Refine
Year of publication
Document Type
- Article (52)
- Postprint (13)
- Review (2)
- Monograph/Edited Volume (1)
Language
- English (68)
Is part of the Bibliography
- yes (68)
Keywords
- Quantitative Trait Locus (4)
- Quantitative Trait Locus analysis (4)
- metabolomics (4)
- recombinant inbred line (4)
- Gene Ontology (2)
- Glioma (2)
- Partial Little Square (2)
- Phosphorylation Site (2)
- dominance effect (2)
- feature selection (2)
- gene expression (2)
- heterosis (2)
- phosphorylated amino acid (2)
- prediction (2)
- recombinant inbred line population (2)
- single nucleotide polymorphism mapping (2)
- slim term (2)
- Algebraic geometry (1)
- Arabidopsis thaliana (1)
- Bifurcation parameters (1)
- Biomass (1)
- Calvin cycle (1)
- Complexity (1)
- Conjunctive Normal Form (1)
- Constraint-based approaches (1)
- Correlation networks (1)
- DNA methylation (1)
- Data integration (1)
- Dickkopf 1 (1)
- Disjunctive Normal Form (1)
- Docking interactions (1)
- Evolution (1)
- External structural measures (1)
- Full Adder (1)
- Fusion (1)
- Gap junction (1)
- Gene function prediction (1)
- Gene regulatory network (1)
- Gene structure (1)
- Genotype Inference (1)
- Gliomas (1)
- Graph theory (1)
- HOMA (1)
- Haplotype Inference (1)
- Human mesenchymal stem cells (1)
- Hybrid prediction (1)
- Hypoxia (1)
- Intercellular crosstalk (1)
- LASSO (1)
- Maize (1)
- Mesenchymal stem cell (1)
- Metabolic networks (1)
- Metabolite profiles (1)
- Microarray data (1)
- Multistationarity (1)
- Mutual Information (1)
- NP-completeness (1)
- Phenomics (1)
- Regression (1)
- Robustness (1)
- STAT6 (1)
- Sequence alignment (1)
- Signal-transduction (1)
- Small-world networks (1)
- Support vector machines (1)
- Syncytium (1)
- Th2 cells (1)
- Transcription factors (1)
- Transductive learning (1)
- U87 glioma cells (1)
- Zea mays (1)
- action language (1)
- algorithms (1)
- answer set programming (1)
- arabidopsis (1)
- balance analysis (1)
- biochemical networks (1)
- biological network model (1)
- biological robustness (1)
- biomarker (1)
- biomass (1)
- centrality (1)
- combinatorics (1)
- comparative proteomics (1)
- computational biochemistry (1)
- computational molecular biology (1)
- couple reaction (1)
- coupling relationship (1)
- databases (1)
- decision tree (1)
- differential gene expression (1)
- efficient (1)
- endothelial progenitor cell (1)
- fasting glucose (1)
- functional genomics (1)
- gene expression matrix (1)
- gene-expression (1)
- genetic variability (1)
- hematopoietic stem cell (1)
- heterogeneous tissue (1)
- homogeneous cell population (1)
- impaired glucose tolerance (1)
- information (1)
- insulin (1)
- insulin resistance (1)
- kidney cancer (1)
- linear programming problem (1)
- linkage disequilibrium (1)
- lipoxygenase (1)
- mass accuracy (1)
- metabolic network (1)
- metabolic networks (1)
- metabolic regulation (1)
- metabolism (1)
- metabolite (1)
- metabolite profiling (1)
- metastasis (1)
- microarray data (1)
- microdissection (1)
- models (1)
- morphological analysis (1)
- muscle development (1)
- null model (1)
- package (1)
- pathways (1)
- phenotype (1)
- plant biology (1)
- plasma (1)
- polycystic ovary syndrome (1)
- potato (1)
- potato tuber (1)
- principal component (1)
- proinsulin (1)
- protease inhibitor (1)
- protein isoforms (1)
- pure parsimony (1)
- quantile normalization (1)
- random forest (1)
- randomization (1)
- reconstruction (1)
- regression (1)
- regular exercise training (1)
- resistance (1)
- reversible reaction (1)
- saccharomyces-cerevisiae (1)
- seedlings (1)
- selection (1)
- significance (1)
- solanum (1)
- stress-response (1)
- subcellular localization (1)
- support vector machine (1)
- systems biology (1)
- transcript profiling (1)
- trehalose synthesis (1)
- type 2 diabetes (1)
- type 2 diabetes mellitus (1)
