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Results for “Genome scale models”

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PubMed2001

Emerging technologies in yeast genomics.

The genomic revolution is undeniable: in the past year alone, the term 'genomics' was found in nearly 500 research articles, and at least 6 journals are devoted solely to genomic biology. More than just a buzzword, molecular biology has genuinely embraced genomics (the systematic, large-scale study of genomes and their functions). With its facile genetics, the budding yeast Saccharomyces cerevisiae has emerged as an important model organism in the development of many current genomic methodologies. These techniques have greatly influenced the manner in which biology is studied in yeast and in other organisms. In this review, we summarize the most promising technologies in yeast genomics.

Binding Sites↗
PubMed2001

RefSeq and LocusLink: NCBI gene-centered resources.

Thousands of genes have been painstakingly identified and characterized a few genes at a time. Many thousands more are being predicted by large scale cDNA and genomic sequencing projects, with levels of evidence ranging from supporting mRNA sequence and comparative genomics to computing ab initio models. This, coupled with the burgeoning scientific literature, makes it critical to have a comprehensive directory for genes and reference sequences for key genomes. The NCBI provides two resources, LocusLink and RefSeq, to meet these needs. LocusLink organizes information around genes to generate a central hub for accessing gene-specific information for fruit fly, human, mouse, rat and zebrafish. RefSeq provides reference sequence standards for genomes, transcripts and proteins; human, mouse and rat mRNA RefSeqs, and their corresponding proteins, are discussed here. Together, RefSeq and LocusLink provide a non-redundant view of genes and other loci to support research on genes and gene families, variation, gene expression and genome annotation. Additional information about LocusLink and RefSeq is available at http://www.ncbi.nlm.nih.gov/LocusLink/.

Animals↗
PubMed2003

Does selection mold molecular networks?

The dissection of molecular networks vital to cellular life can provide important hints about optimal network design principles. However, these hints can become conclusive only if one can determine that natural selection has molded a network's structure. I illustrate the importance of thorough studies of network evolution with two recent examples, one from genome-scale networks, the other from small transcriptional regulation circuits.

Animals↗
PubMed2004

Hotspots of mammalian chromosomal evolution.

BACKGROUND: Chromosomal evolution is thought to occur through a random process of breakage and rearrangement that leads to karyotype differences and disruption of gene order. With the availability of both the human and mouse genomic sequences, detailed analysis of the sequence properties underlying these breakpoints is now possible. RESULTS: We report an abundance of primate-specific segmental duplications at the breakpoints of syntenic blocks in the human genome. Using conservative criteria, we find that 25% (122/461) of all breakpoints contain > or = 10 kb of duplicated sequence. This association is highly significant (p < 0.0001) when compared to a simulated random-breakage model. The significance is robust under a variety of parameters, multiple sets of conserved synteny data, and for orthologous breakpoints between and within chromosomes. A comparison of mouse lineage-specific breakpoints since the divergence of rat and mouse showed a similar association with regions associated with segmental duplications in the primate genome. CONCLUSION: These results indicate that segmental duplications are associated with syntenic rearrangements, even when pericentromeric and subtelomeric regions are excluded. However, segmental duplications are not necessarily the cause of the rearrangements. Rather, our analysis supports a nonrandom model of chromosomal evolution that implicates specific regions within the mammalian genome as having been predisposed to both recurrent small-scale duplication and large-scale evolutionary rearrangements.

Animals↗
PubMed2026

A stratified urine-based molecular diagnostic and prognostic model for non-muscle-invasive bladder cancer management.

