The Role of Type 4 Phosphodiesterases in Generating Microdomains of cAMP: Large Scale Stochastic Simulations
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{"title"=>"The role of type 4 phosphodiesterases in generating microdomains of cAMP: Large scale stochastic simulations", "type"=>"journal", "authors"=>[{"first_name"=>"Rodrigo F.", "last_name"=>"Oliveira", "scopus_author_id"=>"55939201300"}, {"first_name"=>"Anna", "last_name"=>"Terrin", "scopus_author_id"=>"9240839000"}, {"first_name"=>"Giulietta", "last_name"=>"di Benedetto", "scopus_author_id"=>"15055773500"}, {"first_name"=>"Robert C.", "last_name"=>"Cannon", "scopus_author_id"=>"7201491609"}, {"first_name"=>"Wonryull", "last_name"=>"Koh", "scopus_author_id"=>"55757408500"}, {"first_name"=>"Myungsook", "last_name"=>"Kim", "scopus_author_id"=>"56122919200"}, {"first_name"=>"Manuela", "last_name"=>"Zaccolo", "scopus_author_id"=>"6603747773"}, {"first_name"=>"Kim T.", "last_name"=>"Blackwell", "scopus_author_id"=>"7005536162"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"77955374442", "issn"=>"19326203", "scopus"=>"2-s2.0-77955374442", "pmid"=>"20661441", "pui"=>"359319291", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0011725"}, "id"=>"8a2c126b-3a57-35eb-8ace-8d7411b7206b", "abstract"=>"Cyclic AMP (cAMP) and its main effector Protein Kinase A (PKA) are critical for several aspects of neuronal function including synaptic plasticity. Specificity of synaptic plasticity requires that cAMP activates PKA in a highly localized manner despite the speed with which cAMP diffuses. Two mechanisms have been proposed to produce localized elevations in cAMP, known as microdomains: impeded diffusion, and high phosphodiesterase (PDE) activity. This paper investigates the mechanism of localized cAMP signaling using a computational model of the biochemical network in the HEK293 cell, which is a subset of pathways involved in PKA-dependent synaptic plasticity. This biochemical network includes cAMP production, PKA activation, and cAMP degradation by PDE activity. The model is implemented in NeuroRD: novel, computationally efficient, stochastic reaction-diffusion software, and is constrained by intracellular cAMP dynamics that were determined experimentally by real-time imaging using an Epac-based FRET sensor (H30). The model reproduces the high concentration cAMP microdomain in the submembrane region, distinct from the lower concentration of cAMP in the cytosol. Simulations further demonstrate that generation of the cAMP microdomain requires a pool of PDE4D anchored in the cytosol and also requires PKA-mediated phosphorylation of PDE4D which increases its activity. The microdomain does not require impeded diffusion of cAMP, confirming that barriers are not required for microdomains. The simulations reported here further demonstrate the utility of the new stochastic reaction-diffusion algorithm for exploring signaling pathways in spatially complex structures such as neurons.", "link"=>"http://www.mendeley.com/research/role-type-4-phosphodiesterases-generating-microdomains-camp-large-scale-stochastic-simulations", "reader_count"=>71, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>5, "Researcher"=>29, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>18, "Student > Master"=>4, "Other"=>3, "Student > Bachelor"=>4, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>5, "Researcher"=>29, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>18, "Student > Master"=>4, "Other"=>3, "Student > Bachelor"=>4, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Agricultural and Biological Sciences"=>38, "Chemistry"=>1, "Computer Science"=>4, "Engineering"=>8, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>2, "Medicine and Dentistry"=>4, "Neuroscience"=>4, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>1, "Psychology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>2}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Engineering"=>{"Engineering"=>8}, "Chemistry"=>{"Chemistry"=>1}, "Neuroscience"=>{"Neuroscience"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>38}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}}, "reader_count_by_country"=>{"United States"=>3, "Norway"=>1, "Japan"=>1, "United Kingdom"=>1, "Portugal"=>1, "Germany"=>1}, "group_count"=>5}

