates, other possibilities must be considered. We consider one such possibility by conditioning the unique ergodic size distribution on subcritical aggregate sizes; this least-informed distribution is then used as an initial condition. We make the claim that this initial condition carries fewer assumptions than an all-monomer one and verify that it can yield significantly different averaged waiting times relative to the all-monomer condition under various models of assembly. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
37. Amyloid-associated activity contributes to the severity and toxicity of a prion phenotype.
- Author
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Pezza, John A., Villali, Janice, Sindi, Suzanne S., and Serio, Tricia R.
- Published
- 2014
- Full Text
- View/download PDF
38. Assessing the impact of product inhibition in a chromogenic assay.
- Author
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Stobb, Michael T., Monroe, Dougald M., Leiderman, Karin, and Sindi, Suzanne S.
- Subjects
- *
BLOOD coagulation factors , *INHIBITION (Chemistry) , *CHROMOGENIC compounds , *MARKOV chain Monte Carlo - Abstract
Chromogenic substrates (CS) are synthetic substrates used to monitor the activity of a target enzyme. It has been reported that some CSs display competitive product inhibition with their target enzyme. Thus, in assays where enzyme activity is continuously monitored over long periods of time, the product inhibition may significantly interfere with the reactions being monitored. Despite this knowledge, it is rare for CSs to be directly incorporated into mathematical models that simulate these assays. This devalues the predictive power of the models. In this study, we examined the interactions between a single enzyme, coagulation factor Xa, and its chromogenic substrate. We developed, and experimentally validated, a mathematical model of a chromogenic assay for factor Xa that explicitly included product inhibition from the CS. We employed Bayesian inference, in the form of Markov-Chain Monte Carlo, to estimate the strength of the product inhibition and other sources of uncertainty such as pipetting error and kinetic rate constants. Our model, together with carefully calibrated biochemistry experiments, allowed for full characterization of the strength and impact of product inhibition in the assay. The effect of CS product inhibition in more complex reaction mixtures was further explored using mathematical models. Image 1 [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
39. CD8 Follicular T Cells Promote B Cell Antibody Class Switch in Autoimmune Disease.
- Author
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Valentine, Kristen M., Lawrence, Travis J., Mullins, Genevieve N., Davini, Dan, Al-Kuhlani, Mufadhal, Pinney, James M., Beaudin, Anna E., Hoyer, Katrina K., Manansala, Miguel, Gravano, David M., Davis, Jason K., and Sindi, Suzanne S.
- Subjects
- *
CD8 antigen , *T cells , *FOLLICULAR dendritic cells , *AUTOIMMUNE diseases , *INTERLEUKIN-2 , *CD4 antigen ,ANIMAL models of immunologic diseases - Abstract
CD8 T cells can play both a protective and pathogenic role in inflammation and autoimmune development. Recent studies have highlighted the ability of CD8 T cells to function as T follicular helper (Tfh) cells in the germinal center in the context of infection. However, whether this phenomenon occurs in autoimmunity and contributes to autoimmune pathogenesis is largely unexplored. In this study, we show that CD8 T cells acquire a CD4 Tfh profile in the absence of functional regulatory T cells in both the IL-2-deficient and scurfy mouse models. Depletion of CD8 T cells mitigates autoimmune pathogenesis in IL-2-deficient mice. CD8 T cells express the B cell follicle-localizing chemokine receptor CXCR5, a principal Tfh transcription factor Bcl6, and the Tfh effector cytokine IL-21. CD8 T cells localize to the B cell follicle, express B cell costimulatory proteins, and promote B cell differentiation and Ab isotype class switching. These data reveal a novel contribution of autoreactive CD8 T cells to autoimmune disease, in part, through CD4 follicular-like differentiation and functionality. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
