Causal interactions from proteomic profiles: Molecular data meet pathway knowledge

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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number100257
JournalPatterns
Volume2
Issue number6
DOIs
StatePublished - Jun 11 2021

Keywords

  • DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems
  • cancer
  • causal pathway analysis
  • proteomics

ASJC Scopus subject areas

  • Decision Sciences(all)

Fingerprint

Dive into the research topics of 'Causal interactions from proteomic profiles: Molecular data meet pathway knowledge'. Together they form a unique fingerprint.

Cite this