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  • {"files"=>["https://ndownloader.figshare.com/files/839402"], "description"=>"<p>(A) Steady state dose-response simulation (black) and experimental (gray) curves show excellent agreement. Ordinates are background-subtracted FRET emission ratio changes, ΔR, measured relative to the prestimulus ratio R(0). (B) Time course of simulated and experimental FRET signal shows excellent agreement. FRET ratio trace obtained by delivery of 30 µM cAMP to cell under whole-cell recording conditions. All experiments were performed in HeLa cells transfected with H30 (acquisition conditions: 1 frame/5 s). The microscope was equipped with a CCD camera (Sensicam QE; PCO), a software-controlled monochromator (Polychrome IV; TILL Photonics), and a beam-splitter optical device (Multispec Microimager; Optical Insights).</p>", "links"=>[], "tags"=>["biophysics/theory and simulation", "computational biology/computational neuroscience", "computational biology/signaling networks"], "article_id"=>509854, "categories"=>["Infectious Diseases", "Medicine", "Biophysics"], "users"=>["Rodrigo F. Oliveira", "Anna Terrin", "Giulietta Di Benedetto", "Robert C. Cannon", "Wonryull Koh", "MyungSook Kim", "Manuela Zaccolo", "Kim T. Blackwell"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011725.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_FRET_signal_as_a_function_of_cAMP_concentration_/509854", "title"=>"FRET signal as a function of cAMP concentration.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-22 02:44:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/839290"], "description"=>"<p>(A) G<sub>α</sub>GTP binds to and activates adenylate cyclase, which then produces cAMP from ATP. cAMP activates PKA, a heterotetramer with two regulatory and two catalytic subunits. After binding 4 molecules of cAMP, the two catalytic subunits (PKAc) dissociate from the regulatory subunit dimer (PKAr) and become active <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011725#pone.0011725-Zawadzki1\" target=\"_blank\">[26]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011725#pone.0011725-Ogreid1\" target=\"_blank\">[27]</a>. cAMP is degraded by phosphodiesterase, type 4B (PDE4B) and type 4D (PDE4D). AC, G<sub>α</sub>GTP, PKA and PDE4B are anchored at the submembrane while PKA and PDE4D are distributed throughout the cytosol. cAMP, ATP, AMP and PKAc freely diffuse. (B) Confocal image showing the localization of the membrane-targeted version of the unimolecular Epac-based sensor for cAMP (mpH30) in HEK293 cells. Confocal images were acquired 24 hours after transfection by using the broadband confocal Leica TCS SP5 system (Leica Microsystems) and a HCX PL APO 63x1.4NA oil-immersion objective (scale bar 10 µm). The representation superimposed on the micrograph corresponds to the grid in C. (C) Schematic representation of the spatial structure of the HEK293 cell model, light gray compartments correspond to the cytosol while dark gray compartments correspond to the submembrane region in a slice of the 3-dimensional cell.</p>", "links"=>[], "tags"=>["biochemical", "signaling", "pathway"], "article_id"=>509738, "categories"=>["Infectious Diseases", "Medicine", "Biophysics"], "users"=>["Rodrigo F. Oliveira", "Anna Terrin", "Giulietta Di Benedetto", "Robert C. Cannon", "Wonryull Koh", "MyungSook Kim", "Manuela Zaccolo", "Kim T. Blackwell"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011725.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_representation_of_the_biochemical_signaling_pathway_modeled_/509738", "title"=>"Schematic representation of the biochemical signaling pathway modeled.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-22 02:42:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/839716"], "description"=>"<p>(A) The increase in the quantity of PKA with 4 cAMP molecules bound is greater in the submembrane region than in the cytosol. However, the percent increase is the same submembrane and cytosol. (B) PKA catalytic subunit (PKAc) concentration is the same in submembrane and cytosolic compartments. (C) The higher cAMP concentration observed submembrane translates into a larger fraction of phosphorylated PDE4s in the submembrane (pPDE4B) as compared to the cytosol (pPDE4D). A single representative trace is illustrated in each panel.</p>", "links"=>[], "tags"=>["microdomains", "downstream"], "article_id"=>510170, "categories"=>["Infectious Diseases", "Medicine", "Biophysics"], "users"=>["Rodrigo F. Oliveira", "Anna Terrin", "Giulietta Di Benedetto", "Robert C. Cannon", "Wonryull Koh", "MyungSook Kim", "Manuela Zaccolo", "Kim T. Blackwell"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011725.g006", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Propagation_of_cAMP_microdomains_to_downstream_targets_/510170", "title"=>"Propagation of cAMP microdomains to downstream targets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-22 00:02:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/839987"], "description"=>"<p>Time and memory allocation were measured for several sets of simulations (see section Validation in the text for details). All simulations were run for 3000 msecs and the total volume of the system was 110 µm<sup>3</sup>. The simulation <i>Diffusion</i> includes one molecular species and no reactions while all the remaining simulations have 4 molecular species and 2 reversible bimolecular reactions. The simulation labeled <i>Reaction</i> starts out of biochemical equilibrium albeit the distribution of molecules in space is homogeneous. <i>Reaction & Diffusion</i> (I and II), start in equilibrium but molecules are injected after 100 msecs disturbing both the homogeneous distributions of molecules and their biochemical equilibrium. Concentrations in simulation <i>Reaction & Diffusion II</i> are well within the physiological range (highest molecular species (A) concentration: ∼400 nM).