Natural genetic diversity provides a powerful tool to study the complex interrelationship between metabolism and growth. Profiling of metabolic traits combined with network-based and statistical analyses allow the comparison of conditions and identification of sets of traits that predict biomass. However, it often remains unclear why a particular set of metabolites is linked with biomass and to what extent the predictive model is applicable beyond a particular growth condition. A panel of 97 genetically diverse Arabidopsis (Arabidopsis thaliana) accessions was grown in near-optimal carbon and nitrogen supply, restricted carbon supply, and restricted nitrogen supply and analyzed for biomass and 54 metabolic traits. Correlation-based metabolic networks were generated from the genotype-dependent variation in each condition to reveal sets of metabolites that show coordinated changes across accessions. The networks were largely specific for a single growth condition. Partial least squares regression from metabolic traits allowed prediction of biomass within and, slightly more weakly, across conditions (cross-validated Pearson correlations in the range of 0.27-0.58 and 0.21-0.51 and P values in the range of <0.001-<0.13 and <0.001-<0.023, respectively). Metabolic traits that correlate with growth or have a high weighting in the partial least squares regression were mainly condition specific and often related to the resource that restricts growth under that condition. Linear mixed-model analysis using the combined metabolic traits from all growth conditions as an input indicated that inclusion of random effects for the conditions improves predictions of biomass. Thus, robust prediction of biomass across a range of conditions requires condition-specific measurement of metabolic traits to take account of environment-dependent changes of the underlying networks.
Rising demand for food and bioenergy makes it imperative to breed for increased crop yield. Vegetative plant growth could be driven by resource acquisition or developmental programs. Metabolite profiling in 94 Arabidopsis accessions revealed that biomass correlates negatively with many metabolites, especially starch. Starch accumulates in the light and is degraded at night to provide a sustained supply of carbon for growth. Multivariate analysis revealed that starch is an integrator of the overall metabolic response. We hypothesized that this reflects variation in a regulatory network that balances growth with the carbon supply. Transcript profiling in 21 accessions revealed coordinated changes of transcripts of more than 70 carbon-regulated genes and identified 2 genes (myo-inositol-1- phosphate synthase, a Kelch-domain protein) whose transcripts correlate with biomass. The impact of allelic variation at these 2 loci was shown by association mapping, identifying them as candidate lead genes with the potential to increase biomass production.
We describe an approach to modeling biological networks by action languages via answer set programming. To this end, we propose an action language for modeling biological networks, building on previous work by Baral et al. We introduce its syntax and semantics along with a translation into answer set programming, an efficient Boolean Constraint Programming Paradigm. Finally, we describe one of its applications, namely, the sulfur starvation response-pathway of the model plant Arabidopsis thaliana and sketch the functionality of our system and its usage.
Motivation: Continued development of analytical techniques based on gas chromatography and mass spectrometry now facilitates the generation of larger sets of metabolite concentration data. An important step towards the understanding of metabolite dynamics is the recognition of stable states where metabolite concentrations exhibit a simple behaviour. Such states can be characterized through the identification of significant thresholds in the concentrations. But general techniques for finding discretization thresholds in continuous data prove to be practically insufficient for detecting states due to the weak conditional dependences in concentration data. Results: We introduce a method of recognizing states in the framework of decision tree induction. It is based upon a global analysis of decision forests where stability and quality are evaluated. It leads to the detection of thresholds that are both comprehensible and robust. Applied to metabolite concentration data, this method has led to the discovery of hidden states in the corresponding variables. Some of these reflect known properties of the biological experiments, and others point to putative new states