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      Coupled Stochastic Spatial and Non-Spatial Simulations of ErbB1 Signaling Pathways Demonstrate the Importance of Spatial Organization in Signal Transduction

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          Abstract

          Background

          The ErbB family of receptors activates intracellular signaling pathways that control cellular proliferation, growth, differentiation and apoptosis. Given these central roles, it is not surprising that overexpression of the ErbB receptors is often associated with carcinogenesis. Therefore, extensive laboratory studies have been devoted to understanding the signaling events associated with ErbB activation.

          Methodology/Principal Findings

          Systems biology has contributed significantly to our current understanding of ErbB signaling networks. However, although computational models have grown in complexity over the years, little work has been done to consider the spatial-temporal dynamics of receptor interactions and to evaluate how spatial organization of membrane receptors influences signaling transduction. Herein, we explore the impact of spatial organization of the epidermal growth factor receptor (ErbB1/EGFR) on the initiation of downstream signaling. We describe the development of an algorithm that couples a spatial stochastic model of membrane receptors with a nonspatial stochastic model of the reactions and interactions in the cytosol. This novel algorithm provides a computationally efficient method to evaluate the effects of spatial heterogeneity on the coupling of receptors to cytosolic signaling partners.

          Conclusions/Significance

          Mathematical models of signal transduction rarely consider the contributions of spatial organization due to high computational costs. A hybrid stochastic approach simplifies analyses of the spatio-temporal aspects of cell signaling and, as an example, demonstrates that receptor clustering contributes significantly to the efficiency of signal propagation from ligand-engaged growth factor receptors.

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          Most cited references48

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          Paradigm shift of the plasma membrane concept from the two-dimensional continuum fluid to the partitioned fluid: high-speed single-molecule tracking of membrane molecules.

          Recent advancements in single-molecule tracking methods with nanometer-level precision now allow researchers to observe the movement, recruitment, and activation of single molecules in the plasma membrane in living cells. In particular, on the basis of the observations by high-speed single-particle tracking at a frame rate of 40,000 frames s(1), the partitioning of the fluid plasma membrane into submicron compartments throughout the cell membrane and the hop diffusion of virtually all the molecules have been proposed. This could explain why the diffusion coefficients in the plasma membrane are considerably smaller than those in artificial membranes, and why the diffusion coefficient is reduced upon molecular complex formation (oligomerization-induced trapping). In this review, we first describe the high-speed single-molecule tracking methods, and then we critically review a new model of a partitioned fluid plasma membrane and the involvement of the actin-based membrane-skeleton "fences" and anchored-transmembrane protein "pickets" in the formation of compartment boundaries.
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            Phospholipids undergo hop diffusion in compartmentalized cell membrane

            The diffusion rate of lipids in the cell membrane is reduced by a factor of 5–100 from that in artificial bilayers. This slowing mechanism has puzzled cell biologists for the last 25 yr. Here we address this issue by studying the movement of unsaturated phospholipids in rat kidney fibroblasts at the single molecule level at the temporal resolution of 25 μs. The cell membrane was found to be compartmentalized: phospholipids are confined within 230-nm-diameter (φ) compartments for 11 ms on average before hopping to adjacent compartments. These 230-nm compartments exist within greater 750-nm-φ compartments where these phospholipids are confined for 0.33 s on average. The diffusion rate within 230-nm compartments is 5.4 μm2/s, which is nearly as fast as that in large unilamellar vesicles, indicating that the diffusion in the cell membrane is reduced not because diffusion per se is slow, but because the cell membrane is compartmentalized with regard to lateral diffusion of phospholipids. Such compartmentalization depends on the actin-based membrane skeleton, but not on the extracellular matrix, extracellular domains of membrane proteins, or cholesterol-enriched rafts. We propose that various transmembrane proteins anchored to the actin-based membrane skeleton meshwork act as rows of pickets that temporarily confine phospholipids.
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              Growth factor-induced MAPK network topology shapes Erk response determining PC-12 cell fate.

              The mitogen-activated protein kinase (MAPK) network is a conserved signalling module that regulates cell fate by transducing a myriad of growth-factor signals. The ability of this network to coordinate and process a variety of inputs from different growth-factor receptors into specific biological responses is, however, still not understood. We investigated how the MAPK network brings about signal specificity in PC-12 cells, a model for neuronal differentiation. Reverse engineering by modular-response analysis uncovered topological differences in the MAPK core network dependent on whether cells were activated with epidermal or neuronal growth factor (EGF or NGF). On EGF stimulation, the network exhibited negative feedback only, whereas a positive feedback was apparent on NGF stimulation. The latter allows for bi-stable Erk activation dynamics, which were indeed observed. By rewiring these regulatory feedbacks, we were able to reverse the specific cell responses to EGF and NGF. These results show that growth factor context determines the topology of the MAPK signalling network and that the resulting dynamics govern cell fate.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2009
                23 July 2009
                : 4
                : 7
                : e6316
                Affiliations
                [1 ]Department of Chemical and Nuclear Engineering, University of New Mexico, Albuquerque, New Mexico, United States of America
                [2 ]Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, United States of America
                [3 ]Cancer Research and Treatment Center, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, United States of America
                [4 ]Department of Chemical Engineering, University of Delaware, Newark, Delaware, United States of America
                [5 ]Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, United States of America
                Lund University, Sweden
                Author notes

                Conceived and designed the experiments: DGV JE. Performed the experiments: MC. Analyzed the data: MC KR DGV JE. Contributed reagents/materials/analysis tools: DGV BW. Wrote the paper: MC DGV JE.

                Article
                09-PONE-RA-08618R1
                10.1371/journal.pone.0006316
                2710010
                19626123
                53169581-e0ca-447f-a706-6ae6432c34a6
                Costa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 10 February 2009
                : 17 June 2009
                Page count
                Pages: 9
                Categories
                Research Article
                Computational Biology
                Cell Biology/Cell Signaling
                Computational Biology/Systems Biology

                Uncategorized
                Uncategorized

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