BACKGROUND: Non-muscle-invasive bladder cancer (NMIBC) is characterized by a high recurrence rate requiring lifelong cystoscopic surveillance. Existing urine-based molecular assays mainly rely on mutations or methylation, which fail to capture large-scale genomic instability. Copy number variation (CNV) profiling offers complementary information on tumor evolution and aggressiveness, but its application in urinary diagnosis remains limited. We aimed to integrate CNV and DNA methylation signals from urinary DNA to establish a noninvasive and biologically informed stratified diagnostic model for NMIBC recurrence surveillance and risk stratification. METHODS: Urine samples were prospectively collected from 91 patients (75 evaluable) between June 2021 and August 2023. Shallow whole-genome sequencing (sWGS) was used to detect CNVs at chromosomal arm and focal gene levels, while ONECUT2 promoter methylation was quantified by qPCR. Diagnostic and prognostic performance was evaluated by ROC analysis, Kaplan-Meier survival, and stratified recurrence assessment. RESULTS: We evaluated a stratified diagnostic model combining CNV and ONECUT2 methylation testing in a cohort of 79 patients. CNV analysis alone showed high specificity (0.923) for NMIBC diagnosis. A combined model, using CNV as an initial screen followed by ONECUT2 methylation testing in CNV-positive cases, achieved a sensitivity of 0.783, specificity of 0.981, and a negative predictive value (NPV) of 0.911. This approach reduced the number of required ONECUT2 tests by 35% and identified a high proportion of true-negative patients (98.1%), which may help reduce unnecessary cystoscopy procedures. The model also demonstrated significant prognostic value, with the molecularly defined high-risk group showing significantly shorter recurrence-free survival (RFS) than the low-risk group (median RFS: 4.33 months vs. not reached; p&#x2009;<&#x2009;0.001). Additional, in patients with initially negative cystoscopy after urine sample collection, the model demonstrated a predictive accuracy of 0.922 for recurrence, with molecular positivity observed a median of 9.6 months prior to clinical diagnosis. CONCLUSIONS: Integrating CNV and DNA methylation profiling from urinary DNA provides a powerful and noninvasive molecular framework for NMIBC surveillance. By combining early epigenetic changes with genomic instability signals, this approach enhances recurrence risk assessment and enables earlier detection compared with conventional cystoscopy. It offers a practical route toward personalized and adaptive post-treatment monitoring of NMIBC. TRIAL REGISTRATION: NCT04994197.

Humans↗
PubMed2005

A physical model for the condensation and decondensation of eukaryotic chromosomes.

During the eukaryotic cell cycle, chromatin undergoes several conformational changes, which are believed to play key roles in gene expression regulation during interphase, and in genome replication and division during mitosis. In this paper, we propose a scenario for chromatin structural reorganization during mitosis, which bridges all the different scales involved in chromatin architecture, from nucleosomes to chromatin loops. We build a model for chromatin, based on available data, taking into account both physical and topological constraints DNA has to deal with. Our results suggest that the mitotic chromosome condensation/decondensation process is induced by a structural change at the level of the nucleosome itself.

Cell Cycle↗
PubMed2003

Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling.

While the fundamental building blocks of biology are being tabulated by the various genome projects, microarray technology is setting the stage for the task of deducing the connectivity of large-scale gene networks. We show how the perturbation of carefully chosen genes in a microarray experiment can be used in conjunction with a reverse engineering algorithm to reveal the architecture of an underlying gene regulatory network. Our iterative scheme identifies the network topology by analyzing the steady-state changes in gene expression resulting from the systematic perturbation of a particular node in the network. We highlight the validity of our reverse engineering approach through the successful deduction of the topology of a linear in numero gene network and a recently reported model for the segmentation polarity network in Drosophila melanogaster. Our method may prove useful in identifying and validating specific drug targets and in deconvolving the effects of chemical compounds.

Algorithms↗
PubMed2002

In silico approaches to mechanistic and predictive toxicology: an introduction to bioinformatics for toxicologists.

Bioinformatics, or in silico biology, is a rapidly growing field that encompasses the theory and application of computational approaches to model, predict, and explain biological function at the molecular level. This information rich field requires new skills and new understanding of genome-scale studies in order to take advantage of the rapidly increasing amount of sequence, expression, and structure information in public and private databases. Toxicologists are poised to take advantage of the large public databases in an effort to decipher the molecular basis of toxicity. With the advent of high-throughput sequencing and computational methodologies, expressed sequences can be rapidly detected and quantitated in target tissues by database searching. Novel genes can also be isolated in silico, while their function can be predicted and characterized by virtue of sequence homology to other known proteins. Genomic DNA sequence data can be exploited to predict target genes and their modes of regulation, as well as identify susceptible genotypes based on single nucleotide polymorphism data. In addition, highly parallel gene expression profiling technologies will allow toxicologists to mine large databases of gene expression data to discover molecular biomarkers and other diagnostic and prognostic genes or expression profiles. This review serves to introduce to toxicologists the concepts of in silico biology most relevant to mechanistic and predictive toxicology, while highlighting the applicability of in silico methods using select examples.

Cluster Analysis↗
PubMed2004

RNA interference technologies for understanding and treating neurodegenerative diseases.

