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Results for “Genome scale models”

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At least 451 records · Page 25Linked to original sources
PubMed2005

Genome-wide screening for gene function using RNAi in mammalian cells.

Mammalian genome sequencing has identified numerous genes requiring functional annotation. The discovery that dsRNA can direct gene-specific silencing in both model organisms and mammalian cells through RNA interference (RNAi) has provided a platform for dissecting the function of independent genes. The generation of large-scale RNAi libraries targeting all predicted genes within mouse, rat and human cells, combined with the large number of cell-based assays, provides a unique opportunity to perform high-throughput genetics in these complex cell systems. Many different formats exist for the generation of genome-wide RNAi libraries for use in mammalian cells. Furthermore, the use of these libraries in either genetic screens or genetic selections allows for the identification of known and novel genes involved in complex cellular phenotypes and biological processes, some of which underpin human disease. In this review, we examine genome-wide RNAi libraries used in model organisms and mammalian cells and provide examples of how these information rich reagents can be used for determining gene function, discovering novel therapeutic targets and dissecting signalling pathways, cellular processes and complex phenotypes.

Animals↗
PubMed2026

Accelerating inference in genomic and proteomic foundation models via speculative decoding.

MOTIVATION: Genomic and protein foundation models (GFMs and PFMs) have demonstrated strong performance in learning the language of DNA and proteins, but their use in large-scale sequence generation is limited by the latency of autoregressive decoding. Because every token triggers a forward pass of a large Transformer, whose inference is relatively slow, long-sequence generation quickly becomes costly. RESULTS: In this work we adapt speculative decoding to a representative GFM: the DNA model DNAGPT and two representative PFMs: ProGen2 and ProtGPT2. We implement a probabilistic variant of speculative decoding, in which a lightweight draft model proposes short token spans and a larger target model verifies or corrects them in parallel, while preserving the target model's sampling distribution. Across all three models we systematically study the effect of speculation window length, temperature, draft architecture and prompt length, and we benchmark tokens per second over multiple runs per configuration. Speculative decoding yields consistent speedups over standard key-value cached decoding, with maximum observed speedup reaching 100% increase, while average gains across models ranging between 20% and 40% (e.g. 1.2×-1.4×), without changing the underlying target model predictions. Our results show that speculative decoding is a practical and model-agnostic strategy for accelerating genomic and proteomic sequence generation without sacrificing prediction quality. AVAILABILITY AND IMPLEMENTATION: All code and results are freely available at https://github.com/Georgakopoulos-Soares-lab/BioSpecDec.

Genomics↗
PubMed2006

Anatomic demarcation by positional variation in fibroblast gene expression programs.

Fibroblasts are ubiquitous mesenchymal cells with many vital functions during development, tissue repair, and disease. Fibroblasts from different anatomic sites have distinct and characteristic gene expression patterns, but the principles that govern their molecular specialization are poorly understood. Spatial organization of cellular differentiation may be achieved by unique specification of each cell type; alternatively, organization may arise by cells interpreting their position along a coordinate system. Here we test these models by analyzing the genome-wide gene expression profiles of primary fibroblast populations from 43 unique anatomical sites spanning the human body. Large-scale differences in the gene expression programs were related to three anatomic divisions: anterior-posterior (rostral-caudal), proximal-distal, and dermal versus nondermal. A set of 337 genes that varied according to these positional divisions was able to group all 47 samples by their anatomic sites of origin. Genes involved in pattern formation, cell-cell signaling, and matrix remodeling were enriched among this minimal set of positional identifier genes. Many important features of the embryonic pattern of HOX gene expression were retained in fibroblasts and were confirmed both in vitro and in vivo. Together, these findings suggest that site-specific variations in fibroblast gene expression programs are not idiosyncratic but rather are systematically related to their positional identities relative to major anatomic axes.

Cell Differentiation↗
PubMed2005

Bioinformatics and proteomics approaches for aging research.

