Static network structure can be used to model the phenotypic effects of perturbations in regulatory networks.

MOTIVATION: Biological processes are dynamic, whereas the networks that depict them are typically static. Quantitative modeling using differential equations or logic-based functions can offer quantitative predictions of the behavior of biological systems, but they require detailed experimental characterization of interaction kinetics, which is typically unavailable. To determine to what extent complex biological processes can be modeled and analyzed using only the static structure of the network (i.e. the direction and sign of the edges), we attempt to predict the phenotypic effect of perturbations in biological networks from the static network structure. RESULTS: We analyzed three networks from different sources: The EGFR/MAPK and PI3K/AKT network from a detailed experimental study, the TNF regulatory network from the STRING database and a large network of all NCI-curated pathways from the Protein Interaction Database. Altogether, we predicted the effect of 39 perturbations (e.g. by one or two drugs) on 433 target proteins/genes. In up to 82% of the cases, an algorithm that used only the static structure of the network correctly predicted whether any given protein/gene is upregulated or downregulated as a result of perturbations of other proteins/genes. CONCLUSION: While quantitative modeling requires detailed experimental data and heavy computations, which limit its scalability for large networks, a wiring-based approach can use available data from pathway and interaction databases and may be scalable. These results lay the foundations for a large-scale approach of predicting phenotypes based on the schematic structure of networks.

Comparative analysis of T4 DNA ligases and DNA polymerases used in chromosome conformation capture assays.

Three-dimensional (3-D) genome organization in the nuclear space affects various genomic functions. Circular chromosome conformation capture (4C-seq) is a powerful technique that allows researchers to measure long-range chromosomal interactions with a locus of interest across the entire genome. This method relies on enzymatic cleavage of cross-linked chromatin and consecutive ligation to create ligation junctions between physically adjacent loci, followed by PCR amplification of locus-specific associating loci. The enzymes used must meet 4C standards because variations in their efficiency and performance may affect the quality of the obtained data. Here we systematically compare the efficiency and reliability of different T4 DNA ligases and PCR DNA polymerases, assessing the most critical and technically challenging steps in 4C. The results of this analysis enable the use of cost-effective enzymes with superior specificity and efficiency for 4C and save time in screening for appropriate primers. This information provides users with flexibility in their experimental design and guidelines for adapting and testing any enzyme of choice for obtaining standardized results.