Causal interactions from proteomic profiles: Molecular data meet pathway knowledge

• Main Authors: Özgün Babur, Augustin Luna, Anil Korkut, Funda Durupinar, Metin Can Siper, Ugur Dogrusoz, Alvaro Sebastian Vaca Jacome, Ryan Peckner, Karen E. Christianson, Jacob D. Jaffe, Paul T. Spellman, Joseph E. Aslan, Chris Sander, Emek Demir
• Format: Article
• Language: English
• Published: Elsevier 2021-06-01
• Series: Patterns
• Subjects: proteomics; causal pathway analysis; cancer
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666389921000830

Summary: We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http://causalpath.org. The bigger picture: Molecular profiling of biological organisms provides us with a great amount of information on cellular differences, but converting it to mechanistic insights is still a very challenging task. A prominent approach is to integrate new measurements with the mechanistic knowledge described in the scientific literature and build a model that is supported by both. Although this can be done in many ways, an adept approach will use the literature knowledge in detail and follow high standards of logical reasoning while integrating the known and the new. This article describes an approach that utilizes the details in human biological pathways to identify pairs of changes with a likely cause-effect relation within. The approach automatically converts comparative proteomic and other molecular profiles into hypotheses of differentially active mechanistic relations that explain how the profiles came to be.