Aging is a natural phenomenon that affects the entire physiology of an organism. Elucidating the molecular mechanisms underlying this complex process remains a major challenge today. Humans make poor models for research into aging because of their long life span. Thus, most of the current knowledge is through studies conducted in lower organisms. Large differences in life spans make it difficult to extrapolate the results of experiments carried out in model organisms to humans. Recent advances in genomic and proteomic technologies now permit generation of data pertaining to aging on a large-scale. In addition, several web-based community resources and databases are available that provide easy access to the available data. Use of bioinformatics and systems biology type of approaches provide a framework to start dissecting this complex biological phenomenon. Here, we discuss various genomic, transcriptomic and proteomic approaches that have the potential to provide a comprehensive mechanistic insight into the aging process.

Aging↗
PubMed1999

Higher-order chromatin structure: looping long molecules.

Chromatin structure plays a variety of roles in eukaryotes, ranging from the structural organization of the genome to the facilitation of transcription factors and remodeling of individual gene promoters. Higher-order chromatin structure typically refers to those structural features of the genome that serve to facilitate large-scale condensation and packaging. It is becoming increasingly clear, however, that large-scale features that create loop domains play an important role in the management and functional organization of the genome as well. Recently, plant models have made significant contributions to our understanding of higher-order chromatin structures in eukaryotes.

Animals↗
PubMed2000

Zebrafish: a model system for the study of human disease.

The zebrafish (Danio rerio) is a powerful model organism for the study of vertebrate biology, being well suited to both developmental and genetic analysis. Large-scale genetic screens have identified hundreds of mutant phenotypes, many of which resemble human clinical disorders. The creation of critical genetic reagents, coupled with the rapid progress of the zebrafish genome initiative directed by the National Institutes of Health, are bringing this model system to its full potential for the study of vertebrate biology, physiology and human disease.

Animals↗
PubMed2024

Dissecting host-microbe interactions with modern functional genomics.

Interrogation of host-microbe interactions has long been a source of both basic discoveries and benefits to human health. Here, we review the role that functional genomics approaches have played in such efforts, with an emphasis on recent examples that have harnessed technological advances to provide mechanistic insight at increased scale and resolution. Finally, we discuss how concurrent innovations in model systems and genetic tools have afforded opportunities to interrogate additional types of host-microbe relationships, such as those in the mammalian gut. Bringing these innovations together promises many exciting discoveries ahead.

Genomics↗
PubMed1993

The human genome project.

The Human Genome Project in the United States is now well underway. Its programmatic direction was largely set by a National Research Council report issued in 1988. The broad framework supplied by this report has survived almost unchanged despite an upheaval in the technology of genome analysis. This upheaval has primarily affected physical and genetic mapping, the two dominant activities in the present phase of the project. Advances in mapping techniques have allowed good progress toward the specific goals of the project and are also providing strong corollary benefits throughout biomedical research. Actual DNA sequencing of the genomes of the human and model organisms is still at an early stage. There has been little progress in the intrinsic efficiency of DNA-sequence determination. However, refinements in experimental protocols, instrumentation, and project management have made it practical to acquire sequence data on an enlarged scale. It is also increasingly apparent that DNA-sequence data provide a potent means of relating knowledge gained from the study of model organisms to human biology. There is as yet little indication that the infusion of technology from outside biology into the Human Genome Project has been effectively stimulated. Opportunities in this area remain large, posing substantial technical and policy challenges.

Chromosome Mapping↗
PubMed1995

Evidence for the organization of chromatin in megabase pair-sized loops arranged along a random walk path in the human G0/G1 interphase nucleus.

We determined the folding of chromosomes in interphase nuclei by measuring the distance between points on the same chromosome. Over 25,000 measurements were made in G0/G1 nuclei between DNA sequences separated by 0.15-190 megabase pairs (Mbp) on three human chromosomes. The DNA sequences were specifically labeled by fluorescence in situ hybridization. The relationship between mean-square interphase distance and genomic separation has two linear phases, with a transition at approximately 2 Mbp. This biphasic relationship indicates the existence of two organizational levels at scales > 100 kbp. On one level, chromatin appears to be arranged in large loops several Mbp in size. Within each loop, chromatin is randomly folded. On the second level, specific loop-attachment sites are arranged to form a supple, backbonelike structure, which also shows characteristic random walk behavior. This random walk/giant loop model is the simplest model that fully describes the observed large-scale spatial relationships. Additional evidence for large loops comes from measurements among probes in Xq28, where interphase distance increases and then locally decreases with increasing genomic separation.

Cell Cycle↗
PubMed1999

Plant tagnology.

