Information Transduction Capacity of Noisy Biochemical Signaling Networks

• Type of Material: Article
• Authors: Raymond Cheong, Johns Hopkins University; Alex Rhee, Johns Hopkins University; Chiaochun Joanne Wang, Johns Hopkins University; Ilya Nemenman, Emory University; Andre Levchenko, Johns Hopkins University
• Language: English
• Date: 2011-10-21
• Publisher: Volgogradskii Gosudarstvennyi Universitet (Volgograd State University)
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2011, American Association for the Advancement of Science
• Final Published Version (URL): http://dx.doi.org/10.1126/science.1204553
• Title of Journal or Parent Work: Вестник Волгоградского государственног... / Science Journal of Volgograd State University. History. Area Studies. International Relations
• ISSN: 1998-9938
• Volume: 334
• Issue: 6054
• Start Page: 354
• End Page: 358
• Grant/Funding Information: This work was supported by the National Institutes of Health (GM072024, R.C., A.R., C.J.W., A.L.), the Medical Scientist Training Program at the Johns Hopkins University (R.C.), and, in early stages of the work, the Los Alamos National Laboratory Directed Research and Development program (I.N.).
• Keywords: Multidisciplinary Sciences; Science & Technology - Other Topics; TEMPORAL CONTROL; OSCILLATIONS; SINGLE-CELL; Science & Technology; ALPHA; DYNAMICS; NF-KAPPA-B; ACTIVATION; STOCHASTIC GENE-EXPRESSION; POPULATION; VARIABILITY
• Research Categories: Engineering, Biomedical; Biophysics, Medical

Abstract

Molecular noise restricts the ability of an individual cell to resolve input signals of different strengths and gather information about the external environment. Transmitting information through complex signaling networks with redundancies can overcome this limitation. We developed an integrative theoretical and experimental framework, based on the formalism of information theory, to quantitatively predict and measure the amount of information transduced by molecular and cellular networks. Analyzing tumor necrosis factor (TNF) signaling revealed that individual TNF signaling pathways transduce information sufficient for accurate binary decisions, and an upstream bottleneck limits the information gained via multiple integrated pathways. Negative feedback to this bottleneck could both alleviate and enhance its limiting effect, despite decreasing noise. Bottlenecks likewise constrain information attained by networks signaling through multiple genes or cells.