RNA interference (RNAi) is an evolutionarily conserved process that silences gene expression through double-stranded RNA species in a sequence-specific manner. With the completion of genome sequencing in multiple organisms, RNAi provides an efficient reverse genetics tool to reveal gene functions on a genome-wide scale. Conditional/inducible RNAi offers a new way to analyze gene function at different developmental stages and to create a new generation of animal models of human diseases. The sequence-specificity of RNAi and the fact that it is a naturally occurring process in human make it an excellent therapeutic tool for a wide range of diseases. This article provides a brief review of the current understandings of the mechanism of RNAi and its application to the nervous system, with particular focus on its application to understand mechanisms of neurodegenerative diseases. The prospects of the application of RNAi in clinical setting to treat these devastating diseases will also be presented.

Animals↗
PubMed2005

Hierarchical structure analysis describing abnormal base composition of genomes.

Abnormal base compositional patterns of genomic DNA sequences are studied in the framework of a hierarchical structure (HS) model originally proposed for the study of fully developed turbulence [She and Lévêque, Phys. Rev. Lett. 72, 336 (1994)]. The HS similarity law is verified over scales between 10(3)bp and 10(5)bp, and the HS parameter beta is proposed to describe the degree of heterogeneity in the base composition patterns. More than one hundred bacteria, archaea, virus, yeast, and human genome sequences have been analyzed and the results show that the HS analysis efficiently captures abnormal base composition patterns, and the parameter beta is a characteristic measure of the genome. Detailed examination of the values of beta reveals an intriguing link to the evolutionary events of genetic material transfer. Finally, a sequence complexity (S) measure is proposed to characterize gradual increase of organizational complexity of the genome during the evolution. The present study raises several interesting issues in the evolutionary history of genomes.

Animals↗
PubMed2003

Molecular and evolutionary analysis of the growth-controlling region on the human Y chromosome.

The human Y chromosome has been predicted to harbour a locus termed GCY, affecting height in males. GCY has been positioned by deletion mapping to the pericentromeric region on the long arm of the Y chromosome. As the relevant gene has not been identified yet, we have carried out exon amplification and isolated nine different exon trap clones within the critical region. Gene prediction programs have proposed 17 different gene models and standard BLASTN searches with the genomic sequence detected significant homologies to six known genes/pseudogenes or expressed sequence tags. Large-scale cDNA library screening and reverse transcription of polyA(+) RNAs, however, could not demonstrate unequivocally the existence of a novel transcriptional unit. All potential transcriptional units are embedded in subintervals of the GCY critical region that have been transposed to the human Y during different stages of primate evolution. These results challenge our present view on the Y-chromosomal stature locus GCY, proposing the existence of an unusual gene with an extremely confined spatial and/or temporal expression pattern, albeit the structural impact of the nearby pericentromeric heterochromatin should not be excluded.

Body Height↗
PubMed2001

Large-scale production of mouse phenotypes: the search for animal models for inherited diseases in humans.

This paper is aimed principally at bioinformaticians and biologists as an introduction to recent advances in mouse mutagenesis, concentrating on genome-wide screens utilising the powerful mutagen N-ethyl-N-nitroso-urea (ENU). It contains a brief background to the underlying genetics as well as details of the practical aspects of organisation and data capture for such projects.

Animals↗
PubMed2001

Monte Carlo predictions of DNA fragment-size distributions for large sizes after HZE particle irradiation.

DSBs (double-strand breaks) produced by densely ionizing space radiation are not located randomly in the genome: recent data indicate DSB clustering along chromosomes. DSB clustering at large scales, from >100 Mbp down to approximately 2 kbp, is modeled using a Monte-Carlo algorithm. A random-walk model of chromatin is combined with a track model, that predicts the radial distribution of energy from an ion, and the RLC (randomly-located-clusters) formalism, in software called DNAbreak. This model generalizes the random-breakage model, whose broken-stick fragment-size distribution is applicable to low-LET radiation. DSB induction due to track interaction with the DNA volume depends on the radiation quality parameter Q. This dose-independent parameter depends only weakly on LET. Multi-track, high-dose effects depend on the cluster intensity parameter lambda, proportional to fluence as defined by the RLC formalism. After lambda is determined by a numerical experiment, the model reduces to one adjustable parameter Q. The best numerical fits to the experimental data, determining Q, are obtained. The knowledge of lambda and Q allows us to give biophysically based extrapolations of high-dose DNA fragment-size data to low doses or to high LETs.

