Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Genome scale models”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 379 records · Page 21Linked to original sources
PubMed2004

Thrombin peptide (TP508) promotes fracture repair by up-regulating inflammatory mediators, early growth factors, and increasing angiogenesis.

Previous studies have shown that a single injection of thrombin peptide (TP508) accelerates fracture repair in a closed rat femoral fracture model. The present study was conducted to elucidate the molecular mechanisms of TP508 action using Affymetrix genome-scale profiling and to link early gene expression changes to fracture histology and bone strength changes. Treatment of femoral fractures with TP508 accelerated fracture repair as determined by destructive torsion testing. Blinded histological analysis demonstrated that TP508-treated fracture callus had a significant increase in blood vessels relative to the controls. Gene array analysis showed that TP508 significantly induced expression of early growth factors, inflammatory response modifiers, and angiogenesis-related genes. This study therefore suggests that TP508 promotes fracture repair through a mechanism that involves an increased induction of a number of growth factors, enhanced expression of inflammatory mediators, and angiogenesis-related genes.

Animals↗
PubMed2005

Comparative genomics of Gossypium and Arabidopsis: unraveling the consequences of both ancient and recent polyploidy.

Both ancient and recent polyploidy, together with post-polyploidization loss of many duplicated gene copies, complicates angiosperm comparative genomics. To explore an approach by which these challenges might be mitigated, genetic maps of extant diploid and tetraploid cottons (Gossypium spp.) were used to infer the approximate order of 3016 loci along the chromosomes of their hypothetical common ancestor. The inferred Gossypium gene order corresponded more closely than the original maps did to a similarly inferred ancestral gene order predating an independent paleopolyploidization (alpha) in Arabidopsis. At least 59% of the cotton map and 53% of the Arabidopsis transcriptome showed correspondence in multilocus gene arrangements based on one or both of two software packages (CrimeStatII, FISH). Genomic regions in which chromosome structural rearrangement has been rapid (obscuring gene order correspondence) have also been subject to greater divergence of individual gene sequences. About 26%-44% of corresponding regions involved multiple Arabidopsis or cotton chromosomes, in some cases consistent with known, more ancient, duplications. The genomic distributions of multiple-locus probes provided early insight into the consequences for chromosome structure of an ancient large-scale duplication in cotton. Inferences that mitigate the consequences of ancient duplications improve leveraging of genomic information for model organisms in the study of more complex genomes.

Arabidopsis↗
PubMed1993

Characterization of the pufferfish (Fugu) genome as a compact model vertebrate genome.

Cloning and sequencing techniques now allow us to characterize genes directly instead of having to deduce their properties from their effects. This new genetics reaches its apotheosis in the plan to obtain the complete DNA sequence of the human genome, but this is far beyond the capacity of present sequencing methods. Small 'model' genomes, 'such as those of Escherichia coli (4.7 megabases (Mb) and yeast (14 Mb), or even those of Caenorhabditis elegans (100 Mb) and Drosophila (165 Mb), are better scaled to existing technology. The yeast genome will contain genes with functions common to all eukaryotic cells, and those of simple multicellular organisms may throw light on the genetic specification of more complex functions. However, vertebrates differ in their morphology and development, so the ideal model would be a vertebrate genome of minimum size and complexity but with maximum homology to the human genome. Here we report the characterization of the small genome (400 Mb) of the tetraodontoid fish, Fugu rubripes. A random sequencing approach supported by gene probing shows that the haploid genome contains 400 Mb of DNA, of which more that 90% is unique. This genome is 7.5 times smaller than the human genome and because it has a similar gene repertoire it is the best model genome for the discovery of human genes.

Animals↗
PubMed2001

A comparison of genetic network models.

With the completion of the sequencing of the human genome, the need for tools capable of unraveling the interaction and functionality of genes becomes extremely urgent. In answer to this quest, the advent of microarray technology provides the opportunity to perform large scale gene expression analyses. Recently, genetic networks were proposed as a possible methodology for modeling genetic interactions. Since then, a wide variety of different models have been introduced. However, it is, in general, unclear what the strengths and weaknesses of each of these approaches are and where these models overlap and differ. This paper compares different genetic modeling approaches that attempt to extract the gene regulation matrix from expression data. A taxonomy of continuous genetic network models is proposed and the following important characteristics are suggested and employed to compare the models: (1) inferential power; (2) predictive power; (3) robustness; (4) consistency; (5) stability and (6) computational cost. Where possible, synthetic time series data are employed to investigate some of these properties.

