ios - Predicate matching one to many relationship coredata - Stack Overflow
Core Data and Swift: Managed Objects and Fetch Requests . If a relationship has an inverse relationship, then Core Data takes care of this . easy to use, but predicates are what really makes fetching powerful in Core Data. This year's workshop contains work on newsfeeds , clinical data [2, 3, 12, 13], full text , and .. Unsupervised Learning of the Morpho-Semantic Relationship in MEDLINE. W. John between the core predicate and the frame elements. ( Ruggieri lase” contains “that phenylalanine hydroxylase” as. creased availability of quantitative experimental data has of SBML Core and represented in the Process Description ferential equations , Petri nets  and predicate .. model is unexpectedly able to synthesize phenylalanine, an . number of mathematical relationships (i.e., reactions and.
An assessment of the ability of the model to correctly predict gene essentiality was also performed; in this case the predictions of growth were compared to the list of essential genes alone, with no reference to the minimal medium experimental data. ROC analysis was used to compare this logical model to iND [ 5 ], a state-of-the-art Flux Balance Analysis FBA model [ 56 ] iND reflects a naming scheme for systems biology models, i refers to a in silico model, ND reflects the creators of the model Natalie Duarte and represents the number of genes included in the model.
Both models have also been compared with the predicted growth outcomes generated by a simple majority class classifier, as well as the probability that prediction success was purely random.
Systems biology and the modelling of biochemical networks Systems Biology [ 7 - 10 ] represents a shift towards a synergistic approach to whole cell modelling, with the concentration on the interactions of many inter-related components rather than the behaviour of the individual components.
Advances in mathematics and computer science have led to the development of diverse techniques and formalisms allowing the in silico modelling of these cell systems. All computer models represent varying degrees of abstraction from the observable phenomena they represent, from coarse large scale models that capture only essential interactions and components of the system e.
Using a logical model to predict the growth of yeast
KEGG [ 1112 ] and EcoCyc [ 13 ], to higher fidelity representations of detailed functioning and interactions of a smaller set of components [ 14 ]. There are two main groups of modelling techniques used to represent metabolic networks: ODEs are the most established modelling representation in science. In using ODEs to model metabolism the concentration of each metabolite is calculated by a single ODE encapsulating all the reactions where the metabolite is synthesised or consumed, with fluxes determining the transformations to and from other metabolites in the network.
The use of ODE models is the main technique of the quantitative sciences. There are also now a number of specialised ODE modelling packages for metabolism, e.
Despite this, the current application of ODEs to modelling large-scale metabolism has a number of serious problems: Flux Balance Analysis FBA [ 5619 - 21 ] is currently the most common approach to quantitatively modelling metabolism. Standard FBA assumes a steady state model of cell metabolism; although more recent developments in FBA [ 20 ] have extended this to dynamic flux balance analysis that is capable of modelling cells with some state change.
Cell reactions are modelled by two matrices: FBA models of the metabolism of a number of organisms exist, e. The steady state assumption relaxed for dynamic FBA however and the inaccuracies of the unknown fluxes can lead to inaccuracies in the overall simulation [ 22 ].
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However, increased experimental evidence can decrease these inaccuracies. A number of flux based alternatives to FBA have been developed that address the changes in fluxes that occur after major environmental or other perturbations such as gene knockouts: Both make use of distance measures to determine a point in flux space that is closest to the wild type flux distribution, in keeping with a homeostasis hypothesis. MOMA minimises the changes to each individual flux, thereby the overall network is as similar as possible to the wild type.
In contrast, ROOM minimises the number of significant flux changes, thereby better approximating how short alternative pathways allow redundancy in metabolic networks. Elementary mode analysis [ 25 ] determines the set of smallest sub-networks of a larger metabolic network that still allow a metabolic steady state to be reached. Each elementary mode represents an alternative that the organism may use in conditions of perturbation. However there can be a large number of elementary modes for even a moderately large metabolic network, indicating that there may be problems scaling this approach to whole metabolism networks.
Logical and Graph LG based models [ 111 - 13 ] are the commonest qualitative representations for modelling metabolism. Graph based models are used in metabolic databases e.
Metabolic pathways are represented explicitly, each metabolite is a node in the graph and edges represent the chemical transformations found in the reactions comprising the pathway.
Edges are further annotated by the enzyme s that catalyse the reactions, and these are in turn are related to the gene s that encode the enzymes. Lemke et al [ 2627 ] have developed a graph-based model of the metabolic network of E. Metabolic damage is defined as the number of metabolites that can no longer be produced by the organism. Logical models may use computationally efficient forms of both propositional and FOL Prolog as their representation language [ 28 ]. As in graph models, the reaction network is represented by a series of metabolite nodes and chemical transformation arcs, however the increased expressive power of logic can allow more accurate representations of the relationships between the genes, enzymes and gene products used as annotations to the reactions, as well as various cellular compartments.
However, studies on the WRKY that regulate tanshinones and phenolics biosynthesis are limited. Hairy roots of S. Taken together, this study has provided a significant resource that could be used for further research on SmWRKY in S.
Introduction Salvia miltiorrhiza Bunge is a well-known Chinese herb with significant medicinal and economic value. The major bioactive constituents of S. More than 40 tanshinones tanshinone I, tanshinone IIA, cryptotanshinone, dihydrotanshinone I, and so on and 20 hydrophilic phenolic acids salvianolic acid B, rosmarinic acid, dihydroxyphenyllactic acid, and lithospermic acid have been isolated and identified from S.
Phenolic acids in S. The biosynthetic pathway of rosmarinic acid RA is well characterized in plants [ 45 ]. Most of the key biosynthetic enzyme genes of those pathways have been cloned [ 67891011 ]. However, the regulatory mechanisms of tanshinones and phenolic acids biosynthesis are largely unresolved.
Adding Core Data entity relationships: lightweight vs heavyweight migration
Tanshinones and phenolic aicds were major secondary metabolites in S. Many secondary metabolites play important roles in the biology of pants; for example, anthocyanins are pigments in fruits and flowers, generating colorful form and thereby conferring the quality of fruits [ 12 ]. In tomato plants, quercetin derivatives were dramatically increased in response to low temperatures when under conditions of nitrogen starvation, and especially at higher light intensities [ 13 ].
In addition, a cold-acclimation acquisition olive-tree, named Canino, produces more unsaturation and cutinisation than cold-sensitive genotypes [ 14 ]. For people, plant secondary metabolites, especially phenolic acid, are important health-promoting pigments because of their potential antiradical scavenging activities [ 15 ]. Transcription factors play a central role in the progress of plant secondary metabolite biosynthesis [ 16 ].
In addition to this TFs-regulated phenolic acid production, the basic leucine zipper bZIP transcription factor OsTGAP1 that was overexpressed in rice can hyperaccumulate momilactones and phytocassanes, which function as antimicrobial secondary metabolites in response to pathogen attacks [ 20 ]. The role of WRKY responds to abiotic and biotic stresses, such as wounding, drought, heat, and pathogens; these topics that have been of extensive concern recently [ 242526272829 ].