40. Host tracheal and intestinal microbiomes inhibit Coccidioides growth in vitro .
- Author
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Tejeda-Garibay S, Zhao L, Hum NR, Pimentel M, Diep AL, Amiri B, Sindi SS, Weilhammer DR, Loots GG, and Hoyer KK
- Subjects
- Animals, Mice, Coccidioidomycosis microbiology, Microbiota drug effects, Bacteria drug effects, Bacteria isolation & purification, Bacteria classification, Bacteria genetics, Bacteria growth & development, Female, Anti-Bacterial Agents pharmacology, RNA, Ribosomal, 16S genetics, Coccidioides growth & development, Coccidioides drug effects, Gastrointestinal Microbiome drug effects, Trachea microbiology
- Abstract
Coccidioidomycosis, also known as Valley fever, is a disease caused by the fungal pathogen Coccidioides . Unfortunately, patients are often misdiagnosed with bacterial pneumonia, leading to inappropriate antibiotic treatment. The soil Bacillus subtilis -like species exhibits antagonistic properties against Coccidioides in vitro ; however, the antagonistic capabilities of host microbiota against Coccidioides are unexplored. We sought to examine the potential of the tracheal and intestinal microbiomes to inhibit the growth of Coccidioides in vitro . We hypothesized that an uninterrupted lawn of microbiota obtained from antibiotic-free mice would inhibit the growth of Coccidioides, while partial in vitro depletion through antibiotic disk diffusion assays would allow a niche for fungal growth. We observed that the microbiota grown on 2×GYE (GYE) and Columbia colistin and nalidixic acid with 5% sheep's blood agar inhibited the growth of Coccidioides , but microbiota grown on chocolate agar did not. Partial depletion of the microbiota through antibiotic disk diffusion revealed diminished inhibition and comparable growth of Coccidioides to controls. To characterize the bacteria grown and identify potential candidates contributing to the inhibition of Coccidioides , 16S rRNA sequencing was performed on tracheal and intestinal agar cultures and murine lung extracts. We found that the host bacteria likely responsible for this inhibition primarily included Lactobacillus and Staphylococcus . The results of this study demonstrate the potential of the host microbiota to inhibit the growth of Coccidioides in vitro and suggest that an altered microbiome through antibiotic treatment could negatively impact effective fungal clearance and allow a niche for fungal growth in vivo ., Importance: Coccidioidomycosis is caused by a fungal pathogen that invades the host lungs, causing respiratory distress. In 2019, 20,003 cases of Valley fever were reported to the CDC. However, this number likely vastly underrepresents the true number of Valley fever cases, as many go undetected due to poor testing strategies and a lack of diagnostic models. Valley fever is also often misdiagnosed as bacterial pneumonia, resulting in 60%-80% of patients being treated with antibiotics prior to an accurate diagnosis. Misdiagnosis contributes to a growing problem of antibiotic resistance and antibiotic-induced microbiome dysbiosis; the implications for disease outcomes are currently unknown. About 5%-10% of symptomatic Valley fever patients develop chronic pulmonary disease. Valley fever causes a significant financial burden and a reduced quality of life. Little is known regarding what factors contribute to the development of chronic infections and treatments for the disease are limited., Competing Interests: The authors declare no conflict of interest.
- Published
- 2024
- Full Text
- View/download PDF
41. Host tracheal and intestinal microbiomes inhibit Coccidioides growth in vitro .
- Author
-
Tejeda-Garibay S, Zhao L, Hum NR, Pimentel M, Diep AL, Amiri B, Sindi SS, Weilhammer DR, Loots GG, and Hoyer KK
- Abstract
Coccidioidomycosis, also known as Valley fever, is a disease caused by the fungal pathogen Coccidioides . Unfortunately, patients are often misdiagnosed with bacterial pneumonia leading to inappropriate antibiotic treatment. Soil bacteria B. subtilis -like species exhibits antagonistic properties against Coccidioides in vitro ; however, the antagonistic capabilities of host microbiota against Coccidioides are unexplored. We sought to examine the potential of the tracheal and intestinal microbiomes to inhibit the growth of Coccidioides in vitro . We hypothesized that an uninterrupted lawn of microbiota obtained from antibiotic-free mice would inhibit the growth of Coccidioides while partial in vitro depletion through antibiotic disk diffusion assays would allow a niche for fungal growth. We observed that the microbiota grown on 2xGYE (GYE) and CNA w/ 5% sheep's blood agar (5%SB-CNA) inhibited the growth of Coccidioides , but that grown on chocolate agar does not. Partial depletion of the microbiota through antibiotic disk diffusion revealed that microbiota depletion leads to diminished inhibition and comparable growth of Coccidioides growth to controls. To characterize the bacteria grown and narrow down potential candidates contributing to the inhibition of Coccidioides , 16s rRNA sequencing of tracheal and intestinal agar cultures and murine lung extracts was performed. The identity of host bacteria that may be responsible for this inhibition was revealed. The results of this study demonstrate the potential of the host microbiota to inhibit the growth of Coccidioides in vitro and suggest that an altered microbiome through antibiotic treatment could negatively impact effective fungal clearance and allow a niche for fungal growth in vivo .
