An information-theoretic framework for deciphering pleiotropic and noisy biochemical signaling

• Published in: Nature communications 2018-11, Vol.9 (1), p.4591-9, Article 4591
• Main Authors: Jetka, Tomasz, Nienałtowski, Karol, Filippi, Sarah, Stumpf, Michael P. H., Komorowski, Michał
• Format: Article
• Language: English
• Subjects: 631/250/127/1212; 631/553/1044; 631/553/1806; 631/553/2708; 631/553/2709; Biochemistry; Coding; Complexity; Effectors; Genetic Pleiotropy; Heterogeneity; Humanities and Social Sciences; Information Theory; Interferon; Interferon Type I - metabolism; Interferons - metabolism; Kinetics; Modelling; Models, Biological; multidisciplinary; Science; Science (multidisciplinary); Signal Transduction; Signaling
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-018-07085-1

Many components of signaling pathways are functionally pleiotropic, and signaling responses are marked with substantial cell-to-cell heterogeneity. Therefore, biochemical descriptions of signaling require quantitative support to explain how complex stimuli (inputs) are encoded in distinct activities of pathways effectors (outputs). A unique perspective of information theory cannot be fully utilized due to lack of modeling tools that account for the complexity of biochemical signaling, specifically for multiple inputs and outputs. Here, we develop a modeling framework of information theory that allows for efficient analysis of models with multiple inputs and outputs; accounts for temporal dynamics of signaling; enables analysis of how signals flow through shared network components; and is not restricted by limited variability of responses. The framework allows us to explain how identity and quantity of type I and type III interferon variants could be recognized by cells despite activating the same signaling effectors. Signalling responses are marked by substantial cell-to-cell variability. Here, the authors propose an information theoretic framework that accounts for multiple inputs and temporal dynamics to analyse how signals flow through shared network components.