Quantification of degeneracy in biological systems for characterization of functional interactions between modules

• Type of Material: Article
• Authors: Yao Li, Georgia Institute of Technology; Gaurav Dwivedi, Georgia Institute of Technology; Wen Huang, University of Science & Technology of China; Melissa L. Kemp, Emory University; Yingfei Yi, Georgia Institute of Technology
• Language: English
• Date: 2012-06-07
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2012 Elsevier Ltd. All rights reserved.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.jtbi.2012.02.020
• Title of Journal or Parent Work: Journal of Theoretical Biology
• ISSN: 0022-5193
• Volume: 302
• Start Page: 29
• End Page: 38
• Grant/Funding Information: YY was supported by NSF grants DMS0708331, DMS1109201 and a scholarship from Jilin University.; WH was supported by NSFC, Fok Ying Tung Education Foundation, FANEDD (Grant 200520) and the Fundamental Research Funds for the Central Universities.; MLK was supported by a NIH New Innovator Award DP2OD006483.
• Keywords: COMPLEXITY; Degeneracy; Complexity; Biology; Life Sciences & Biomedicine; Systems biology; Mathematical & Computational Biology; INTERLEUKIN-4; Biological networks; Science & Technology; ERYTHROPOIETIN RECEPTOR; ROBUSTNESS; Life Sciences & Biomedicine - Other Topics; NETWORKS; PERTURBATIONS; REGULARITY; Robustness
• Research Categories: Engineering, Biomedical; Mathematics

Abstract

There is an evolutionary advantage in having multiple components with overlapping functionality (i.e degeneracy) in organisms. While theoretical considerations of degeneracy have been well established in neural networks using information theory, the same concepts have not been developed for differential systems, which form the basis of many biochemical reaction network descriptions in systems biology. Here we establish mathematical definitions of degeneracy, complexity and robustness that allow for the quantification of these properties in a system. By exciting a dynamical system with noise, the mutual information associated with a selected observable output and the interacting subspaces of input components can be used to define both complexity and degeneracy. The calculation of degeneracy in a biological network is a useful metric for evaluating features such as the sensitivity of a biological network to environmental evolutionary pressure. Using a two-receptor signal transduction network, we find that redundant components will not yield high degeneracy whereas compensatory mechanisms established by pathway crosstalk will. This form of analysis permits interrogation of large-scale differential systems for non-identical, functionally equivalent features that have evolved to maintain homeostasis during disruption of individual components.