</p>", "links"=>[], "tags"=>["scalability", "neurord"], "article_id"=>510440, "categories"=>["Infectious Diseases", "Medicine", "Biophysics"], "users"=>["Rodrigo F. Oliveira", "Anna Terrin", "Giulietta Di Benedetto", "Robert C. Cannon", "Wonryull Koh", "MyungSook Kim", "Manuela Zaccolo", "Kim T. Blackwell"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011725.t003", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_scalability_between_NeuroRD_and_Smoldyn_/510440", "title"=>"Comparison of scalability between NeuroRD and Smoldyn.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-07-22 00:07:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/839488"], "description"=>"<p>Simulations show good agreement between NeuroRD, Smoldyn <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011725#pone.0011725-Andrews1\" target=\"_blank\">[25]</a> and deterministic solutions (XPPAUT <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011725#pone.0011725-Ermentrout1\" target=\"_blank\">[38]</a> or Chemesis <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011725#pone.0011725-Blackwell2\" target=\"_blank\">[39]</a>). (A) Validation of diffusion alone. Deterministic trace generated using Chemesis; (B) Validation of reactions alone. The deterministic trace is generated using XPPAUT; (C and D) Validation of reaction-diffusion. The deterministic trace is generated using Chemesis. In all panels <i>Distance</i> refers to the Euclidean distance in microns between center of source subvolume and center of other subvolumes. Panel C shows molecule “A” which has a relatively high concentration and fast dynamics, whereas Panel D shows molecule “C”, which has a low concentration and slower dynamics.</p>", "links"=>[], "tags"=>["biophysics/theory and simulation", "computational biology/computational neuroscience", "computational biology/signaling networks"], "article_id"=>509941, "categories"=>["Infectious Diseases", "Medicine", "Biophysics"], "users"=>["Rodrigo F. Oliveira", "Anna Terrin", "Giulietta Di Benedetto", "Robert C. Cannon", "Wonryull Koh", "MyungSook Kim", "Manuela Zaccolo", "Kim T. Blackwell"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011725.g003", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Validation_of_NeuroRD_/509941", "title"=>"Validation of <i>NeuroRD</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-22 02:45:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/839948"], "description"=>"<p>Concentrations (in nM) for submembrane (S) and cytosolic (C) compartments and diffusion constants (K<sub>diff</sub> in µm<sup>2</sup>/s) are calculated based on molecular weight of molecular species in the HEK293 cell model (0 denotes non-diffusible molecule and NA denotes NOT APLICABLE). Initial concentrations reported in this table are extracted from output of simulations without expression of biosensor. References in K<sub>diff</sub> column allow comparison between experimentally measured and calculated (eq. 1) diffusion constants.</p>", "links"=>[], "tags"=>["concentrations", "molecules"], "article_id"=>510397, "categories"=>["Infectious Diseases", "Medicine", "Biophysics"], "users"=>["Rodrigo F. Oliveira", "Anna Terrin", "Giulietta Di Benedetto", "Robert C. Cannon", "Wonryull Koh", "MyungSook Kim", "Manuela Zaccolo", "Kim T. Blackwell"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011725.t002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Initial_concentrations_for_molecules_in_the_model_/510397", "title"=>"Initial concentrations for molecules in the model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-07-22 00:06:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/839851"], "description"=>"<p>Impeded diffusion of cAMP is not required for the microdomain, but influences the concentration difference between submembrane and cytosol. The faster the cAMP diffusion coefficient, the smaller the difference between submembrane and cytosol concentration (measured as difference between FRET ΔF/F submembrane and FRET ΔF/F cystosol (solid black line, black squares). cAMP diffusion coefficient ranges from k = 0.5 to k = 3 times its control value of 295 µm<sup>2</sup>/s). Diffusion of the PKA catalytic subunit plays no significant role in generating cAMP microdomains (solid gray line, open squares). PKAc diffusion constant ranges from k = 0 to k = 2 times its control value of 59.54 µm<sup>2</sup>/s.