Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET.

ABSTRACT: High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell's response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using co

SUBMITTER: Rodriguez A 

PROVIDER: S-EPMC4461287 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature