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Pathway Reconstruction Algorithms

The graph algorithms below have been used (or have the potential to be used) for the pathway reconstruction task. This task is typically formulated as follows: given a protein-protein interaction (PPI) network (represented as a graph, which may be weighted and/or directed) and a set of proteins of interest, return a subnetwork of the PPI graph that contains the proteins of interest.

ANAT

References:

• Yosef et al. Toward accurate reconstruction of functional protein networks. Molecular Systems Biology. 2009. doi:10.1038/msb.2009.3
• Yosef et al. ANAT: a tool for constructing and analyzing functional protein networks. Science Signaling. 2011. doi:10.1126/scisignal.2001935
• Almozlino et al. ANAT 2.0: reconstructing functional protein subnetworks. BMC Bioinformatics. 2017. doi:10.1186/s12859-017-1932-1

NetBox

References:

• Cerami et al. Automated network analysis identifies core pathways in glioblastoma. PLoS One. 2010. doi:10.1371/journal.pone.0008918
• Liu et al. netboxr: Automated discovery of biological process modules by network analysis in R. PLoS One. 2020. doi:10.1371/journal.pone.0234669

ResponseNet

References:

• Yeger-Lotem et al. Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity. Nature Genetics. 2009. doi:10.1038/ng.337
• Lan et al. ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data. Nucleic Acids Research. 2011. doi:10.1093/nar/gkr359
• Basha et al., ResponseNet2.0: revealing signaling and regulatory pathways connecting your proteins and genes–now with human data. Nucleic Acids Research. 2013. doi:10.1093/nar/gkt532
• Basha et al. ResponseNet v.3: revealing signaling and regulatory pathways connecting your proteins and genes across human tissues. Nucleic Acids Research. 2019. doi:10.1093/nar/gkz421

Prize Collecting Steiner Forest (PCSF): OmicsIntegrator1 and OmicsIntegrator2

References:

• Huang and Fraenkel. Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks. Science Signaling. 2009. doi:10.1126/scisignal.2000350
• Tuncbag et al. Simultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem. Journal of Computational Biology. 2013. doi:10.1089/cmb.2012.0092
• Gitter et al. Sharing information to reconstruct patient-specific pathways in heterogeneous diseases. Pacific Symposium on Biocomputing. 2014. doi:10.1142/9789814583220_0005
• Tuncbag et al., Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package. PLoS Computational Biology. 2016. doi:10.1371/journal.pcbi.1004879

One of the parameter options for OmicsIntegraor1 is dummy_mode. There are 4 dummy mode possibilities:

1. terminals: connect the dummy node to all nodes that have been assigned prizes
2. all: connect the dummy node to all nodes in the interactome i.e. full set of nodes in graph
3. others: connect the dummy node to all nodes that are not terminal nodes i.e. nodes w/o prizes
4. file: connect the dummy node to a specific list of nodes provided in a file To support the file dummy node logic as part of OmicsIntegrator1, you can either add a separate dummy.txt file (and add this to the node_files argument in config.yaml ) or add a dummy column node attribute to a file that contains NODEID, prize, source, etc.

PathLinker

PathLinker takes as input (1) a weighted, directed PPI network, (2) two sets of nodes: a source set (representing receptors of a pathway of interest) and a target set (representing transcriptional regulators of a pathway of interest), and (3) an integer k. PathLinker efficiently computes the k-shortest paths from any source to any target and returns the subnetwork of the top k paths as the pathway reconstruction. Later work expanded PathLinker by incorporating protein localization information to re-score tied paths, dubbed Localized PathLinker (LocPL).

References:

• Ritz et al. Pathways on demand: automated reconstruction of human signaling networks. NPJ Systems Biology and Applications. 2016. doi:10.1038/npjsba.2016.2
• Youssef, Law, and Ritz. Integrating protein localization with automated signaling pathway reconstruction. BMC Bioinformatics. 2019. doi:10.1186/s12859-019-3077-x

Pathway Reconstruction Augmenter (PRAUG)

References:

• Rubel and Ritz. Augmenting Signaling Pathway Reconstructions. Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB '20). 2020. doi:10.1145/3388440.3412411

Parameter Advising for Pathway Reconstruction Methods

References:

• Magnano and Gitter. Automating parameter selection to avoid implausible biological pathway models. NPJ Systems Biology and Applications. 2021. doi:10.1038/s41540-020-00167-1