Transposable elements have been used as an effective mutagen and as a tool to clone tagged genes. Insertion of a transposable element into a gene can lead to loss- or gain-of-function, changes in expression pattern, or can have no effect on gene function at all, depending on whether the insertion took place in coding or non-coding regions of the gene. Cloning transposable elements from different plant species has made them available as a tool for the isolation of tagged genes using homologous or heterologous tagging strategies. Based on these transposons, new elements have been engineered bearing reporter genes that can be used for expression analysis of the tagged gene, or resistance genes that can be used to select for knockout insertions. While many genes have been cloned using transposon tagging following traditional forward genetics strategies, gene cloning has ceased to be the rate-limiting step in the process of determining sequence-function relations in several important plant model species. Large-scale insertion mutagenesis and identification of insertion sites following a reverse genetics strategy appears to be the best method for unravelling the biological role of the thousands of genes with unknown functions identified by genome or expressed sequence tag (EST) sequencing projects. Here we review the progress in forward tagging technologies and discuss reverse genetics strategies and their applications in different model species.

Journal Article↗
PubMed1998

Functional genomics in the mouse: phenotype-based mutagenesis screens.

Significant progress has been made in sequencing the genomes of several model organisms, and efforts are now underway to complete the sequencing of the human genome. In parallel with this effort, new approaches are being developed for the elucidation of the functional content of the human genome. The mouse will have an important role in this phase of the genome project as a model system. In this review we discuss and compare classical genetic approaches to gene function-phenotype-based mutagenesis screens aimed at the establishment of a large collection of single gene mutations affecting a wide range of phenotypic traits in the mouse. Whereas large scale genome-wide screens that are directed at the identification of all loci contributing to a specific phenotype may be impractical, region-specific saturation screens that provide mutations within a delimited chromosomal region are a feasible alternative. Region-specific screens in the mouse can be performed in only two generations by combining high-efficiency chemical mutagenesis with deletion complexes generated using embryonic stem (ES) cells. The ability to create and analyze deletion complexes rapidly, as well as to map novel chemically-induced mutations within these complexes, will facilitate systematic functional analysis of the mouse genome and corresponding gene sequences in humans. Furthermore, as the extent of the mouse genome sequencing effort is still uncertain, we underscore a necessity to direct sequencing efforts to those chromosomal regions that are targets for extensive mutagenesis screens.

Animals↗
PubMed2003

Gaining confidence in high-throughput protein interaction networks.

Although genome-scale technologies have benefited from statistical measures of data quality, extracting biologically relevant pathways from high-throughput proteomics data remains a challenge. Here we develop a quantitative method for evaluating proteomics data. We present a logistic regression approach that uses statistical and topological descriptors to predict the biological relevance of protein-protein interactions obtained from high-throughput screens for yeast. Other sources of information, including mRNA expression, genetic interactions and database annotations, are subsequently used to validate the model predictions without bias or cross-pollution. Novel topological statistics show hierarchical organization of the network of high-confidence interactions: protein complex interactions extend one to two links, and genetic interactions represent an even finer scale of organization. Knowledge of the maximum number of links that indicates a significant correlation between protein pairs (correlation distance) enables the integrated analysis of proteomics data with data from genetics and gene expression. The type of analysis presented will be essential for analyzing the growing amount of genomic and proteomics data in model organisms and humans.

Algorithms↗
PubMed1995

A random-walk/giant-loop model for interphase chromosomes.

Fluorescence in situ hybridization data on distances between defined genomic sequences are used to construct a quantitative model for the overall geometric structure of a human chromosome. We suggest that the large-scale geometry during the G0/G1 part of the cell cycle may consist of flexible chromatin loops, averaging approximately 3 million bp, with a random-walk backbone. A fully explicit, three-parametric polymer model of this random-walk/giant-loop structure can account well for the data. More general models consistent with the data are briefly discussed.

Base Composition↗
PubMed2006

Positive selection versus demography: evolutionary inferences based on an unusual haplotype structure in Drosophila simulans.

