title: Signalling entropy: A novel network-theoretical framework for systems analysis and interpretation of functional omic data.
abstract: A key challenge in systems biology is the elucidation of the underlying principles, or fundamental laws, which determine the cellular phenotype. Understanding how these fundamental principles are altered in diseases like cancer is important for translating basic scientific knowledge into clinical advances. While significant progress is being made, with the identification of novel drug targets and treatments by means of systems biological methods, our fundamental systems level understanding of why certain treatments succeed and others fail is still lacking. We here advocate a novel methodological framework for systems analysis and interpretation of molecular omic data, which is based on statistical mechanical principles. Specifically, we propose the notion of cellular signalling entropy (or uncertainty), as a novel means of analysing and interpreting omic data, and more fundamentally, as a means of elucidating systems-level principles underlying basic biology and disease. We describe the power of signalling entropy to discriminate cells according to differentiation potential and cancer status. We further argue the case for an empirical cellular entropy-robustness correlation theorem and demonstrate its existence in cancer cell line drug sensitivity data. Specifically, we find that high signalling entropy correlates with drug resistance and further describe how entropy could be used to identify the achilles heels of cancer cells. In summary, signalling entropy is a deep and powerful concept, based on rigorous statistical mechanical principles, which, with improved data quality and coverage, will allow a much deeper understanding of the systems biological principles underlying normal and disease physiology.
authors.affiliation: CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai Institute for Biological Sciences, 320 Yue Yang Road, Shanghai 200031, China; Statistical Cancer Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, London WC1E 6BT, UK. Electronic address: a.teschendorff@ucl.ac.uk.
authors.forename: Andrew E
authors.initials: AE
authors.lastname: Teschendorff
authors.affiliation: Department of Mathematics, King's College London, London WC2R 2LS, UK.
authors.forename: Peter
authors.initials: P
authors.lastname: Sollich
authors.affiliation: Department of Mathematics, King's College London, London WC2R 2LS, UK.
authors.forename: Reimer
authors.initials: R
authors.lastname: Kuehn
mesh_terms: D004351:Drug Resistance; D019277:Entropy; D008954:Models, Biological; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*
keywords: Cancer; Differentiation; Drug resistance; Entropy; Genomics; Network; Signalling; Stem cell