Pathway models and microarray gene expression data are often the first resources used when searching for biological mechanisms. Many questions can be addressed by these resources, including: (1) Can gene expression data be used to successfully reconstruct a known pathway? (2) What other hidden molecular factors should be included to more fully understand the mechanism? (3) Can we model interactions among multiple pathways? and (4) How can prior knowledge help refine pathway models. MARIMBA targets to answer these questions.
MARIMBA is a web-based pipeline to model biological pathways using Bayesian network. Bayesian networks are graphical models that describe causal or apparently causal interactions between variables. Bayesian networks are ideal for merging pathway models and gene expression data due to the network’s flexibility, robustness to error, and human interpretability. Compared to other modeling methods, Bayesian networks can be learned automatically by searching through the space of network topologies and retaining the most significant top scoring networks.
MARIMBA integrates gene expression data from publicly available databases (e.g., NCBI GEO and AfCS) or user-defined data with existing pathway knowledge (e.g., KEGG Pathways). MARIMBA provides a user-friendly graphical interface environment to simplify dataset selection, probeset/gene inclusion, observational file processing, settings selection, Bayesian network execution, and results visualization. MARIMBA also offers automated data processing tools including foldchange and clustering. MARIMBA allows users to store and update their own data and modeling results. New algorithms such as BN+1 have been developed to address fundamental pathway-related questions. MARIMBA is implemented using a PHP interface, MySQL database, and Python.
Thank you for using MARIMBA. Your suggestions and comments are welcome and appreciated!
MARIMBA is optimized in
