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Results for “Genome scale models”

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At least 163 records · Page 9Linked to original sources
PubMed2005

From DNA sequence analysis to modeling replication in the human genome.

We explore the large-scale behavior of nucleotide compositional strand asymmetries along human chromosomes. As we observe for 7 of 9 origins of replication experimentally identified so far, the (TA+GC) skew displays rather sharp upward jumps, with a linear decreasing profile in between two successive jumps. We present a model of replication with well positioned replication origins and random terminations that accounts for the observed characteristic serrated skew profiles. We succeed in identifying 287 pairs of putative adjacent replication origins with an origin spacing approximately 1-2 Mbp that are likely to correspond to replication foci observed in interphase nuclei and recognized as stable structures that persist throughout subsequent cell generations.

DNA Replication↗
PubMed2026

Characterizing the ecological niche of insertion sequences within prokaryotic genomes.

Insertion sequences (ISs) are widespread prokaryotic transposable elements, often regarded as genomic parasites that primarily cause deleterious mutations. However, they can also promote adaptive changes. These antagonistic properties make their overall impact on prokaryotic evolution difficult to grasp. Here, we address this challenge by leveraging the framework of transposon ecology to analyze IS occurrences across and within 30 499 prokaryotic genomes. Combining phylogenomics with multi-scale genomic analysis, quantitative ecology, and mathematical modeling, we provide evidence that although genomes generally provide sufficient resources for IS coexistence, universal mechanisms shape their occurrence and chromosomal distribution across genomes. These include (i) the preferential localization of ISs within highly variable and GC-heterogeneous chromosomal regions of genomic plasticity, which act as the primary reservoir of IS niches; (ii) a linear scaling between IS abundance and niche size, with an average of $5.4$ additional accessible insertion sites per IS; (iii) a dependence of IS occurrence on the presence of other ISs, suggesting a form of group behavior; (iv) the accumulation of AT-rich sequences in both coding and noncoding regions up to 100 kb around ISs, indicative of ecological isolation; and (v) the spatial partitioning of mobile genetic elements around ISs, reminiscent of ecological niche differentiation. Besides these general principles, we also uncover niche specificities associated with particular IS families, hinting at regulatory mechanisms that modulate IS activity. Altogether, this comprehensive transposon ecology approach offers new insights and avenues for understanding IS-host interactions and genome evolution, moving beyond traditional host-centric perspectives.

DNA Transposable Elements↗
PubMed2006

Global transcriptional analysis of Methanosarcina mazei strain Gö1 under different nitrogen availabilities.

Certain archaeal species can fix molecular nitrogen under nitrogen limiting conditions although little is known about this process at either the genetic or molecular level. To address this on a genome-wide scale, transcriptional analysis was performed on the model methanogen Methanosarcina mazei strain Gö1 using DNA-microarrays. The genomic expression patterns for cells grown under nitrogen fixing conditions versus nitrogen sufficiency (10 mM ammonium) revealed that approximately 5% of all genes are differentially expressed. Besides a small set of genes previously known to be up-regulated under nitrogen limitation, 14 additional genes involved in nitrogen metabolism were identified plus 10 genes encoding potential transcriptional regulators, 13 genes involved in carbon metabolism, 3 genes in general stress response, 8 putative transporter genes, and an additional 21 genes with unknown function. Quantitative reverse transcriptase PCR experiments confirmed the differential expression of a subset of these genes. Promoter analysis revealed a palindromic DNA motif centered nearby the transcriptional start point for several genes up-regulated under nitrogen limitation. A bioinformatics study demonstrated the presence of this motif in the up-stream region of 52 genes genome-wide, the majority of which showed nitrogen dependent differential transcription. We therefore hypothesize that this DNA element is involved in nitrogen control in M. mazei where it may act as a binding site for a regulatory protein.

Archaeal Proteins↗
PubMed2025

Bayesian Inference of Pathogen Phylogeography using the Structured Coalescent Model.

