705
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction

      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

          Large-scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach—implemented in the free CNO software—for turning signalling networks into logical models and calibrating the models against experimental data. When a literature-derived network of 82 proteins covering the immediate-early responses of human cells to seven cytokines was modelled, we found that training against experimental data dramatically increased predictive power, despite the crudeness of Boolean approximations, while significantly reducing the number of interactions. Thus, many interactions in literature-derived networks do not appear to be functional in the liver cells from which we collected our data. At the same time, CNO identified several new interactions that improved the match of model to data. Although missing from the starting network, these interactions have literature support. Our approach, therefore, represents a means to generate predictive, cell-type-specific models of mammalian signalling from generic protein signalling networks.

          Related collections

          Most cited references70

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Network-based classification of breast cancer metastasis

          Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Modeling and simulation of genetic regulatory systems: a literature review.

            In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Causal protein-signaling networks derived from multiparameter single-cell data.

              Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.
                Bookmark

                Author and article information

                Journal
                Mol Syst Biol
                Molecular Systems Biology
                Nature Publishing Group
                1744-4292
                2009
                01 December 2009
                : 5
                : 331
                Affiliations
                [1 ]Center for Cell Decision Processes, Boston, MA, USA
                [2 ]Department of Systems Biology, Harvard Medical School, Boston, MA, USA
                [3 ]Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
                [4 ]Department of Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
                Author notes
                [a ]Department of Systems Biology, Harvard Medical School, Warren Alpert 438, 200 Longwood Avenue, Boston, MA 02115, USA. Tel.: +1 617 432 6901/Ext. 6902; Fax: +1 617 432 5012; sbpipeline@ 123456hms.harvard.edu
                [*]

                These authors contributed equally to this work

                [†]

                Present address: Department of Mechanical Engineering, National Technical University of Athens, Zografou 15780, Greece

                Article
                msb200987
                10.1038/msb.2009.87
                2824489
                19953085
                b0e54657-877f-4d79-8a2c-42baa913ae06
                Copyright © 2009, EMBO and Nature Publishing Group

                This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits distribution and reproduction in any medium, provided the original author and source are credited. Creation of derivative works is permitted but the resulting work may be distributed only under the same or similar licence to this one. This licence does not permit commercial exploitation without specific permission.

                Page count
                Pages: 1
                Categories
                Article

                Quantitative & Systems biology

                logical modelling, protein networks, signal transduction

                Comments

                Comment on this article