</p>", "links"=>[], "tags"=>["microdomain", "influenced", "diffusion", "pkac"], "article_id"=>510300, "categories"=>["Infectious Diseases", "Medicine", "Biophysics"], "users"=>["Rodrigo F. Oliveira", "Anna Terrin", "Giulietta Di Benedetto", "Robert C. Cannon", "Wonryull Koh", "MyungSook Kim", "Manuela Zaccolo", "Kim T. Blackwell"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011725.g007", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Amplitude_of_the_microdomain_is_influenced_by_cAMP_diffusion_coefficient_but_not_by_PKAc_diffusion_coefficient_/510300", "title"=>"Amplitude of the microdomain is influenced by cAMP diffusion coefficient, but not by PKAc diffusion coefficient.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-22 00:05:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/839573"], "description"=>"<p>(A) The FRET signal for the submembrane region is 6.8% higher than the cytosol. Mean (black traces) and ±SD (gray traces, n = 5). (B) Difference between submembrane and cytosolic cAMP concentration is similar to that observed for the FRET signal, and is independent of overexpression of the H30 sensor. The model cell with H30 is shown in black; the model cell without H30 is shown in gray. SD traces are not illustrated because they overlap with the mean. No diffusional barriers are present for these simulations. The expression of the sensor does not disturb the cAMP microdomain, therefore the difference between submembrane FRET and cytosolic FRET is not an artifact of the method. (C) Representative kinetics of FRET changes recorded in cells expressing either the membrane targeted sensor mpH30 or the cytosolic sensor H30 <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011725#pone.0011725-Terrin1\" target=\"_blank\">[16]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011725#pone.0011725-Ponsioen1\" target=\"_blank\">[56]</a> upon stimulation with 1µM PGE<sub>1</sub>. FRET experiments were performed as described previously in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011725#pone.0011725-Terrin1\" target=\"_blank\">[16]</a>.</p>", "links"=>[], "tags"=>["fret", "microdomains", "diffusional"], "article_id"=>510026, "categories"=>["Infectious Diseases", "Medicine", "Biophysics"], "users"=>["Rodrigo F. Oliveira", "Anna Terrin", "Giulietta Di Benedetto", "Robert C. Cannon", "Wonryull Koh", "MyungSook Kim", "Manuela Zaccolo", "Kim T. Blackwell"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011725.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_theoretical_FRET_signal_and_cAMP_concentration_show_microdomains_without_diffusional_barriers_/510026", "title"=>"The theoretical FRET signal and cAMP concentration show microdomains without diffusional barriers.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-22 00:00:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/839906"], "description"=>"<p>The simulation of cAMP microdomains produced by PKA activation of PDE4s (without H30, as plotted in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011725#pone.0011725.s005\" target=\"_blank\">Figure S5</a>) was run with different mesh sizes and timesteps. The simulation time and amount of memory allocated shows that <i>NeuroRD</i> scales approximately linearly with number of subvolumes, and number of timesteps.</p>", "links"=>[], "tags"=>["neurord", "mesh"], "article_id"=>510359, "categories"=>["Infectious Diseases", "Medicine", "Biophysics"], "users"=>["Rodrigo F. Oliveira", "Anna Terrin", "Giulietta Di Benedetto", "Robert C. Cannon", "Wonryull Koh", "MyungSook Kim", "Manuela Zaccolo", "Kim T. Blackwell"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011725.t004", "stats"=>{"downloads"=>7, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scalability_of_NeuroRD_as_a_function_of_mesh_size_space_discretization_and_time_step_/510359", "title"=>"Scalability of NeuroRD as a function of mesh size (space discretization) and time step.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-07-22 00:05:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/418074", "https://ndownloader.figshare.com/files/418104", "https://ndownloader.figshare.com/files/418133", "https://ndownloader.figshare.com/files/418164", "https://ndownloader.figshare.com/files/418192", "https://ndownloader.figshare.com/files/418225"], "description"=>"<div><p>Cyclic AMP (cAMP) and its main effector Protein Kinase A (PKA) are critical for several aspects of neuronal function including synaptic plasticity. Specificity of synaptic plasticity requires that cAMP activates PKA in a highly localized manner despite the speed with which cAMP diffuses. Two mechanisms have been proposed to produce localized elevations in cAMP, known as microdomains: impeded diffusion, and high phosphodiesterase (PDE) activity. This paper investigates the mechanism of localized cAMP signaling using a computational model of the biochemical network in the HEK293 cell, which is a subset of pathways involved in