- Published
- 2023
- Full Text
- View/download PDF
42. Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention.
- Author
-
Davalos OA, Heydari AA, Fertig EJ, Sindi SS, and Hoyer KK
- Abstract
A limitation of current deep learning (DL) approaches for single-cell RNA sequencing (scRNAseq) analysis is the lack of interpretability. Moreover, existing pipelines are designed and trained for specific tasks used disjointly for different stages of analysis. We present scANNA, a novel interpretable DL model for scRNAseq studies that leverages neural attention to learn gene associations. After training, the learned gene importance (interpretability) is used to perform downstream analyses (e.g., global marker selection and cell-type classification) without retraining. ScANNA's performance is comparable to or better than state-of-the-art methods designed and trained for specific standard scRNAseq analyses even though scANNA was not trained for these tasks explicitly. ScANNA enables researchers to discover meaningful results without extensive prior knowledge or training separate task-specific models, saving time and enhancing scRNAseq analyses., Competing Interests: Competing interests E.J.F is on the Scientific Advisory Board for Resistance-Bio and Viosera Therapeutics, and a paid consultant for Mestag Therapeutics and Merck. Other authors declare no competing interests.
- Published
- 2023
- Full Text
- View/download PDF
43. Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing.
- Author
-
Heydari AA and Sindi SS
- Abstract
Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. Data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially resolved data (such as spatial resolution, sensitivity, and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering, among others. However, DL models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially resolved transcriptomics while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight domains in which we anticipate transformational DL applications., Competing Interests: The authors have no conflicts to disclose., (© 2023 Author(s).)
- Published
- 2023
- Full Text
- View/download PDF
44. Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow.
- Author
-
Qasim MN, Valle Arevalo A, Paropkari AD, Ennis CL, Sindi SS, Nobile CJ, and Hernday AD
- Subjects
- DNA metabolism, Data Analysis, Endonucleases, High-Throughput Nucleotide Sequencing, Humans, Workflow, Candida albicans genetics, Candida albicans metabolism, Transcription Factors genetics, Transcription Factors metabolism
- Abstract
Regulatory transcription factors control many important biological processes, including cellular differentiation, responses to environmental perturbations and stresses, and host-pathogen interactions. Determining the genome-wide binding of regulatory transcription factors to DNA is essential to understanding the function of transcription factors in these often complex biological processes. Cleavage under targets and release using nuclease (CUT&RUN) is a modern method for genome-wide mapping of in vivo protein-DNA binding interactions that is an attractive alternative to the traditional and widely used chromatin immunoprecipitation followed by sequencing (ChIP-seq) method. CUT&RUN is amenable to a higher-throughput experimental setup and has a substantially higher dynamic range with lower per-sample sequencing costs than ChIP-seq. Here, a comprehensive CUT&RUN protocol and accompanying data analysis workflow tailored for genome-wide analysis of transcription factor-DNA binding interactions in the human fungal pathogen Candida albicans are described. This detailed protocol includes all necessary experimental procedures, from epitope tagging of transcription factor-coding genes to library preparation for sequencing; additionally, it includes a customized computational workflow for CUT&RUN data analysis.
