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IET Syst Biol. 2016 Apr;10(2):76-85. doi: 10.1049/iet-syb.2015.0009.

Constrained inference of protein interaction networks for invadopodium formation in cancer.

Author information

1
Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA.
2
Departments of Biology and Computer Science, Duke University, Durham, NC 27708, USA.
3
Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA.
4
Department of Pharmaceutical Sciences, University of New Mexico, Albuquerque, NM 87131, USA.
5
Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA. joemsong@cs.nmsu.edu.

Abstract

Integrating prior molecular network knowledge into interpretation of new experimental data is routine practice in biology research. However, a dilemma for deciphering interactome using Bayes' rule is the demotion of novel interactions with low prior probabilities. Here the authors present constrained generalised logical network (CGLN) inference to predict novel interactions in dynamic networks, respecting previously known interactions and observed temporal coherence. It encodes prior interactions as probabilistic logic rules called local constraints, and forms global constraints using observed dynamic patterns. CGLN finds constraint-satisfying trajectories by solving a k-stops problem in the state space of dynamic networks and then reconstructs candidate networks. They benchmarked CGLN on randomly generated networks, and CGLN outperformed its alternatives when 50% or more interactions in a network are given as local constraints. CGLN is then applied to infer dynamic protein interaction networks regulating invadopodium formation in motile cancer cells. CGLN predicted 134 novel protein interactions for their involvement in invadopodium formation. The most frequently predicted interactions centre around focal adhesion kinase and tyrosine kinase substrate TKS4, and 14 interactions are supported by the literature in molecular contexts related to invadopodium formation. As an alternative to the Bayesian paradigm, the CGLN method offers constrained network inference without requiring prior probabilities and thus can promote novel interactions, consistent with the discovery process of scientific facts that are not yet in common beliefs.

PMID:
26997662
PMCID:
PMC4804358
DOI:
10.1049/iet-syb.2015.0009
[Indexed for MEDLINE]
Free PMC Article

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