Coalescent simulations were used to investigate the possible role of population subdivision and history in shaping nucleotide variation in a recombining 88-kb genomic fragment of Drosophila simulans displaying an unusual large-scale haplotype structure. The multilocus analysis, based on summary statistics using specific demographic null models under recombination, indicates that the observed levels of linkage disequilibrium differed significantly from the values expected under different bottleneck and population admixture scenarios. These results indicate that demography alone may not account for the observed pattern of variation and support the previous claim that the data are better described by a model in which an adaptive mutation has not yet gone to fixation.

Animals↗
PubMed2006

Hierarchical modularity of nested bow-ties in metabolic networks.

BACKGROUND: The exploration of the structural topology and the organizing principles of genome-based large-scale metabolic networks is essential for studying possible relations between structure and functionality of metabolic networks. Topological analysis of graph models has often been applied to study the structural characteristics of complex metabolic networks. RESULTS: In this work, metabolic networks of 75 organisms were investigated from a topological point of view. Network decomposition of three microbes (Escherichia coli, Aeropyrum pernix and Saccharomyces cerevisiae) shows that almost all of the sub-networks exhibit a highly modularized bow-tie topological pattern similar to that of the global metabolic networks. Moreover, these small bow-ties are hierarchically nested into larger ones and collectively integrated into a large metabolic network, and important features of this modularity are not observed in the random shuffled network. In addition, such a bow-tie pattern appears to be present in certain chemically isolated functional modules and spatially separated modules including carbohydrate metabolism, cytosol and mitochondrion respectively. CONCLUSION: The highly modularized bow-tie pattern is present at different levels and scales, and in different chemical and spatial modules of metabolic networks, which is likely the result of the evolutionary process rather than a random accident. Identification and analysis of such a pattern is helpful for understanding the design principles and facilitate the modelling of metabolic networks.

Algorithms↗
PubMed2004

Applications of Bayesian statistical methods in microarray data analysis.

Microarray technology allows one to measure gene expression levels simultaneously on the whole-genome scale. The rapid progress generates both a great wealth of information and challenges in making inferences from such massive data sets. Bayesian statistical modeling offers an alternative approach to frequentist methodologies, and has several features that make these methods advantageous for the analysis of microarray data. These include the incorporation of prior information, flexible exploration of arbitrarily complex hypotheses, easy inclusion of nuisance parameters, and relatively well developed methods to handle missing data. Recent developments in Bayesian methodology generated a variety of techniques for the identification of differentially expressed genes, finding genes with similar expression profiles, and uncovering underlying gene regulatory networks. Bayesian methods will undoubtedly become more common in the future because of their great utility in microarray analysis.

Bayes Theorem↗
PubMed2005

Modelling the evolution of genetic regulatory networks.

An evolutionary model of genetic regulatory networks is developed, based on a model of network encoding and dynamics called the Artificial Genome (AG). This model derives a number of specific genes and their interactions from a string of (initially random) bases in an idealized manner analogous to that employed by natural DNA. The gene expression dynamics are determined by updating the gene network as if it were a simple Boolean network. The generic behaviour of the AG model is investigated in detail. In particular, we explore the characteristic network topologies generated by the model, their dynamical behaviours, and the typical variance of network connectivities and network structures. These properties are demonstrated to agree with a probabilistic analysis of the model, and the typical network structures generated by the model are shown to lie between those of random networks and scale-free networks in terms of their degree distribution. Evolutionary processes are simulated using a genetic algorithm, with selection acting on a range of properties from gene number and degree of connectivity through periodic behaviour to specific patterns of gene expression. The evolvability of increasingly complex patterns of gene expression is examined in detail. When a degree of redundancy is introduced, the average number of generations required to evolve given targets is reduced, but limits on evolution of complex gene expression patterns remain. In addition, cyclic gene expression patterns with periods that are multiples of shorter expression patterns are shown to be inherently easier to evolve than others. Constraints imposed by the template-matching nature of the AG model generate similar biases towards such expression patterns in networks in initial populations, in addition to the somewhat scale-free nature of these networks. The significance of these results on current understanding of biological evolution is discussed.

Algorithms↗
PubMed2026

Predictive evolutionary genomics: principles, validation, and practice.

Climate change and habitat loss are driving rapid evolutionary responses in populations world-wide, which creates an urgent need for evolutionary forecasting in conservation and agriculture. Such forecasting can be categorized into three time scales: trait-based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to c. 20 generations, allele-based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world.

Genomics↗