PKA-dependent synaptic plasticity. This biochemical network includes cAMP production, PKA activation, and cAMP degradation by PDE activity. The model is implemented in NeuroRD: novel, computationally efficient, stochastic reaction-diffusion software, and is constrained by intracellular cAMP dynamics that were determined experimentally by real-time imaging using an Epac-based FRET sensor (H30). The model reproduces the high concentration cAMP microdomain in the submembrane region, distinct from the lower concentration of cAMP in the cytosol. Simulations further demonstrate that generation of the cAMP microdomain requires a pool of PDE4D anchored in the cytosol and also requires PKA-mediated phosphorylation of PDE4D which increases its activity. The microdomain does not require impeded diffusion of cAMP, confirming that barriers are not required for microdomains. The simulations reported here further demonstrate the utility of the new stochastic reaction-diffusion algorithm for exploring signaling pathways in spatially complex structures such as neurons.</p></div>", "links"=>[], "tags"=>["phosphodiesterases", "generating", "microdomains", "stochastic", "simulations"], "article_id"=>142552, "categories"=>["Cancer", "Medicine", "Biophysics"], "users"=>["Rodrigo F. Oliveira", "Anna Terrin", "Giulietta Di Benedetto", "Robert C. Cannon", "Wonryull Koh", "MyungSook Kim", "Manuela Zaccolo", "Kim T. Blackwell"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0011725.s001", "https://dx.doi.org/10.1371/journal.pone.0011725.s002", "https://dx.doi.org/10.1371/journal.pone.0011725.s003", "https://dx.doi.org/10.1371/journal.pone.0011725.s004", "https://dx.doi.org/10.1371/journal.pone.0011725.s005", "https://dx.doi.org/10.1371/journal.pone.0011725.s006"], "stats"=>{"downloads"=>6, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_Role_of_Type_4_Phosphodiesterases_in_Generating_Microdomains_of_cAMP_Large_Scale_Stochastic_Simulations/142552", "title"=>"The Role of Type 4 Phosphodiesterases in Generating Microdomains of cAMP: Large Scale Stochastic Simulations", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-07-22 00:42:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/839647"], "description"=>"<p>(A) Silencing of PDE4B does not eliminate the submembrane microdomain. (B) Silencing of PDE4D does eliminate the submembrane microdomain. (C) Blocking PDE4 phosphorylation by PKAc eliminates the submembrane microdomain, and also eliminates the decay of the FRET signal from the peak. There is substantial overlap of cytosol and submembrane standard deviation traces. These results suggest that PKA is the main effector of the microdomain through phosphorylation of PDE4s (inset shows representative experimental data). Mean and ±SD traces in red and orange for submembrane and black and gray for cytosol, respectively (n = 5).</p>", "links"=>[], "tags"=>["biophysics/theory and simulation", "computational biology/computational neuroscience", "computational biology/signaling networks"], "article_id"=>510099, "categories"=>["Infectious Diseases", "Medicine", "Biophysics"], "users"=>["Rodrigo F. Oliveira", "Anna Terrin", "Giulietta Di Benedetto", "Robert C. Cannon", "Wonryull Koh", "MyungSook Kim", "Manuela Zaccolo", "Kim T. Blackwell"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011725.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mechanisms_underlying_cAMP_microdomains_/510099", "title"=>"Mechanisms underlying cAMP microdomains.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-22 00:01:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/840019"], "description"=>"<p>Reactions and rate constants of HEK293 cell biochemical network.</p>", "links"=>[], "tags"=>["constants", "hek293", "biochemical"], "article_id"=>510474, "categories"=>["Infectious Diseases", "Medicine", "Biophysics"], "users"=>["Rodrigo F. Oliveira", "Anna Terrin", "Giulietta Di Benedetto", "Robert C. Cannon", "Wonryull Koh", "MyungSook Kim", "Manuela Zaccolo", "Kim T. Blackwell"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011725.t001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reactions_and_rate_constants_of_HEK293_cell_biochemical_network_/510474", "title"=>"Reactions and rate constants of HEK293 cell biochemical network.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-07-22 00:07:54"}

PMC Usage Stats | Further Information

  • {"scanned-page-browse"=>"0", "month"=>"7", "cited-by"=>"0", "abstract"=>"0", "full-text"=>"1", "unique-ip"=>"2", "pdf"=>"1", "year"=>"2010", "figure"=>"0", "scanned-summary"=>"0", "supp-data"=>"0"}
  • {"month"=>"8", "scanned-page-browse"=>"0", "cited-by"=>"0", "abstract"=>"1", "full-text"=>"45", "year"=>"2010", "pdf"=>"30", "unique-ip"=>"44", "figure"=>"4", "scanned-summary"=>"0", "supp-data"=>"5"}
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Relative Metric

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