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Статті в журналах з теми "Bioinformatics and computational biology not elsewhere classified"
Pons, Joan Carles, David Paez-Espino, Gabriel Riera, Natalia Ivanova, Nikos C. Kyrpides, and Mercè Llabrés. "VPF-Class: taxonomic assignment and host prediction of uncultivated viruses based on viral protein families." Bioinformatics 37, no. 13 (January 20, 2021): 1805–13. http://dx.doi.org/10.1093/bioinformatics/btab026.
Повний текст джерелаXu, Jing, Han Zhang, Jinfang Zheng, Philippe Dovoedo, and Yanbin Yin. "eCAMI: simultaneous classification and motif identification for enzyme annotation." Bioinformatics 36, no. 7 (December 3, 2019): 2068–75. http://dx.doi.org/10.1093/bioinformatics/btz908.
Повний текст джерелаLi, Zhi, Tianyue Zhang, Haojie Lei, Liyan Wei, Yuanning Liu, Yadi Shi, Shuyi Li, et al. "Research on Gastric Cancer’s Drug-resistant Gene Regulatory Network Model." Current Bioinformatics 15, no. 3 (May 23, 2020): 225–34. http://dx.doi.org/10.2174/1574893614666190722102557.
Повний текст джерелаCleemput, Sara, Wim Dumon, Vagner Fonseca, Wasim Abdool Karim, Marta Giovanetti, Luiz Carlos Alcantara, Koen Deforche, and Tulio de Oliveira. "Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes." Bioinformatics 36, no. 11 (February 28, 2020): 3552–55. http://dx.doi.org/10.1093/bioinformatics/btaa145.
Повний текст джерелаPham, Vu V. H., Lin Liu, Cameron P. Bracken, Gregory J. Goodall, Jiuyong Li, and Thuc D. Le. "DriverGroup: a novel method for identifying driver gene groups." Bioinformatics 36, Supplement_2 (December 2020): i583—i591. http://dx.doi.org/10.1093/bioinformatics/btaa797.
Повний текст джерелаMeirson, Tomer, David Bomze, Liron Kahlon, Hava Gil-Henn, and Abraham O. Samson. "A helical lock and key model of polyproline II conformation with SH3." Bioinformatics 36, no. 1 (June 28, 2019): 154–59. http://dx.doi.org/10.1093/bioinformatics/btz527.
Повний текст джерелаXie, Xiaoli, and Yunxiu Zhao. "A 2D Non-degeneracy Graphical Representation of Protein Sequence and Its Applications." Current Bioinformatics 15, no. 7 (December 15, 2020): 758–66. http://dx.doi.org/10.2174/1574893615666200106114337.
Повний текст джерелаWei, Pan, XiaoDong Xie, Ran Wang, JianFeng Zhang, Feng Li, ZhaoPeng Luo, Zhong Wang, MingZhu Wu, Jun Yang, and PeiJian Cao. "Genetic Diversity of Blattella germanica Isolates from Central China based on Mitochondrial Genes." Current Bioinformatics 14, no. 7 (September 17, 2019): 574–80. http://dx.doi.org/10.2174/1574893614666190204153041.
Повний текст джерелаNguyen, Hien Thi, Van Mai Do, Thanh Thuy Phan, and Dung Tam Nguyen Huynh. "The Potential of Ameliorating COVID-19 and Sequelae From Andrographis paniculata via Bioinformatics." Bioinformatics and Biology Insights 17 (January 2023): 117793222211496. http://dx.doi.org/10.1177/11779322221149622.
Повний текст джерелаPournoor, Ehsan, Naser Elmi, and Ali Masoudi-Nejad. "CatbNet: A Multi Network Analyzer for Comparing and Analyzing the Topology of Biological Networks." Current Genomics 20, no. 1 (February 27, 2019): 69–75. http://dx.doi.org/10.2174/1389202919666181213101540.
Повний текст джерелаДисертації з теми "Bioinformatics and computational biology not elsewhere classified"
Lauber, Chris, Barbara Klink, and Michael Seifert. "Comparative analysis of histologically classified oligodendrogliomas reveals characteristic molecular differences between subgroups." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-235567.
