Virtual Patients and Sensitivity Analysis of the Guyton Model of Blood Pressure Regulation: Towards Individualized Models of Whole-Body Physiology
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{"title"=>"Virtual patients and sensitivity analysis of the Guyton model of blood pressure regulation: Towards individualized models of whole-body physiology", "type"=>"journal", "authors"=>[{"first_name"=>"Robert", "last_name"=>"Moss", "scopus_author_id"=>"28167595000"}, {"first_name"=>"Thibault", "last_name"=>"Grosse", "scopus_author_id"=>"53877517500"}, {"first_name"=>"Ivanny", "last_name"=>"Marchant", "scopus_author_id"=>"19639164500"}, {"first_name"=>"Nathalie", "last_name"=>"Lassau", "scopus_author_id"=>"7004046662"}, {"first_name"=>"François", "last_name"=>"Gueyffier", "scopus_author_id"=>"26642876400"}, {"first_name"=>"S. Randall", "last_name"=>"Thomas", "scopus_author_id"=>"7404655327"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"365284160", "doi"=>"10.1371/journal.pcbi.1002571", "pmid"=>"22761561", "issn"=>"1553734X", "sgr"=>"84864050091", "scopus"=>"2-s2.0-84864050091", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)"}, "id"=>"facf84cb-4117-3c21-b712-012bd0ca74ef", "abstract"=>"Mathematical models that integrate multi-scale physiological data can offer insight into physiological and pathophysiological function, and may eventually assist in individualized predictive medicine. We present a methodology for performing systematic analyses of multi-parameter interactions in such complex, multi-scale models. Human physiology models are often based on or inspired by Arthur Guyton's whole-body circulatory regulation model. Despite the significance of this model, it has not been the subject of a systematic and comprehensive sensitivity study. Therefore, we use this model as a case study for our methodology. Our analysis of the Guyton model reveals how the multitude of model parameters combine to affect the model dynamics, and how interesting combinations of parameters may be identified. It also includes a \"virtual population\" from which \"virtual individuals\" can be chosen, on the basis of exhibiting conditions similar to those of a real-world patient. This lays the groundwork for using the Guyton model for in silico exploration of pathophysiological states and treatment strategies. The results presented here illustrate several potential uses for the entire dataset of sensitivity results and the \"virtual individuals\" that we have generated, which are included in the supplementary material. More generally, the presented methodology is applicable to modern, more complex multi-scale physiological models.", "link"=>"http://www.mendeley.com/research/virtual-patients-sensitivity-analysis-guyton-model-blood-pressure-regulation-towards-individualized", "reader_count"=>36, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>14, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>2, "Other"=>3, "Student > Master"=>2, "Student > Bachelor"=>2, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>14, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>2, "Other"=>3, "Student > Master"=>2, "Student > Bachelor"=>2, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Engineering"=>6, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>3, "Agricultural and Biological Sciences"=>6, "Medicine and Dentistry"=>7, "Chemistry"=>1, "Social Sciences"=>1, "Computer Science"=>7, "Decision Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>6}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Chemistry"=>{"Chemistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>6}, "Computer Science"=>{"Computer Science"=>7}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"United States"=>1, "United Kingdom"=>1, "Chile"=>1, "France"=>1}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/617300"], "description"=>"<p>The effects on mean arterial pressure (MAP), cardiac output (QAO) and rate of urine production (VUD) are plotted as . Elementary effects were sorted by the magnitude of their largest effect on the three output variables. <b>A:</b> The elementary effects at (excluding HYL). <b>B:</b> The elementary effects at .</p>", "links"=>[], "tags"=>["largest", "variables"], "article_id"=>287789, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.g003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_largest_elementary_effects_on_three_key_output_variables_at_and_/287789", "title"=>"The largest elementary effects on three key output variables at and .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-28 02:09:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/616971"], "description"=>"<p>The vertical line marks the threshold of hypertension (), and both gamma and chi-squared distributions have been fitted.</p>", "links"=>[], "tags"=>["arterial"], "article_id"=>287463, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.g001", "stats"=>{"downloads"=>1, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probability_density_of_mean_arterial_pressure_MAP_in_the_virtual_population_/287463", "title"=>"Probability density of mean arterial pressure (MAP) in the virtual population.