Classification↗
PubMed2006

Bias of selection on human copy-number variants.

Although large-scale copy-number variation is an important contributor to conspecific genomic diversity, whether these variants frequently contribute to human phenotype differences remains unknown. If they have few functional consequences, then copy-number variants (CNVs) might be expected both to be distributed uniformly throughout the human genome and to encode genes that are characteristic of the genome as a whole. We find that human CNVs are significantly overrepresented close to telomeres and centromeres and in simple tandem repeat sequences. Additionally, human CNVs were observed to be unusually enriched in those protein-coding genes that have experienced significantly elevated synonymous and nonsynonymous nucleotide substitution rates, estimated between single human and mouse orthologues. CNV genes encode disproportionately large numbers of secreted, olfactory, and immunity proteins, although they contain fewer than expected genes associated with Mendelian disease. Despite mouse CNVs also exhibiting a significant elevation in synonymous substitution rates, in most other respects they do not differ significantly from the genomic background. Nevertheless, they encode proteins that are depleted in olfactory function, and they exhibit significantly decreased amino acid sequence divergence. Natural selection appears to have acted discriminately among human CNV genes. The significant overabundance, within human CNVs, of genes associated with olfaction, immunity, protein secretion, and elevated coding sequence divergence, indicates that a subset may have been retained in the human population due to the adaptive benefit of increased gene dosage. By contrast, the functional characteristics of mouse CNVs either suggest that advantageous gene copies have been depleted during recent selective breeding of laboratory mouse strains or suggest that they were preferentially fixed as a consequence of the larger effective population size of wild mice. It thus appears that CNV differences among mouse strains do not provide an appropriate model for large-scale sequence variations in the human population.

Animals↗
PubMed2026

Integrated bioinformatics and SEM analysis reveal GPAM as a key mediator of fibrosis in NAFLD with metabolic dysfunction.

Nonalcoholic fatty liver disease (NAFLD) is a complex condition influenced by metabolic and genetic factors, yet the shared genetic architecture underlying its progression remains poorly understood. The aim of this study was to employ genomic structural equation modeling (GSEM) to elucidate the genetic architecture linking NAFLD with key metabolic traits-including insulin resistance, body mass index (BMI), hemoglobin A1c (HbA1c), and liver fibrosis using summary statistics from large-scale genome-wide association studies. By harmonizing 2.18 million variants across five genome-wide association studies (GWAS) datasets, we identified 134 genome-wide significant loci that mapped to 24 genes. GSEM revealed a latent genetic structure composed of two distinct dimensions: a metabolic regulation factor primarily driven by insulin resistance, BMI, and HbA1c; and a structural pathology factor specifically associated with liver fibrosis. These factors explained 65.5% and 78.1% of the genetic variance in BMI and fibrosis, respectively, with minimal correlation (rg = 0:07), indicating their genetic distinctness. Additionally, integrating Mendelian randomization with liver transcriptome profiling, we characterized how the 24 genes contribute to disease and identified mitochondrial glycerol-3-phosphate acyltransferase (GPAM) as the key gene that causally links lipid metabolism to fibrogenesis. In conclusion, we present the first genetically grounded mechanism for the progression of NAFLD to fibrosis. This mechanism encompasssses genetic variants, dysregulated gene expression, metabolic disturbances, and the processes involved in fibrotic remodeling. This research establishes a genetic framework for understanding the pathogenesis of NAFLD and highlights novel therapeutic targets for intervention.

Non-alcoholic Fatty Liver Disease↗
PubMed2001

TOUCHSTONE: an ab initio protein structure prediction method that uses threading-based tertiary restraints.

The successful prediction of protein structure from amino acid sequence requires two features: an efficient conformational search algorithm and an energy function with a global minimum in the native state. As a step toward addressing both issues, a threading-based method of secondary and tertiary restraint prediction has been developed and applied to ab initio folding. Such restraints are derived by extracting consensus contacts and local secondary structure from at least weakly scoring structures that, in some cases, can lack any global similarity to the sequence of interest. Furthermore, to generate representative protein structures, a reduced lattice-based protein model is used with replica exchange Monte Carlo to explore conformational space. We report results on the application of this methodology, termed TOUCHSTONE, to 65 proteins whose lengths range from 39 to 146 residues. For 47 (40) proteins, a cluster centroid whose rms deviation from native is below 6.5 (5) A is found in one of the five lowest energy centroids. The number of correctly predicted proteins increases to 50 when atomic detail is added and a knowledge-based atomic potential is combined with clustered and nonclustered structures for candidate selection. The combination of the ratio of the relative number of contacts to the protein length and the number of clusters generated by the folding algorithm is a reliable indicator of the likelihood of successful fold prediction, thereby opening the way for genome-scale ab initio folding.