Over the past decade, pathogen genome sequencing has become well established as a powerful approach to study infectious disease epidemiology. In particular, when multiple genomes are available from several geographical locations, comparing them is informative about the relative size of the local pathogen populations as well as past migration rates and events between locations. The structured coalescent model has a long history of being used as the underlying process for such phylogeographic analysis. However, the computational cost of using this model does not scale well to the large number of genomes frequently analysed in pathogen genomic epidemiology studies. Several approximations of the structured coalescent model have been proposed, but their effects are difficult to predict. Here we show how the exact structured coalescent model can be used to analyse a precomputed dated phylogeny, in order to perform Bayesian inference on the past migration history, the effective population sizes in each location, and the directed migration rates from any location to another. We describe an efficient reversible jump Markov Chain Monte Carlo scheme which is implemented in a new R package StructCoalescent. We use simulations to demonstrate the scalability and correctness of our method and to compare it with existing software. We also applied our new method to several state-of-the-art datasets on the population structure of real pathogens to showcase the relevance of our method to current data scales and research questions.

Bayes Theorem↗
PubMed2005

High-betweenness proteins in the yeast protein interaction network.

Structural features found in biomolecular networks that are absent in random networks produced by simple algorithms can provide insight into the function and evolution of cell regulatory networks. Here we analyze "betweenness" of network nodes, a graph theoretical centrality measure, in the yeast protein interaction network. Proteins that have high betweenness, but low connectivity (degree), were found to be abundant in the yeast proteome. This finding is not explained by algorithms proposed to explain the scale-free property of protein interaction networks, where low-connectivity proteins also have low betweenness. These data suggest the existence of some modular organization of the network, and that the high-betweenness, low-connectivity proteins may act as important links between these modules. We found that proteins with high betweenness are more likely to be essential and that evolutionary age of proteins is positively correlated with betweenness. By comparing different models of genome evolution that generate scale-free networks, we show that rewiring of interactions via mutation is an important factor in the production of such proteins. The evolutionary and functional significance of these observations are discussed.

Journal Article↗
PubMed2005

Integrating microarray gene expression object model and clinical document architecture for cancer genomics research.

Systematic integration of genomic-scale expression profiles with clinical information may facilitate cancer genomics research. MAGE-OM (Microarray Gene Expression Object Model) defines standard objects for genomic but not for clinical data. HL7 CDA (Clinical Document Architecture) is a document model for clinical information, describing syntax (generic structure) but not semantics. We designed a document template in XML Schema with additional constraints for CDA to define content semantics, enabling data model-level integration of MAGE-OM and CDA for cancer genomics research.

Gene Expression Profiling↗
PubMed2005

Use of constraint-based modeling for the prediction and validation of antimicrobial targets.

The overall process of antimicrobial drug discovery and development seems simple, to cure infectious disease by identifying suitable antibiotic drugs. However, this goal has been difficult to fulfill in recent years. Despite the promise of the high-throughput innovations sparked by the genomics revolution, discovery, and development of new antibiotics has lagged in recent years exacerbating the already serious problem of evolution of antibiotic resistance. Therefore, both new antimicrobials are desperately needed as are improvements to speed up or improve nearly all steps in the process of discovering novel antibiotics and bringing these to clinical use. Another product of the genomic revolution is the modeling of metabolism using computational methodologies. Genomic-scale networks of metabolic reactions based on stoichiometry, thermodynamics and other physico-chemical constraints that emulate microbial metabolism have been developed into valuable research tools in metabolic engineering and other fields. This constraint-based modeling is predictive in identifying critical reactions, metabolites, and genes in metabolism. This is extremely useful in determining and rationalizing cellular metabolic requirements. In turn, these methods can be used to predict potential metabolic targets for antimicrobial research especially if used to increase the confidence in prioritization of metabolic targets. The many different capacities of constraint-based modeling also enable prediction of cellular response to specific inhibitors such as antibiotics and this may, ultimately find a role in drug discovery and development. Herein, we describe the principles of metabolic modeling and how they might initially be applied to antimicrobial research.

Anti-Bacterial Agents↗
PubMed1998

Big trees from little genomes: mitochondrial gene order as a phylogenetic tool.

Gene arrangement comparisons are a powerful tool for phylogenetic studies, especially those focused on ancient relationships. Recent reports using metazoan mitochondrial genomes address evolutionary relationships as well as rates and mechanisms of rearrangement. Mitochondrial systems serve as a model for larger-scale comparisons of whole organismal genomes and a stimulus for developing methods for reconstructing the patterns of rearrangement.

Animals↗
PubMed2005

Validation and refinement of gene-regulatory pathways on a network of physical interactions.