Повний текст джерела(8815928), Samantha Jurecki. "APPLICATION AND VALIDATION OF THE EDNA-METABARCODED MIFISH/MITOFISH PIPELINE FOR ASSESSMENT OF NATIVE AND NON-NATIVE FISH COMMUNITIES OF LAKE MICHIGAN." Thesis, 2020.
Знайти повний текст джерела(6848951), Matthew C. Pharris. "Quantitative Models of Calcium-Dependent Protein Signaling in Neuronal Dendritic Spines." Thesis, 2019.
Знайти повний текст джерелаBiochemical signaling at the connections between neurons, called synapses, regulates dynamic shifts in a synapse’s size and connective strength. Called synaptic plasticity, these shifts are initiated by calcium ion (Ca2+) flux into message-receiving structures called dendritic spines. Within dendritic spines, Ca2+ binds sensor proteins such as calmodulin (CaM). Importantly, Ca2+/CaM may bind and activate a wide variety of proteins, which subsequently facilitate signaling pathways regulating the dendritic spine’s size and connective strength.
In this thesis, I use computational models to characterize molecular mechanisms regulating Ca2+-dependent protein signaling within the dendritic spine. Specifically, I explore how Ca2+/CaM differentially activates binding partners and how these binding partners transduce signals downstream. For this, I present deterministic models of Ca2+, CaM, and CaM-dependent proteins, and in analyzing model output I demonstrate in-part that competition for CaM-binding alone may be sufficient to set the Ca2+ frequency-dependence of protein activation. Subsequently, I adapt my deterministic models into particle-based, spatial-stochastic frameworks to quantify how spatial effects influence model output, showing evidence that spatial gradients of Ca2+/CaM may set spatial gradients of activated proteins downstream. Additionally, I incorporate into my models the most detailed model to-date of Ca2+/CaM-dependent protein kinase II (CaMKII), a multi-subunit protein essential to synaptic plasticity. With this detailed model of CaMKII, my analysis suggests that the many subunits of CaMKII provide avidity effects that significantly increase the protein’s effective affinity for binding partners, particularly Ca2+/CaM. Altogether, this thesis provides a detailed analysis of Ca2+-dependent signaling within dendritic spines, characterizing molecular mechanisms that may be useful for the development of novel therapeutics for patients of neurological disorders.
(9757040), Lina M. Aboulmouna. "Towards cybernetic modeling of biological processes in mammalian systems—lipid metabolism in the murine macrophage." Thesis, 2020.
Знайти повний текст джерелаRegulation of metabolism in mammalian cells is achieved through a complex interplay between cellular signaling, metabolic reactions, and transcriptional changes. The modeling of metabolic fluxes in a cell requires the knowledge of all these mechanisms, some of which may be unknown. A cybernetic approach provides a framework to model these complex interactions through the implicit accounting of such regulatory mechanisms, assuming a biological “goal”. The goal-oriented control policies of cybernetic models have been used to predict metabolic phenomena ranging from complex substrate uptake patterns and dynamic metabolic flux distributions to the behavior of gene knockout strains. The premise underlying the cybernetic framework is that the regulatory processes affecting metabolism can be mathematically formulated as a cybernetic objective through variables that constrain the network to achieve a specified biological “goal”.
Cybernetic theory builds on the perspective that regulation is organized towards achieving goals relevant to an organism’s survival or displaying a specific phenotype in response to a stimulus. While cybernetic models have been established by prior work carried out in bacterial systems, we show its applicability to more complex biological systems with a predefined goal. We have modeled eicosanoid, a well-characterized set of inflammatory lipids derived from arachidonic acid, metabolism in mouse bone marrow derived macrophage (BMDM) cells stimulated by Kdo2-Lipid A (KLA, a chemical analogue of Lipopolysaccharide found on the surface of bacterial cells) and adenosine triphosphate (ATP, a danger signal released in response to surrounding cell death) using cybernetic control variables. Here, the cybernetic goal is inflammation; the hallmark of inflammation is the expression of cytokines which act as autocrine signals to stimulate a pro-inflammatory response. Tumor necrosis factor (TNF)-α is an exemplary pro-inflammatory marker and can be designated as a cybernetic objective for modeling eicosanoid—prostaglandin (PG) and leukotriene (LK)—metabolism. Transcriptomic and lipidomic data for eicosanoid biosynthesis and conversion were obtained from the LIPID Maps database. We show that the cybernetic model captures the complex regulation of PG metabolism and provides a reliable description of PG formation using the treatment ATP stimulation. We then validated our model by predicting an independent data set, the PG response of KLA primed ATP stimulated BMDM cells.
