While toxicokinetic modeling did not explain mixture interactions, gene expression profiling and further Gene Ontology-based functional genomics analysis provided clues that the decrement of toxicity may arise from the development of specific toxicodynamics.
Cancer cells propagated in three-dimensional (3D) culture systems exhibit physiologically relevant cell-cell and cell-matrix interactions, gene expression and signaling pathway profiles, heterogeneity and structural complexity that reflect in vivo tumors.
Microgravity research, modulations in mechanical culture conditions (modeled microgravity), and shear stress have spawned innovative approaches to understanding the dynamics of cellular interactions, gene expression, and differentiation in several cellular systems.
The latter immediately suggests the concept of information and reveals an additional function performed by gene expression and regulated by cell interactions: gene expression provides positional information [1].
These tools variously rely on functional associations, protein-protein interactions, gene expression data, sequence and structure properties or combinations thereof to select candidate genes [16], [17], [18], [19], [20], [21], [22], [23], [24], [25].
GeneDistiller includes the following data: Genes, gene positions, gene RIFs, gene ontology, cellular localisation of gene products, transcripts, exons, OMIM reports, mouse phenotypes, protein-protein interactions, gene expression data, protein domains, SNP markers, STR markers.
Next, we analyzed moonlighting proteins in terms of protein-protein interaction, gene expression, phylogenetic profile, and genetic interaction networks.
Visualization in VisAnt allows predictions to be integrated with many other large scale genomic datasets including protein-protein interaction, gene expression, GO functional annotation, and genetic interaction.
In addition to epistasis and gene environment interaction, gene expression in general and epigenetics in particular have been mentioned in relation to missing heritability.
Recent literature on protein function prediction focuses on integrating multiple sources of evidence (e.g. physical interactions, genetic interaction, gene expression data) to assign proteins to processes [ 1- 4] or to predict functional associations or interactions between related proteins [ 5- 10].
Because of the flexibility of the network model base on the algorithmic predict from high throughput gene expression tests, we can look at snapshots of protein-protein interaction, gene expression regulatory networks and metabolism networks among different groups.
