A review of software tools for pathway crosstalk inference

Background: We are living in an era that is in general characterized by a lot of data but little information. An enormous amount of biological data collected over several years is now presented as annotations and databases. In this context, all this data properly combined and grouped has great potential for enabling novel discoveries which would then, finally and hopefully, lead to advances in biology and medicine. The inference of different kinds of relations between pathways constitutes a challenging step towards the analysis of all these sources of biological data. Objective: This review article aims at outlining several methods that analyze associations between pathways starting from different sources of information, namely the internet, databases, and/or gene expression data. Methods: The article consists of a summary of the most important methods for pathway networks inference and arranges them according to the data they use as well as the findings they provide. Results: The advantages and drawbacks of each considered methodology are presented, as well as a taxonomy tree and summary table as an overview of the discussion. Conclusion: The methods explained in this paper consist especially of those that explore the concept of associations between pathways using microarray experimental data and/or topological or curated information. Each strategy was introduced, classified and analyzed. The identification of different kinds of associations between pathways plays a central role in systems biology, revealing information which is undetectable at a gene level. Therefore, a comprehensible understanding of the benefits and limitations of these approaches could be the key to the development of new computational strategies for genome-wide analysis.