As genome-scale measurements lead to increasingly complex models of gene regulation, systematic approaches are needed to validate and refine these models. Towards this goal, we describe an automated procedure for prioritizing genetic perturbations in order to discriminate optimally between alternative models of a gene-regulatory network. Using this procedure, we evaluate 38 candidate regulatory networks in yeast and perform four high-priority gene knockout experiments. The refined networks support previously unknown regulatory mechanisms downstream of SOK2 and SWI4.

Computational Biology↗
PubMed2003

Rearrangements and chromosomal evolution.

Comparisons of the genome sequences of related species suggests varying patterns of chromosomal rearrangements in different evolutionary lineages. In this review, I focus on the quantitative characterization of rearrangement processes and discuss specific inventories that have been compiled to date. Of particular interest are the statistical distribution of the lengths of inverted or locally transposed chromosome fragments (notably very short ones), inhomogeneities in susceptibility to evolutionary breakpoints in chromosomal regions, the relative importance of genome doubling in the history of multicellular eukaryotes, and of lateral transfer versus gene gain and loss in prokaryotes. These developments provide challenges to computational biologists to refine, revise and scale up mathematical models and algorithms for analyzing genome rearrangements.

Animals↗
PubMed1996

Comparative mapping in Arabidopsis and Brassica, fine scale genome collinearity and congruence of genes controlling flowering time.

The model dicotyledonous plant, Arabidopsis thaliana, is closely related to Brassica crop species. It is intended that information concerning the genetic control of basic biological processes in Arabidopsis will be transferable to other species. Genome collinearity and its potential to facilitate the identification of candidate genes in Arabidopsis homologous to genes controlling important agronomic traits in Brassica was investigated. Genetic mapping in B. nigra identified two loci influencing flowering time (FT), with loci on linkage groups 2 and 8 explaining 53% and 12% of the total variation in FT, respectively. The CO gene exerts an important control over FT in A. thaliana, and B. nigra homologues of CO probably also play an important role in regulating FT. B. nigra homologues of CO were identified on linkage groups 2 and 8, the homologue on group 2 was coincident with the major locus controlling FT while the homologue on group 8 was within the 90% confidence interval of the weaker FT gene. The CO homologue on group 2 exhibits abundant allelic variation suggesting that it naturally controls a wide range of flowering times. Fine-scale A. thaliana/B. nigra comparative mapping demonstrated short-range collinearity between the genomes of Arabidopsis and Brassica. Eleven DNA fragments spaced over a 1.5 Mb contig in A. thaliana were used as RFLP probes in B. nigra. Three collinear representations of the A. thaliana contig were identified in B. nigra, with one interrupted by a large chromosomal inversion. Collinearity over this range will allow the resources generated by the Arabidopsis genome project to facilitate map-based cloning in Brassica crops.

Analysis of Variance↗
PubMed2006

Candidate states of Helicobacter pylori's genome-scale metabolic network upon application of "loop law" thermodynamic constraints.

Constraint-based modeling has proven to be a useful tool in the analysis of biochemical networks. To date, most studies in this field have focused on the use of linear constraints, resulting from mass balance and capacity constraints, which lead to the definition of convex solution spaces. One additional constraint arising out of thermodynamics is known as the "loop law" for reaction fluxes, which states that the net flux around a closed biochemical loop must be zero because no net thermodynamic driving force exists. The imposition of the loop-law can lead to nonconvex solution spaces making the analysis of the consequences of its imposition challenging. A four-step approach is developed here to apply the loop-law to study metabolic network properties: 1), determine linear equality constraints that are necessary (but not necessarily sufficient) for thermodynamic feasibility; 2), tighten V(max) and V(min) constraints to enclose the remaining nonconvex space; 3), uniformly sample the convex space that encloses the nonconvex space using standard Monte Carlo techniques; and 4), eliminate from the resulting set all solutions that violate the loop-law, leaving a subset of steady-state solutions. This subset of solutions represents a uniform random sample of the space that is defined by the additional imposition of the loop-law. This approach is used to evaluate the effect of imposing the loop-law on predicted candidate states of the genome-scale metabolic network of Helicobacter pylori.

Algorithms↗
PubMed2026

Diet modulates cardiac metabolic stress during anthracycline treatment.

Diet is a modifiable determinant of cardiovascular risk and may influence tolerance to cancer therapies. The mechanisms by which specific dietary components affect cardiac metabolism during anthracycline treatment remain poorly defined, limiting the incorporation of dietary recommendations into treatment guidelines. Here, we integrated heart proteomics data from patients treated with or without anthracyclines with a genome-scale reconstruction of human cardiac metabolism (CardioNet). Using constraint-based flux analysis, we conducted >30,000 in silico simulations of diet scenarios generated from chemical profiles of ∼500 foods curated in the Periodic Table of Food Initiative. These simulations revealed that diets enriched in rapidly absorbable sugars and depleted of essential fatty acids impair cardiac metabolic efficiency, increasing reactive oxygen species production and the demand for purine salvage fluxes. These predicted metabolic patterns were consistent with plasma metabolomics from patients treated with anthracyclines, validating our findings. Computational modeling of 39 recipes across six cuisines revealed cardiometabolic effects of omnivorous versus vegan diets in patients. Modeling of a healthy vegan diet increased cardiometabolic efficiency compared with a healthy omnivorous diet in patients treated with anthracyclines, independent of the culinary background. Our approach demonstrates that integrating the molecular composition of food with genome-scale metabolic models enables systematic analysis of diet patterns for translational testing. Ultimately, these in silico studies provide a framework for trials and may inform dietary recommendations for improving cardiometabolic health.NEW & NOTEWORTHY We developed a systems biology framework to predict how diet influences cardiac metabolism during cancer therapy. Across >30,000 in silico diet simulations, we identified nutrient patterns that either exacerbate or mitigate anthracycline-induced metabolic stress. These findings demonstrate how computational modeling can uncover diet-metabolism interactions driving cardiotoxicity and guide dietary interventions.

Humans↗
PubMed2001

Metabolic modeling of microbial strains in silico.

The large volume of genome-scale data that is being produced and made available in databases on the World Wide Web is demanding the development of integrated mathematical models of cellular processes. The analysis of reconstructed metabolic networks as systems leads to the development of an in silico or computer representation of collections of cellular metabolic constituents, their interactions and their integrated function as a whole. The use of quantitative analysis methods to generate testable hypotheses and drive experimentation at a whole-genome level signals the advent of a systemic modeling approach to cellular and molecular biology.

Genome↗
PubMed2006

Genome sequencing and identification of gene function in rice.

Rice is known to be one of the most important crops for human consumption. As the model cereal crop, large-scale sequencing of rice genome must play quite important roles both in theoretical research and practical application in rice breeding, which announces the opening of another new way to resolve the world food crisis. At present, the emphasis of rice genome research has been transferred from structure genomics to functional analysis. The discovery of new genes and annotation of gene function was believed to be an important issue in functional genomics research. In this article, the sequencing and functional research of the rice genome were reviewed. These results may provide some useful clues for rice genetic engineering and breeding practices.

Computational Biology↗
PubMed2000

Large-scale functional analysis using peptide or protein arrays.

The array format for analyzing peptide and protein function offers an attractive experimental alternative to traditional library screens. Powerful new approaches have recently been described, ranging from synthetic peptide arrays to whole proteins expressed in living cells. Comprehensive sets of purified peptides and proteins permit high-throughput screening for discrete biochemical properties, whereas formats involving living cells facilitate large-scale genetic screening for novel biological activities. In the past year, three major genome-scale studies using yeast as a model organism have investigated different aspects of protein function, including biochemical activities, gene disruption phenotypes, and protein-protein interactions. Such studies show that protein arrays can be used to examine in parallel the functions of thousands of proteins previously known only by their DNA sequence.

Peptide Library↗
PubMed2006

Characteristic length scale of electric transport properties of genomes.

A tight-binding model together with a statistical method are used to investigate the relation between the sequence-dependent electric transport properties and the sequences of protein-coding regions of complete genomes. A correlation parameter Omega is defined to analyze the relation. For some particular propagation length w max, the transport behaviors of the coding and noncoding sequences are very different and the correlation reaches its maximal value Omega max. w max and Omega max are characteristic values for each species. A possible reason for the difference between the features of transport properties in the coding and noncoding regions is the mechanism of DNA damage repair processes together with natural selection.

Computer Simulation↗
PubMedDate not supplied

Sequencing the yeast genome: the European effort.

For ethical, practical and economic reasons, scientists have traditionally relied on model organisms for biological research. Although model organisms do not always quite constitute the 'real thing', the significant advantages of their use contribute to making their study a viable alternative. The decision to use a specific model, particularly in large-scale studies such as genome projects, will be governed not only by biological consideration, but also by the prevailing financial and organizational infrastructure and expertise of the research community.

Chromosome Mapping↗