Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'

Area of Science:

• Systems Biology
• Computational Biology
• Bioinformatics

Background:

• Network inference from high-throughput data is crucial for understanding biological systems.
• Signaling networks of cancer-related proteins are key to identifying pathways influencing tumor development.
• Cancer cell lines provide models for time-resolved study of drug treatment responses.

Purpose of the Study:

• To develop and present a computational approach for network reconstruction from longitudinal data.
• To enable hypothesis generation regarding potential interference points in biological networks.

Main Methods:

• Implementation of a novel network reconstruction approach in the R-package 'ddepn'.
• Utilizing a Markov Chain Monte Carlo method for sampling network structures with activation and inhibition edges.
• Extending a prior model to penalize deviations from a reference network and incorporating scale-free network properties.

Main Results:

• The R-package 'ddepn' facilitates network inference from longitudinal high-throughput data.
• Novel methods allow for the reconstruction of signaling networks with directed edges (activation/inhibition).
• The package supports different prior models, including those learning scale-free network properties.

Conclusions:

• The 'ddepn' R-package is available on R-Forge and CRAN for convenient use.
• It enables network inference from longitudinal data using advanced sampling-based algorithms.
• This tool aids in the analysis of biological networks, particularly in cancer research.