The process of inflammation is mediated by the production of multiple cytokines, chemokines, and lipid mediators each of which contribute to specific individual objectives. For such complex processes in mammalian systems, a cybernetic objective based on a single protein/component may not be sufficient to capture all the biological processes thereby necessitating the use of multiple objectives. The choice of the objective function has been made by intuitive considerations in this thesis. If objectives are conjectured, an argument can be made for numerous alternatives. Since regulatory effects are estimated from unregulated kinetics, one encounters the risk of multiplicity in this regard giving rise to multiple models. The best model is of course that which is able to predict a comprehensive set of perturbations. Here, we have extended our above model to also capture the dynamics of LKs. We have used migration as a biological goal for LK using the chemoattractant CCL2 as a key representative molecule describing cell activation leading to an inflammatory response where a goal composed of multiple cybernetic objectives is warranted. Alternative model objectives included relating both branches of the eicosanoid metabolic network to the inflammatory cytokine TNF-α, as well as the simple maximization of all metabolic products such that each equally contributes to the inflammatory system outcome. We were again able to show that all three cybernetic objectives describing the LK and PG branches for eicosanoid metabolism capture the complex regulation and provide a reliable description of eicosanoid formation. We performed simulated drug and gene perturbation analyses on the system to identify differences between the models and propose additional experiments to select the best cybernetic model.
The advantage to using cybernetic modeling is in its ability to capture system behavior without the same level of detail required for these interactions as standard kinetic modeling. Given the complexity of mammalian systems, the cybernetic goal for mammalian cells may not be based solely on survival or growth but on specific context dependent cellular responses. In this thesis, we have laid the groundwork for the application of cybernetic modeling in complex mammalian systems through a specific example case of eicosanoid metabolism in BMDM cells, illustrated the case for multiple objectives, and highlighted the extensibility of the cybernetic framework to other complex biological systems.
(9452786), Elia G. Farah. "IDENTIFYING AND TARGETING PATHWAYS INVOLVED IN ENZALUTAMIDE-RESISTANT PROSTATE CANCER." Thesis, 2020.
Знайти повний текст джерелаProstate cancer is the second leading cause of cancer death among men in the United States. The androgen receptor (AR) antagonist enzalutamide is an FDA-approved drug for treatment of patients with late-stage prostate cancer and is currently under clinical study for early-stage prostate cancer treatment. After a short positive response period to enzalutamide, tumors will develop drug resistance. In these studies, we uncovered that NOTCH signaling and DNA methylation are a deregulated in enzalutamide-resistant cells. NOTCH2 and c-MYC gene expression positively correlated with AR expression in samples from patients with hormone refractory disease in which AR expression levels correspond to those typically observed in enzalutamide-resistance. The expression of Notch signaling components was upregulated in enzalutamide-resistant cells suggesting the activation of the pathway. Inhibition of this pathway in vitro and in vivo promoted an increase in the sensitivity to enzalutamide with an impact on AR expression. On the other hand, DNMT activity and DNMT3B expression were upregulated in resistant lines. Enzalutamide induced the expression of DNMT3A and DNMT3B in prostate cancer cells with a potential role for p53 and pRB in this process. The overexpression of DNMT3B3, a DNMT3B variant, promoted an enzalutamide-resistant phenotype in C4-2 cells. DNA methylation inhibition, using low-concentration decitabine, and DNMT3B knockdown induced a re-sensitization of resistant prostate cancer cells and tumors to enzalutamide. Decitabine treatment in enzalutamide-resistant induced a decrease in the expression of AR-V7 and changes in genes from the apoptosis, DNA repair and mRNA splicing pathways. Decitabine plus enzalutamide treatment of 22RV1 xenografts induced a decrease in tumor weight, KI-67 and AR-V7 expression and an increase in Cleaved-Caspase3 levels. All the above suggest that Notch signaling and DNA methylation pathways are deregulated after enzalutamide resistance onset, and targeting these pathways restores the sensitivity to enzalutamide.
(6634556), Longyun Guo. "Mathematical modeling of phenylalanine and lignin biosynthetic networks in plants." Thesis, 2019.
Знайти повний текст джерела(6714896), Xiangying Mao. "INVESTIGATING THE FUNCTIONAL ROLE OF MED5 AND CDK8 IN ARABIDOPSIS MEDIATOR COMPLEX." Thesis, 2019.
Знайти повний текст джерелаThe Mediator (Med) complex comprises about 30 subunits and is a transcriptional co-regulator in eukaryotic systems. The core Mediator complex, consisting of the head, middle and tail modules, functions as a bridge between transcription factors and basal transcription machinery, whereas the CDK8 kinase module can attenuate Mediator’s ability to function as either a co-activator or co-repressor. Many Arabidopsis Mediator subunit has been functionally characterized, which reveals critical roles of Mediator in many aspects of plant growth and development, responses to biotic and abiotic stimuli, and metabolic homeostasis. Traditional genetic and biochemical approaches laid the foundation for our understanding of Mediator function, but recent transcriptomic and metabolomic studies have provided deeper insights into how specific subunits cooperate in the regulation of plant metabolism. In Chapter 1, we highlight recent developments in the investigation of Mediator and plant metabolism, with emphasis on the large-scale biology studies of med mutants.
We previously found that MED5, an Arabidopsis Mediator tail subunit, is required for maintaining phenylpropanoid homeostasis. A semi-dominant mutation (reduced epidermal fluorescence 4-3, ref4-3) that causes a single amino acid substitution in MED5b functions as a strong suppressor of the pathway, leading to decreased soluble phenylpropanoid accumulation, reduced lignin content and dwarfism. In contrast, loss of MED5a and MED5b (med5) results in increased levels of phenylpropanoids. In Chapter 2, we present our finding that ref4-3 requires CDK8, a Mediator kinase module subunit, to repress plant growth even though the repression of phenylpropanoid metabolism in ref4-3 is CDK8-independent. Transcriptome profiling revealed that salicylic acid (SA) biosynthesis genes are up-regulated in a CDK8-dependent manner in ref4-3, resulting in hyper-accumulation of SA and up-regulation of SA response genes. Both growth repression and hyper-accumulation of SA in ref4-3 require CDK8 with intact kinase activity, but these SA phenotypes are not connected with dwarfing. In contrast, mRNA-sequencing (RNA-seq) analysis revealed the up-regulation of a DNA J protein-encoding gene in ref4-3, the elimination of which partially suppresses dwarfing. Together, our study reveals genetic interactions between Mediator tail and kinase module subunits and enhances our understanding of dwarfing in phenylpropanoid pathway mutants.
In Chapter 3, we characterize other phenotypes of med5 and ref4-3, and find that in addition to the up-regulated phenylpropanoid metabolism, med5 show other interesting phenotypes including hypocotyl and petiole elongation as well as accelerated flowering, all of which are known collectively as the shade avoidance syndrome (SAS), suggesting that MED5 antagonize shade avoidance in wild-type plants. In contrast, the constitutive ref4-3 mutant protein inhibits the process, and the stunted growth of ref4-3 mutants is substantially alleviated by the light treatment that triggers SAS. Moreover, ref4-3 mimics the loss-of-function med5 mutants in maintaining abscisic acid (ABA) levels under both normal and drought growth conditions. The phenotypic characterization of med5 mutants extend our understanding of the role of Mediator in SAS and ABA signaling, providing further insight into the physiological and metabolic responses that require MED5.
In Chapter 4, we explore the function of MED5 and CDK8 in gene expression regulation by investigating the effect of mutations in Mediator including med5, ref4-3, cdk8-1 and ref4-3 cdk8-1 on genome-wide Pol II distribution. We find that loss of MED5 results in loss of Pol II occupancy at many target genes. In contrast, many genes show enriched Pol II levels in ref4-3, some of which overlap with those showing reduced Pol II occupancy in med5. In addition, Pol II occupancy is significantly reduced when CDK8 is disrupted in ref4-3. Our results help to narrow down the direct gene targets of MED5 and identify genes that may be closely related to the growth deficiency observed in ref4-3 plants, providing a critical foundation to elucidate the molecular function of Mediator in transcription regulation.
(6635906), Erin L. Sorlien. "The Chromatin Remodeler and Tumor Suppress Chd5 Promotes Expression and Processing of Transcripts During Development of the Zebrafish Neural System." Thesis, 2019.
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