Algorithms↗
PubMed2005

Tools enabling the elucidation of molecular pathways active in human disease: application to Hepatitis C virus infection.

BACKGROUND: The extraction of biological knowledge from genome-scale data sets requires its analysis in the context of additional biological information. The importance of integrating experimental data sets with molecular interaction networks has been recognized and applied to the study of model organisms, but its systematic application to the study of human disease has lagged behind due to the lack of tools for performing such integration. RESULTS: We have developed techniques and software tools for simplifying and streamlining the process of integration of diverse experimental data types in molecular networks, as well as for the analysis of these networks. We applied these techniques to extract, from genomic expression data from Hepatitis C virus-infected liver tissue, potentially useful hypotheses related to the onset of this disease. Our integration of the expression data with large-scale molecular interaction networks and subsequent analyses identified molecular pathways that appear to be induced or repressed in the response to Hepatitis C viral infection. CONCLUSION: The methods and tools we have implemented allow for the efficient dynamic integration and analysis of diverse data in a major human disease system. This integrated data set in turn enabled simple analyses to yield hypotheses related to the response to Hepatitis C viral infection.

Computational Biology↗
PubMed2006

Statistical genetics concepts and approaches in schizophrenia and related neuropsychiatric research.

Statistical genetics is a research field that focuses on mathematical models and statistical inference methodologies that relate genetic variations (ie, naturally occurring human DNA sequence variations or "polymorphisms") to particular traits or diseases (phenotypes) usually from data collected on large samples of families or individuals. The ultimate goal of such analysis is the identification of genes and genetic variations that influence disease susceptibility. Although of extreme interest and importance, the fact that many genes and environmental factors contribute to neuropsychiatric diseases of public health importance (eg, schizophrenia, bipolar disorder, and depression) complicates relevant studies and suggests that very sophisticated mathematical and statistical modeling may be required. In addition, large-scale contemporary human DNA sequencing and related projects, such as the Human Genome Project and the International HapMap Project, as well as the development of high-throughput DNA sequencing and genotyping technologies have provided statistical geneticists with a great deal of very relevant and appropriate information and resources. Unfortunately, the use of these resources and their interpretation are not straightforward when applied to complex, multifactorial diseases such as schizophrenia. In this brief and largely nonmathematical review of the field of statistical genetics, we describe many of the main concepts, definitions, and issues that motivate contemporary research. We also provide a discussion of the most pressing contemporary problems that demand further research if progress is to be made in the identification of genes and genetic variations that predispose to complex neuropsychiatric diseases.

Chromosome Mapping↗
PubMed2006

A supervised hidden markov model framework for efficiently segmenting tiling array data in transcriptional and chIP-chip experiments: systematically incorporating validated biological knowledge.

MOTIVATION: Large-scale tiling array experiments are becoming increasingly common in genomics. In particular, the ENCODE project requires the consistent segmentation of many different tiling array datasets into 'active regions' (e.g. finding transfrags from transcriptional data and putative binding sites from ChIP-chip experiments). Previously, such segmentation was done in an unsupervised fashion mainly based on characteristics of the signal distribution in the tiling array data itself. Here we propose a supervised framework for doing this. It has the advantage of explicitly incorporating validated biological knowledge into the model and allowing for formal training and testing. METHODOLOGY: In particular, we use a hidden Markov model (HMM) framework, which is capable of explicitly modeling the dependency between neighboring probes and whose extended version (the generalized HMM) also allows explicit description of state duration density. We introduce a formal definition of the tiling-array analysis problem, and explain how we can use this to describe sampling small genomic regions for experimental validation to build up a gold-standard set for training and testing. We then describe various ideal and practical sampling strategies (e.g. maximizing signal entropy within a selected region versus using gene annotation or known promoters as positives for transcription or ChIP-chip data, respectively). RESULTS: For the practical sampling and training strategies, we show how the size and noise in the validated training data affects the performance of an HMM applied to the ENCODE transcriptional and ChIP-chip experiments. In particular, we show that the HMM framework is able to efficiently process tiling array data as well as or better than previous approaches. For the idealized sampling strategies, we show how we can assess their performance in a simulation framework and how a maximum entropy approach, which samples sub-regions with very different signal intensities, gives the maximally performing gold-standard. This latter result has strong implications for the optimum way medium-scale validation experiments should be carried out to verify the results of the genome-scale tiling array experiments.