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-28 02:04:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/617679"], "description"=>"<p>Probability densities are shown for AAR (the afferent arteriolar resistance), POR (the reference value of capillary pressure in non-muscle tissue) and CPR (the critical plasma protein concentration for protein destruction).</p>", "links"=>[], "tags"=>["densities", "variables", "normotensive", "hypertensive"], "article_id"=>288163, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.g006", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probability_densities_of_model_variables_in_the_normotensive_and_hypertensive_virtual_sub_populations_/288163", "title"=>"Probability densities of model variables in the normotensive and hypertensive virtual sub-populations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-28 02:16:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/618436"], "description"=>"<p>The parameters used to predict hypertension in the reduced-parameter GLMs (“Renal”, “Liver” and “Renal+Liver”).</p>", "links"=>[], "tags"=>["parameters", "hypertension", "reduced-parameter", "glms"], "article_id"=>288922, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.t006", "stats"=>{"downloads"=>6, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_parameters_used_to_predict_hypertension_in_the_reduced_parameter_GLMs_8220_Renal_8221_8220_Liver_8221_and_8220_Renal_Liver_8221_/288922", "title"=>"The parameters used to predict hypertension in the reduced-parameter GLMs (“Renal”, “Liver” and “Renal+Liver”).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-06-28 02:28:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/618511"], "description"=>"<p>The coefficients of each classifier (GLM) presented in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002571#pcbi-1002571-g008\" target=\"_blank\">Figure 8a</a>.</p>", "links"=>[], "tags"=>["coefficients", "classifier", "presented"], "article_id"=>288999, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.t005", "stats"=>{"downloads"=>2, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_coefficients_of_each_classifier_GLM_presented_in_Figure_8a_/288999", "title"=>"The coefficients of each classifier (GLM) presented in Figure 8a.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-06-28 02:29:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/618026"], "description"=>"<p>Each classifier (binomial GLM) was fitted to a random sample of the virtual population and then evaluated on the remaining . <b>A:</b> ROC curves for several classifiers; the optimal (30-parameter) classifier has an area under curve (AUC) of , demonstrating high predictive power. The 6-parameter “Renal+Liver” classifier performs nearly as well (AUC = 0.948). <b>B:</b> The parameter sensitivity of the optimal classifier. The y-axis measures the variation in the prediction over the range of values for each parameter.</p>", "links"=>[], "tags"=>["linear", "classifiers", "hypertensive"], "article_id"=>288515, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.g008", "stats"=>{"downloads"=>0, "page_views"=>28, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evaluation_of_linear_classifiers_for_identifying_hypertensive_virtual_individuals_/288515", "title"=>"Evaluation of linear classifiers for identifying hypertensive virtual individuals.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-28 02:21:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/617126"], "description"=>"<p><b>A:</b> The effects on mean arterial pressure (MAP), cardiac output (QAO) and rate of urine production (VUD) are plotted (at after the perturbations) as . <b>B:</b> The most significant elementary effects when HYL is ignored. The complete tables of elementary effects are included in the supplementary material.</p>", "links"=>[], "tags"=>["variables"], "article_id"=>287618, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.g002", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_most_significant_elementary_effects_on_three_key_output_variables_at_each_time_/287618", "title"=>"The most significant elementary effects on three key output variables at each time .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-28 02:06:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/617850"], "description"=>"<p>For a given variable, the correlations with each parameter are plotted against the x-axis for the normotensive population, and against the y-axis for the hypertensive population (only correlations are shown). <b>A:</b> Mean arterial pressure (MAP). <b>B:</b> Cardiac output (QAO). <b>C:</b> Blood volume (VB). <b>D:</b> Rate of urine production (VUD).