77
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Network, degeneracy and bow tie. Integrating paradigms and architectures to grasp the complexity of the immune system

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Recently, the network paradigm, an application of graph theory to biology, has proven to be a powerful approach to gaining insights into biological complexity, and has catalyzed the advancement of systems biology. In this perspective and focusing on the immune system, we propose here a more comprehensive view to go beyond the concept of network. We start from the concept of degeneracy, one of the most prominent characteristic of biological complexity, defined as the ability of structurally different elements to perform the same function, and we show that degeneracy is highly intertwined with another recently-proposed organizational principle, i.e. 'bow tie architecture'. The simultaneous consideration of concepts such as degeneracy, bow tie architecture and network results in a powerful new interpretative tool that takes into account the constructive role of noise (stochastic fluctuations) and is able to grasp the major characteristics of biological complexity, i.e. the capacity to turn an apparently chaotic and highly dynamic set of signals into functional information.

          Related collections

          Most cited references92

          • Record: found
          • Abstract: found
          • Article: not found

          Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise.

          A major goal of biology is to provide a quantitative description of cellular behaviour. This task, however, has been hampered by the difficulty in measuring protein abundances and their variation. Here we present a strategy that pairs high-throughput flow cytometry and a library of GFP-tagged yeast strains to monitor rapidly and precisely protein levels at single-cell resolution. Bulk protein abundance measurements of >2,500 proteins in rich and minimal media provide a detailed view of the cellular response to these conditions, and capture many changes not observed by DNA microarray analyses. Our single-cell data argue that noise in protein expression is dominated by the stochastic production/destruction of messenger RNAs. Beyond this global trend, there are dramatic protein-specific differences in noise that are strongly correlated with a protein's mode of transcription and its function. For example, proteins that respond to environmental changes are noisy whereas those involved in protein synthesis are quiet. Thus, these studies reveal a remarkable structure to biological noise and suggest that protein noise levels have been selected to reflect the costs and potential benefits of this variation.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A measure for brain complexity: relating functional segregation and integration in the nervous system.

            In brains of higher vertebrates, the functional segregation of local areas that differ in their anatomy and physiology contrasts sharply with their global integration during perception and behavior. In this paper, we introduce a measure, called neural complexity (CN), that captures the interplay between these two fundamental aspects of brain organization. We express functional segregation within a neural system in terms of the relative statistical independence of small subsets of the system and functional integration in terms of significant deviations from independence of large subsets. CN is then obtained from estimates of the average deviation from statistical independence for subsets of increasing size. CN is shown to be high when functional segregation coexists with integration and to be low when the components of a system are either completely independent (segregated) or completely dependent (integrated). We apply this complexity measure in computer simulations of cortical areas to examine how some basic principles of neuroanatomical organization constrain brain dynamics. We show that the connectivity patterns of the cerebral cortex, such as a high density of connections, strong local connectivity organizing cells into neuronal groups, patchiness in the connectivity among neuronal groups, and prevalent reciprocal connections, are associated with high values of CN. The approach outlined here may prove useful in analyzing complexity in other biological domains such as gene regulation and embryogenesis.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A comprehensive pathway map of epidermal growth factor receptor signaling

              The epidermal growth factor receptor (EGFR) signaling pathway is one of the most important pathways that regulate growth, survival, proliferation, and differentiation in mammalian cells. Reflecting this importance, it is one of the best-investigated signaling systems, both experimentally and computationally, and several computational models have been developed for dynamic analysis. A map of molecular interactions of the EGFR signaling system is a valuable resource for research in this area. In this paper, we present a comprehensive pathway map of EGFR signaling and other related pathways. The map reveals that the overall architecture of the pathway is a bow-tie (or hourglass) structure with several feedback loops. The map is created using CellDesigner software that enables us to graphically represent interactions using a well-defined and consistent graphical notation, and to store it in Systems Biology Markup Language (SBML).
                Bookmark

                Author and article information

                Journal
                Theor Biol Med Model
                Theoretical Biology & Medical Modelling
                BioMed Central
                1742-4682
                2010
                11 August 2010
                : 7
                : 32
                Affiliations
                [1 ]Interdept. Center "Luigi Galvani" for Bioinformatics, Biophysics and Biocomplexity (CIG), University of Bologna, Via F. Selmi 3, 40126 Bologna, Italy
                [2 ]Department of Experimental Pathology, University of Bologna, Via San Giacomo 12, 40126 Bologna, Italy
                [3 ]Institute for Women's Health, University College London, Gower Street, London, WC1E 6BT, UK, and Dept. of Mathematics, University College London, Gower Street, London, WC1E 6BT, UK
                [4 ]Institut für Biochemie, Charité - Universitätsmedizin Berlin, Charité Centrum 2 - Grundlagenmedizin, Oudenarder Strasse 16, 13347 Berlin, Germany
                Article
                1742-4682-7-32
                10.1186/1742-4682-7-32
                2927512
                20701759
                9061c361-0979-4b87-9b78-5985a30a5fff
                Copyright ©2010 Tieri et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 30 June 2010
                : 11 August 2010
                Categories
                Research

                Quantitative & Systems biology
                Quantitative & Systems biology

                Comments

                Comment on this article