Artificial Intelligence↗
PubMed1999

Genetic control of lifespan: studies from animal models.

The premise that there are genes that wield a strong influence on longevity has, until recently, not been a popular one and there has been no concerted effort to find such genes. However, the finding that single genes can have large effects on the lifespans of yeast, worms and flies raises the possibility that individual genes in mammals may similarly have relatively large effects on longevity. Recent advances in mammalian genetics, many associated with the large-scale efforts to sequence the human and mouse genomes, have accelerated the search for longevity genes, principally in mice. Here, we review results using animal models that have recently shed light on genes regulating longevity and ageing patterns. A large number of genetically defined strains of mice are available and this, together with their established history of use in genetic research and their relatively short lifespans, has made murine models particularly useful. We also review our own work in which genes regulating mouse lifespan and those regulating cell cycling of haematopoietic progenitor cells have been mapped to the same locations in the genome. These results suggest that some of the same genes affect both traits, and further suggest a cause-and-effect relationship between cumulative cell-cycle activity and longevity of an organism.

Journal Article↗
PubMed2025

Design of highly functional genome editors by modelling CRISPR-Cas sequences.

Gene editing has the potential to solve fundamental challenges in agriculture, biotechnology and human health. CRISPR-based gene editors derived from microorganisms, although powerful, often show notable functional tradeoffs when ported into non-native environments, such as human cells1. Artificial-intelligence-enabled design provides a powerful alternative with the potential to bypass evolutionary constraints and generate editors with optimal properties. Here, using large language models2 trained on biological diversity at scale, we demonstrate successful precision editing of the human genome with a programmable gene editor designed with artificial intelligence. To achieve this goal, we curated a dataset of more than 1 million CRISPR operons through systematic mining of 26 terabases of assembled genomes and metagenomes. We demonstrate the capacity of our models by generating 4.8× the number of protein clusters across CRISPR-Cas families found in nature and tailoring single-guide RNA sequences for Cas9-like effector proteins. Several of the generated gene editors show comparable or improved activity and specificity relative to SpCas9, the prototypical gene editing effector, while being 400 mutations away in sequence. Finally, we demonstrate that an artificial-intelligence-generated gene editor, denoted as OpenCRISPR-1, exhibits compatibility with base editing. We release OpenCRISPR-1 to facilitate broad, ethical use across research and commercial applications.

CRISPR-Cas Systems↗
PubMed2004

A computational algebra approach to the reverse engineering of gene regulatory networks.

This paper proposes a new method to reverse engineer gene regulatory networks from experimental data. The modeling framework used is time-discrete deterministic dynamical systems, with a finite set of states for each of the variables. The simplest examples of such models are Boolean networks, in which variables have only two possible states. The use of a larger number of possible states allows a finer discretization of experimental data and more than one possible mode of action for the variables, depending on threshold values. Furthermore, with a suitable choice of state set, one can employ powerful tools from computational algebra, that underlie the reverse-engineering algorithm, avoiding costly enumeration strategies. To perform well, the algorithm requires wildtype together with perturbation time courses. This makes it suitable for small to meso-scale networks rather than networks on a genome-wide scale. An analysis of the complexity of the algorithm is performed. The algorithm is validated on a recently published Boolean network model of segment polarity development in Drosophila melanogaster.

Algorithms↗
PubMed2002

What positions nucleosomes?--A model.

Here we propose a new determinant for localization of nucleosomes along genomic DNA, in addition to sequence-dependent features. The new specific class of chromatin scaling signals involves curved DNA. According to the observed positional distribution of DNA curvature, the new synchronizing signal occurs once per four nucleosomes on average. This new factor in nucleosome positioning should substantially influence the efficiency of biological reactions through regulatory factors microscopically and the entire chromatin structure through the 30 nm fiber structure macroscopically. Allocation of the new type of signals is found to be fixed evolutionarily although they could be shifted in accordance with the hierarchy of functional genomic structures.