Algorithms↗
PubMed2000

Zebrafish: bridging the gap between development and disease.

The zebrafish has been the model of choice amongst developmental biologists for many years. This small freshwater species offers many advantages to the study of organ and tissue development that are not provided by other model systems. Against this background, modern molecular genetic approaches are being applied to expand the physical and genetic mapping of the zebrafish genome. These approaches complement the large-scale mutagenic screens that have led to the isolation of mutant phenotypes. Some of the phenotypes have been found to resemble human disease states, while mapping and sequencing data have revealed zebrafish genes with significant homology to human disease-causing genes. It is the realization that the zebrafish offers an amenable system for understanding disease, as opposed to development, that underpins this review. The adventitious identification of disease phenotypes amongst zebrafish mutants and the important area of deliberate disease modelling using transgenesis and gene targeting should lead to a better application of the zebrafish as a vertebrate model of human diseases.

Animals↗
PubMed2006

Solution NMR in structural genomics.

Structural genomics (also known as structural proteomics) aims to generate accurate three-dimensional models for all folded, globular proteins and domains in the protein universe to understand the relationship between protein sequence, structure and function. NMR spectroscopy of small (<20 kDa) proteins has been used successfully within several large-scale structural genomics projects for more than six years now. Recent advances coming from traditional NMR structural biology laboratories as well as large scale centers and consortia using NMR for structural genomics promise to facilitate NMR analysis making it even a more efficient and increasingly automated procedure.

Animals↗
PubMed2002

High-density genotyping and linkage disequilibrium in the human genome using chromosome 22 as a model.

Technology and genetics have advanced to the point where genotyping thousands of individuals at thousands of marker locations around the whole human genome is possible. The whole-genome scan for detection of complex disease genes is a widely discussed topic. We review some of the recent high-density genotyping experiments and discuss related details, particularly the extent and variability of linkage disequilibrium. We also discuss the quality of single nucleotide polymorphisms (SNPs) in public databases and its consequences to the number of SNPs required for large-scale genotyping projects.

Chromosomes, Human, Pair 22↗
PubMed2000

Medakafish as a model system for vertebrate developmental genetics.

Several teleosts, such as the zebrafish and the medakafish or medaka (Oryzias latipes), are used as vertebrate model systems in various fields of biology. The medaka is suitable for use in genomic studies because of its small genome size. Moreover, our recent results of small-scale mutagenesis in the medaka indicate that it is possible to identify mutations, the phenotypes of which could not be found in zebrafish mutants obtained by large-scale mutagenesis. An example is Oot (One-sided optic tectum), a maternal-effect mutation. In the Oot phenotype, bilateral symmetry is broken in the optic tectum in the early developmental stages, and either the left or right morphology is duplicated on both sides. Medaka inbred strains can be produced and used to study quantitative traits in vertebrate development. Data presented support the use of medaka as another important fish model for the study of vertebrate developmental genetics.

Animals↗
PubMed2004

Elucidation and structural analysis of conserved pools for genome-scale metabolic reconstructions.

In this article, we introduce metabolite concentration coupling analysis (MCCA) to study conservation relationships for metabolite concentrations in genome-scale metabolic networks. The analysis allows the global identification of subsets of metabolites whose concentrations are always coupled within common conserved pools. Also, the minimal conserved pool identification (MCPI) procedure is developed for elucidating conserved pools for targeted metabolites without computing the entire basis conservation relationships. The approaches are demonstrated on genome-scale metabolic reconstructions of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae. Despite significant differences in the size and complexity of the examined organism's models, we find that the concentrations of nearly all metabolites are coupled within a relatively small number of subsets. These correspond to the overall exchange of carbon molecules into and out of the networks, interconversion of energy and redox cofactors, and the transfer of nitrogen, sulfur, phosphate, coenzyme A, and acyl carrier protein moieties among metabolites. The presence of large conserved pools can be viewed as global biophysical barriers protecting cellular systems from stresses, maintaining coordinated interconversions between key metabolites, and providing an additional mode of global metabolic regulation. The developed approaches thus provide novel and versatile tools for elucidating coupling relationships between metabolite concentrations with implications in biotechnological and medical applications.

Algorithms↗