</p>", "links"=>[], "tags"=>["correlations", "parameters", "variables", "normotensive", "hypertensive"], "article_id"=>288336, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.g007", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_correlations_between_parameters_and_variables_in_the_normotensive_and_hypertensive_virtual_sub_populations_/288336", "title"=>"Comparison of correlations between parameters and variables in the normotensive and hypertensive virtual sub-populations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-28 02:18:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/618310"], "description"=>"<p>This simulation reproduced the conditions shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002571#pcbi-1002571-g005\" target=\"_blank\">Figure 5</a> of Uttamsingh et al. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002571#pcbi.1002571-Uttamsingh1\" target=\"_blank\">[74]</a>, which inclues experimental data from Davis and Howell <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002571#pcbi.1002571-Davis1\" target=\"_blank\">[94]</a> and Relman and Schwartz <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002571#pcbi.1002571-Relman1\" target=\"_blank\">[76]</a>. <b>A:</b> Extra-cellular fluid volume. <b>B:</b> Mean arterial pressure. <b>C:</b> Serum aldosterone (normalized). <b>D:</b> Sodium excretion rate.</p>", "links"=>[], "tags"=>["aldosterone", "loading"], "article_id"=>288797, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.g010", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effects_of_aldosterone_loading_on_the_human_body_/288797", "title"=>"The effects of aldosterone loading on the human body.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-28 02:26:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/618553"], "description"=>"<p>The conditions on various model parameters that were used to ensure that the <i>virtual person</i> remained “alive” during a simulation, based on definitions from the Common Terminology Criteria for Adverse Events v4.03 (CTCAE) <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002571#pcbi.1002571-United1\" target=\"_blank\">[41]</a>.</p>", "links"=>[], "tags"=>["conditions", "parameters", "remained", "definitions", "terminology", "adverse", "events"], "article_id"=>289038, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.t002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_conditions_on_various_model_parameters_that_were_used_to_ensure_that_the_virtual_person_remained_alive_during_a_simulation_based_on_definitions_from_the_Common_Terminology_Criteria_for_Adverse_Events_v4_03_CTCAE_41_/289038", "title"=>"The conditions on various model parameters that were used to ensure that the <i>virtual person</i> remained “alive” during a simulation, based on definitions from the Common Terminology Criteria for Adverse Events v4.03 (CTCAE) [41].", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-06-28 02:30:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/617558"], "description"=>"<p>Correlations are shown for mean arterial pressure (MAP), cardiac output (QAO) and rate of urine production (VUD), where and .</p>", "links"=>[], "tags"=>["correlations", "parameters"], "article_id"=>288043, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.g005", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Significant_correlations_between_model_parameters_and_three_key_output_variables_/288043", "title"=>"Significant correlations between model parameters and three key output variables.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-28 02:14:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/618473"], "description"=>"<p>Categorization of the virtual individuals into normotensive and hypertensive populations, based on mean arterial pressure (MAP).</p>", "links"=>[], "tags"=>["individuals", "normotensive", "hypertensive", "arterial"], "article_id"=>288956, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.t004", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Categorization_of_the_virtual_individuals_into_normotensive_and_hypertensive_populations_based_on_mean_arterial_pressure_MAP_/288956", "title"=>"Categorization of the virtual individuals into normotensive and hypertensive populations, based on mean arterial pressure (MAP).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-06-28 02:29:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/618141"], "description"=>"<p>These simulations reproduced the conditions shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002571#pcbi-1002571-g003\" target=\"_blank\">Figures 3</a> and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002571#pcbi-1002571-g004\" target=\"_blank\">4</a> of Uttamsingh et al. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002571#pcbi.1002571-Uttamsingh1\" target=\"_blank\">[74]</a>, which include experimental from Baldes and Smirk <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002571#pcbi.1002571-Baldes1\" target=\"_blank\">[93]</a> and Dean and McCance <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002571#pcbi.1002571-Dean1\" target=\"_blank\">[75]</a>. <b>A:</b> Urine flow following ingestion of 1 L of water. <b>B:</b> Urine flow following ingestion of hypertonic saline (normalized wrt. the urine flow rate prior to ingestion).