Chromatin↗
PubMed2005

Automatic annotation of protein function.

The annotation of protein function at genomic scale is essential for day-to-day work in biology and for any systematic approach to the modeling of biological systems. Currently, functional annotation is essentially based on the expansion of the relatively small number of experimentally determined functions to large collections of proteins. The task of systematic annotation faces formidable practical problems related to the accuracy of the input experimental information, the reliability of current systems for transferring information between related sequences, and the reproducibility of the links between database information and the original experiments reported in publications. These technical difficulties merely lie on the surface of the deeper problem of the evolution of protein function in the context of protein sequences and structures. Given the mixture of technical and scientific challenges, it is not surprising that errors are introduced, and expanded, in database annotations. In this situation, a more realistic option is the development of a reliability index for database annotations, instead of depending exclusively on efforts to correct databases. Several groups have attempted to compare the database annotations of similar proteins, which constitutes the first steps toward the calibration of the relationship between sequence and annotation space.

Artificial Intelligence↗
PubMed2002

Sequence variation and the biological function of genes: methodological and biological considerations.

Single nucleotide polymorphisms (SNPs) are expected to facilitate the chromosomal mapping and eventual cloning of genetic determinants of complex quantitative phenotypes. To date, more than 2.5 million non-redundant human SNPs have been reported in the public domain, of which approximately 100000 have been validated by either independent investigators or by independent methods. Equally impressive is the myriad of methods developed for allelic discrimination. Nevertheless, reports of successful applications of SNPs to genome-wide linkage analysis of both mono- and polygenic traits are rare and limited to a few model organisms, that provide affordable platforms to test both novel methodological and biological concepts at a whole-genome scale under conditions that can be reasonably controlled. Progress in the analysis of SNPs needs to be complemented by methods that allow the systematic elucidation of both primary and secondary phenotypes of genes. Importantly, observations made in one species may very well be of immediate applicability to other species including human. This is particularly true for conserved biological processes such as mitochondrial respiration and DNA repair.

Arabidopsis↗
PubMed2004

PlantGDB, plant genome database and analysis tools.

PlantGDB (http://www.plantgdb.org/) is a database of molecular sequence data for all plant species with significant sequencing efforts. The database organizes EST sequences into contigs that represent tentative unique genes. Contigs are annotated and, whenever possible, linked to their respective genomic DNA. Genome sequence fragments are assembled similarly. The goal of the PlantGDB web site is to establish the basis for identifying sets of genes common to all plants or specific to particular species by integrating a number of bioinformatics tools that facilitate gene prediction and cross- species comparisons. For species with large-scale genome sequencing efforts, PlantGDB provides genome browsing capabilities that integrate all available EST and cDNA evidence for current gene models (for Arabidopsis thaliana, see the AtGDB site at http://www.plantgdb.org/AtGDB/).

Computational Biology↗
PubMed2006

Numerous and rapid nonstochastic modifications of gene products in newly synthesized Brassica napus allotetraploids.

Polyploidization is a widespread process that results in the merger of two or more genomes in a common nucleus. To investigate modifications of gene expression occurring during allopolyploid formation, the Brassica napus allotetraploid model was chosen. Large-scale analyses of the proteome were conducted on two organs, the stem and root, so that >1600 polypeptides were screened. Comparative proteomics of synthetic B. napus and its homozygous diploid progenitors B. rapa and B. oleracea showed that very few proteins disappeared or appeared in the amphiploids (<1%), but a strikingly high number (25-38%) of polypeptides displayed quantitative nonadditive pattern. Nonstochastic gene expression repatterning was found since 99% of the detected variations were reproducible in four independently created amphiploids. More than 60% of proteins displayed a nonadditive pattern closer to the paternal parent B. rapa. Interspecific hybridization triggered the majority of the deviations (89%), whereas very few variations (approximately 3%) were associated with genome doubling and more significant alterations arose from selfing (approximately 9%). Some nonadditive proteins behaved similarly in both organs, while others exhibited contrasted behavior, showing rapid organ-specific regulation. B. napus formation was therefore correlated with immediate and directed nonadditive changes in gene expression, suggesting that the early steps of allopolyploidization repatterning are controlled by nonstochastic mechanisms.

Biological Evolution↗