- Published
- 2022
- Full Text
- View/download PDF
45. Rules of Engagement: A Guide to Developing Agent-Based Models.
- Author
-
Griesemer M and Sindi SS
- Subjects
- Animals, Humans, Models, Biological
- Abstract
Agent-based models (ABM), also called individual-based models, first appeared several decades ago with the promise of nearly real-time simulations of active, autonomous individuals such as animals or objects. The goal of ABMs is to represent a population of individuals (agents) interacting with one another and their environment. Because of their flexible framework, ABMs have been widely applied to study systems in engineering, economics, ecology, and biology. This chapter is intended to guide the users in the development of an agent-based model by discussing conceptual issues, implementation, and pitfalls of ABMs from first principles. As a case study, we consider an ABM of the multi-scale dynamics of cellular interactions in a microbial community. We develop a lattice-free agent-based model of individual cells whose actions of growth, movement, and division are influenced by both their individual processes (cell cycle) and their contact with other cells (adhesion and contact inhibition)., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature.)
- Published
- 2022
- Full Text
- View/download PDF
46. AddTag, a two-step approach with supporting software package that facilitates CRISPR/Cas-mediated precision genome editing.
- Author
-
Seher TD, Nguyen N, Ramos D, Bapat P, Nobile CJ, Sindi SS, and Hernday AD
- Subjects
- Genetic Engineering, Genomics, Software, CRISPR-Cas Systems, Gene Editing
- Abstract
CRISPR/Cas-induced genome editing is a powerful tool for genetic engineering, however, targeting constraints limit which loci are editable with this method. Since the length of a DNA sequence impacts the likelihood it overlaps a unique target site, precision editing of small genomic features with CRISPR/Cas remains an obstacle. We introduce a two-step genome editing strategy that virtually eliminates CRISPR/Cas targeting constraints and facilitates precision genome editing of elements as short as a single base-pair at virtually any locus in any organism that supports CRISPR/Cas-induced genome editing. Our two-step approach first replaces the locus of interest with an "AddTag" sequence, which is subsequently replaced with any engineered sequence, and thus circumvents the need for direct overlap with a unique CRISPR/Cas target site. In this study, we demonstrate the feasibility of our approach by editing transcription factor binding sites within Candida albicans that could not be targeted directly using the traditional gene-editing approach. We also demonstrate the utility of the AddTag approach for combinatorial genome editing and gene complementation analysis, and we present a software package that automates the design of AddTag editing., (© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.)
- Published
- 2021
- Full Text
- View/download PDF
47. A unifying model for the propagation of prion proteins in yeast brings insight into the [PSI+] prion.
- Author
-
Lemarre P, Pujo-Menjouet L, and Sindi SS
- Subjects
- Amyloid chemistry, Computer Simulation, Guanidine pharmacology, Heat-Shock Proteins metabolism, Kinetics, Models, Biological, Models, Theoretical, Peptide Termination Factors metabolism, Phenotype, Saccharomyces cerevisiae Proteins metabolism, Prion Proteins metabolism, Saccharomyces cerevisiae metabolism
- Abstract
The use of yeast systems to study the propagation of prions and amyloids has emerged as a crucial aspect of the global endeavor to understand those mechanisms. Yeast prion systems are intrinsically multi-scale: the molecular chemical processes are indeed coupled to the cellular processes of cell growth and division to influence phenotypical traits, observable at the scale of colonies. We introduce a novel modeling framework to tackle this difficulty using impulsive differential equations. We apply this approach to the [PSI+] yeast prion, which is associated with the misconformation and aggregation of Sup35. We build a model that reproduces and unifies previously conflicting experimental observations on [PSI+] and thus sheds light onto characteristics of the intracellular molecular processes driving aggregate replication. In particular our model uncovers a kinetic barrier for aggregate replication at low densities, meaning the change between prion or prion-free phenotype is a bi-stable transition. This result is based on the study of prion curing experiments, as well as the phenomenon of colony sectoring, a phenotype which is often ignored in experimental assays and has never been modeled. Furthermore, our results provide further insight into the effect of guanidine hydrochloride (GdnHCl) on Sup35 aggregates. To qualitatively reproduce the GdnHCl curing experiment, aggregate replication must not be completely inhibited, which suggests the existence of a mechanism different than Hsp104-mediated fragmentation. Those results are promising for further development of the [PSI+] model, but also for extending the use of this novel framework to other yeast prion or amyloid systems., Competing Interests: The authors have declared that no competing interests exist.