</p>", "links"=>[], "tags"=>["ingestion", "hypotonic", "hypertonic", "solutions", "urine"], "article_id"=>288633, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.g009", "stats"=>{"downloads"=>2, "page_views"=>182, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effects_of_ingestion_of_hypotonic_and_hypertonic_solutions_on_urine_flow_/288633", "title"=>"The effects of ingestion of hypotonic and hypertonic solutions on urine flow.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-28 02:23:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/618585"], "description"=>"<p>Parameters for building the virtual population.</p>", "links"=>[], "tags"=>["physiology", "Computational biology"], "article_id"=>289079, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.t003", "stats"=>{"downloads"=>6, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameters_for_building_the_virtual_population_/289079", "title"=>"Parameters for building the virtual population.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-06-28 02:31:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/617412"], "description"=>"<p>Correlations () are shown for the elementary effect of HYL on mean arterial pressure (MAP), cardiac output (QAO) and rate of urine production (VUD).</p>", "links"=>[], "tags"=>["correlations", "parameters", "hyl"], "article_id"=>287896, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.g004", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Significant_correlations_between_model_parameters_and_the_elementary_effects_of_HYL_at_/287896", "title"=>"Significant correlations between model parameters and the elementary effects of HYL at .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-28 02:11:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/321139", "https://ndownloader.figshare.com/files/321156", "https://ndownloader.figshare.com/files/321203"], "description"=>"<div><p>Mathematical models that integrate multi-scale physiological data can offer insight into physiological and pathophysiological function, and may eventually assist in individualized predictive medicine. We present a methodology for performing systematic analyses of multi-parameter interactions in such complex, multi-scale models. Human physiology models are often based on or inspired by Arthur Guyton's whole-body circulatory regulation model. Despite the significance of this model, it has not been the subject of a systematic and comprehensive sensitivity study. Therefore, we use this model as a case study for our methodology. Our analysis of the Guyton model reveals how the multitude of model parameters combine to affect the model dynamics, and how interesting combinations of parameters may be identified. It also includes a “virtual population” from which “virtual individuals” can be chosen, on the basis of exhibiting conditions similar to those of a real-world patient. This lays the groundwork for using the Guyton model for <em>in silico</em> exploration of pathophysiological states and treatment strategies. The results presented here illustrate several potential uses for the entire dataset of sensitivity results and the “virtual individuals” that we have generated, which are included in the supplementary material. More generally, the presented methodology is applicable to modern, more complex multi-scale physiological models.</p> </div>", "links"=>[], "tags"=>["patients", "guyton", "individualized", "models", "whole-body", "physiology"], "article_id"=>123379, "categories"=>["Biological Sciences", "Physiology"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>[nil, nil, nil], "stats"=>{"downloads"=>7, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Virtual_Patients_and_Sensitivity_Analysis_of_the_Guyton_Model_of_Blood_Pressure_Regulation_Towards_Individualized_Models_of_Whole_Body_Physiology/123379", "title"=>"Virtual Patients and Sensitivity Analysis of the Guyton Model of Blood Pressure Regulation: Towards Individualized Models of Whole-Body Physiology", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-06-28 00:56:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/618618"], "description"=>"<p>Equations for calculating elementary effects.</p>", "links"=>[], "tags"=>["physiology", "Computational biology"], "article_id"=>289106, "categories"=>["Physiology", "Biological Sciences"], "users"=>["Robert Moss", "Thibault Grosse", "Ivanny Marchant", "Nathalie Lassau", "François Gueyffier", "S. Randall Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002571.t001", "stats"=>{"downloads"=>4, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Equations_for_calculating_elementary_effects_/289106", "title"=>"Equations for calculating elementary effects.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-06-28 02:31:46"}

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Relative Metric

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