9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Noise Reduction in Complex Biological Switches

      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

          Cells operate in noisy molecular environments via complex regulatory networks. It is possible to understand how molecular counts are related to noise in specific networks, but it is not generally clear how noise relates to network complexity, because different levels of complexity also imply different overall number of molecules. For a fixed function, does increased network complexity reduce noise, beyond the mere increase of overall molecular counts? If so, complexity could provide an advantage counteracting the costs involved in maintaining larger networks. For that purpose, we investigate how noise affects multistable systems, where a small amount of noise could lead to very different outcomes; thus we turn to biochemical switches. Our method for comparing networks of different structure and complexity is to place them in conditions where they produce exactly the same deterministic function. We are then in a good position to compare their noise characteristics relatively to their identical deterministic traces. We show that more complex networks are better at coping with both intrinsic and extrinsic noise. Intrinsic noise tends to decrease with complexity, and extrinsic noise tends to have less impact. Our findings suggest a new role for increased complexity in biological networks, at parity of function.

          Related collections

          Most cited references33

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

          Nature, nurture, or chance: stochastic gene expression and its consequences.

          Gene expression is a fundamentally stochastic process, with randomness in transcription and translation leading to cell-to-cell variations in mRNA and protein levels. This variation appears in organisms ranging from microbes to metazoans, and its characteristics depend both on the biophysical parameters governing gene expression and on gene network structure. Stochastic gene expression has important consequences for cellular function, being beneficial in some contexts and harmful in others. These situations include the stress response, metabolism, development, the cell cycle, circadian rhythms, and aging.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Robustness in simple biochemical networks.

            Cells use complex networks of interacting molecular components to transfer and process information. These "computational devices of living cells" are responsible for many important cellular processes, including cell-cycle regulation and signal transduction. Here we address the issue of the sensitivity of the networks to variations in their biochemical parameters. We propose a mechanism for robust adaptation in simple signal transduction networks. We show that this mechanism applies in particular to bacterial chemotaxis. This is demonstrated within a quantitative model which explains, in a unified way, many aspects of chemotaxis, including proper responses to chemical gradients. The adaptation property is a consequence of the network's connectivity and does not require the 'fine-tuning' of parameters. We argue that the key properties of biochemical networks should be robust in order to ensure their proper functioning.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Protein kinases and phosphatases: the yin and yang of protein phosphorylation and signaling.

                Bookmark

                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                08 February 2016
                2016
                : 6
                : 20214
                Affiliations
                [1 ]Microsoft Research, 21 Station Road , CB1 2FB, Cambridge, United Kingdom
                [2 ]University of Oxford, Department of Computer Science, Wolfson Building, Parks Road , Oxford OX1 3QD, United Kingdom
                [3 ]Randall Division of Cell and Molecular Biophysics, Institute for Mathematical and Molecular Biomedicine, King’s College London , WC2R 2LS, London, United Kingdom
                [4 ]Fondazione Edmund Mach, Via E. Mach 1, 38010 S . Michele all’Adige (TN), Italy
                [5 ]IMT Institute for Advanced Studies, Piazza S . Francesco 19, 55100 Lucca, Italy
                Author notes
                [*]

                These authors contributed equally to this work.

                Article
                srep20214
                10.1038/srep20214
                4745012
                26853830
                c89b128f-8ba3-441f-b73f-bd67ccb93116
                Copyright © 2016, Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 21 August 2015
                : 29 December 2015
                Categories
                Article

                Uncategorized
                Uncategorized

                Comments

                Comment on this article