- Published
- 2020
- Full Text
- View/download PDF
48. A Discrete-Time Branching Process Model of Yeast Prion Curing Curves.
- Author
-
Sindi SS and Olofsson P
- Abstract
The infectious agent of many neurodegenerative disorders is thought to be aggregates of prion protein, which are transmitted between cells. Recent work in yeast supports this hypothesis, but suggests that only aggregates below a critical size are transmitted efficiently. The total number of transmissible aggregates in a typical cell is a key determinant of strain infectivity. In a discrete-time branching process model of a yeast colony with prions, prion aggregates increase in size according to a Poisson process and only aggregates below a threshold size are transmitted during cell division. The total number of cells with aggregates in a growing population of yeast is expressed.
- Published
- 2013
- Full Text
- View/download PDF
49. Identification and frequency estimation of inversion polymorphisms from haplotype data.
- Author
-
Sindi SS and Raphael BJ
- Subjects
- Base Pairing genetics, Chi-Square Distribution, Chromosome Breakage, Computational Biology, Computer Simulation, Humans, Chromosome Inversion genetics, Databases, Genetic, Haplotypes genetics, Polymorphism, Single Nucleotide genetics
- Abstract
Structural rearrangements, including copy-number alterations and inversions, are increasingly recognized as an important contributor to human genetic variation. Copy number variants are readily measured via array-based techniques like comparative genomic hybridization, but copy-neutral variants such as inversion polymorphisms remain difficult to identify without whole genome sequencing. We introduce a method to identify inversion polymorphisms and estimate their frequency in a population using readily available single nucleotide polymorphism (SNP) data. Our method uses a probabilistic model to describe a population as a mixture of forward and inverted chromosomes and identifies putative inversions by characteristic differences in haplotype frequencies around inversion breakpoints. On simulated data, our method accurately predicts inversions with frequencies as low as 25% in the population and reliably estimates inversion frequencies over a wide range. On the human HapMap Phase 2 data, we predict between 88 and 142 inversion polymorphisms with frequency ranging from 20 to 81 percent. Many of these correspond to known inversions or have other evidence supporting them, and the predicted inversion frequencies largely agree with the limited information presently available.
- Published
- 2010
- Full Text
- View/download PDF
50. Duplication count distributions in DNA sequences.
- Author
-
Sindi SS, Hunt BR, and Yorke JA
- Subjects
- Animals, Base Sequence, Biophysical Phenomena, Chromosomes genetics, Gene Duplication, Genomics, Humans, Markov Chains, Models, Chemical, Multigene Family, Repetitive Sequences, Nucleic Acid, DNA chemistry, DNA genetics, Models, Genetic
- Abstract
We study quantitative features of complex repetitive DNA in several genomes by studying sequences that are sufficiently long that they are unlikely to have repeated by chance. For each genome we study, we determine the number of identical copies, the "duplication count," of each sequence of length 40, that is of each "40-mer." We say a 40-mer is "repeated" if its duplication count is at least 2. We focus mainly on "complex" 40-mers, those without short internal repetitions. We find that we can classify most of the complex repeated 40-mers into two categories: one category has its copies clustered closely together on one chromosome, the other has its copies distributed widely across multiple chromosomes. For each genome and each of the categories above, we compute N(c), the number of 40-mers that have duplication count c, for each integer c. In each case, we observe a power-law-like decay in N(c) as c increases from 3 to 50 or higher. In particular, we find that N(c) decays much more slowly than would be predicted by evolutionary models where each 40-mer is equally likely to be duplicated. We also analyze an evolutionary model that does reflect the slow decay of N(c).
- Published
- 2008
- Full Text
- View/download PDF
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