Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems
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{"title"=>"Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems", "type"=>"journal", "authors"=>[{"first_name"=>"Kieran", "last_name"=>"Alden", "scopus_author_id"=>"36162419700"}, {"first_name"=>"Mark", "last_name"=>"Read", "scopus_author_id"=>"7202794836"}, {"first_name"=>"Jon", "last_name"=>"Timmis", "scopus_author_id"=>"8987417100"}, {"first_name"=>"Paul S.", "last_name"=>"Andrews", "scopus_author_id"=>"8974576300"}, {"first_name"=>"Henrique", "last_name"=>"Veiga-Fernandes", "scopus_author_id"=>"6508113770"}, {"first_name"=>"Mark", "last_name"=>"Coles", "scopus_author_id"=>"26643204500"}], "year"=>2013, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"368490509", "sgr"=>"84874780992", "issn"=>"1553734X", "pmid"=>"23468606", "scopus"=>"2-s2.0-84874780992", "doi"=>"10.1371/journal.pcbi.1002916", "isbn"=>"1553-7358"}, "id"=>"5ac3f15a-b846-3657-9705-e97435f97313", "abstract"=>"Integrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology. Here we present spartan (Simulation Parameter Analysis R Toolkit ApplicatioN), a package of statistical techniques specifically designed to help researchers understand this relationship and provide novel biological insight. The tools comprising spartan help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity. Statistical analyses reveal the influence that pathways and components have on simulation behaviour, offering valuable biological insight into aspects of the system under study. We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation. Spartan is released under a GPLv2 license, implemented within the open source R statistical environment, and freely available from both the Comprehensive R Archive Network (CRAN) and http://www.cs.york.ac.uk/spartan. The techniques within the package can be applied to traditional ordinary or partial differential equation simulations as well as agent-based implementations. Manuals, comprehensive tutorials, and example simulation data upon which spartan can be applied are available from the website.", "link"=>"http://www.mendeley.com/research/spartan-comprehensive-tool-understanding-uncertainty-simulations-biological-systems", "reader_count"=>101, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>4, "Researcher"=>32, "Student > Doctoral Student"=>6, "Student > Ph. D. 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Student"=>33, "Student > Postgraduate"=>3, "Other"=>4, "Student > Master"=>6, "Student > Bachelor"=>3, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>9, "Agricultural and Biological Sciences"=>49, "Computer Science"=>10, "Decision Sciences"=>2, "Economics, Econometrics and Finance"=>1, "Engineering"=>3, "Environmental Science"=>7, "Biochemistry, Genetics and Molecular Biology"=>5, "Mathematics"=>5, "Medicine and Dentistry"=>2, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>3, "Social Sciences"=>2, "Immunology and Microbiology"=>1, "Chemical Engineering"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Social Sciences"=>{"Social Sciences"=>2}, "Decision Sciences"=>{"Decision Sciences"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>9}, "Environmental Science"=>{"Environmental Science"=>7}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Chemical Engineering"=>{"Chemical Engineering"=>1}, "Engineering"=>{"Engineering"=>3}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>49}, "Computer Science"=>{"Computer Science"=>10}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}}, "reader_count_by_country"=>{"Benin"=>1, "Netherlands"=>1, "Sweden"=>1, "United States"=>4, "Norway"=>1, "Brazil"=>2, "United Kingdom"=>6, "Belarus"=>1, "Chile"=>1, "Germany"=>2}, "group_count"=>6}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/973819"], "description"=>"<p><i>Spartan's</i> consistency analysis technique that can determine the number of runs required to generate a representative result from a stochastic simulation. A, B, C: A-Test scores for sample sizes of 5, 50, and 300 runs respectively. D. Maximum A Test score for each simulation response over 20 result sets for all sample sizes analysed. Scores below 0.5 are assigned corresponding values above 0.5 as direction of effect is not important. The effect magnitude thresholds are indicated.</p>", "links"=>[], "tags"=>["mitigate", "aleatory", "stochastic"], "article_id"=>642073, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Immunology"], "users"=>["Kieran Alden", "Mark Read", "Jon Timmis", "Paul S. Andrews", "Henrique Veiga-Fernandes", "Mark Coles"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002916.g001", "stats"=>{"downloads"=>0, "page_views"=>29, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Use_of_spartan_to_mitigate_aleatory_uncertainty_in_stochastic_simulations_/642073", "title"=>"Use of <i>spartan</i> to mitigate aleatory uncertainty in stochastic simulations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-01 12:12:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/973825"], "description"=>"<p>A: A-Test scores for simulations perturbing the initial expression level of a chemoattractant. This parameter has a large effect on both simulation responses. B: A-Test scores for simulations perturbing the upper limit of chemoattractant expression, which when perturbed has no significant effect on simulation response. C: Distribution of cell displacement responses for the parameter perturbed in A.</p>", "links"=>[], "tags"=>["simulators", "robustness", "parameter"], "article_id"=>642075, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Immunology"], "users"=>["Kieran Alden", "Mark Read", "Jon Timmis", "Paul S. Andrews", "Henrique Veiga-Fernandes", "Mark Coles"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002916.g002", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Use_of_spartan_to_determine_the_simulators_robustness_to_parameter_perturbation_/642075", "title"=>"Use of <i>spartan</i> to determine the simulators robustness to parameter perturbation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-01 12:13:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/973827"], "description"=>"<p>A: Parameter that captures the chemoattractant expression level required to influence cell motility. No trend or effects are apparent. B: Parameter which captures the level of adhesion required to restrict cell motility. A clear trend is apparent suggesting this has a large influence on simulated cell behaviour.</p>", "links"=>[], "tags"=>["immunology", "Computational biology", "computer science", "mathematics"], "article_id"=>642076, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Immunology"], "users"=>["Kieran Alden", "Mark Read", "Jon Timmis", "Paul S. Andrews", "Henrique Veiga-Fernandes", "Mark Coles"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002916.g003", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Use_of_spartan_to_identify_compound_effects_between_parameters_/642076", "title"=>"Use of <i>spartan</i> to identify compound effects between parameters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-01 12:13:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/973831"], "description"=>"<p>Si (black): the fraction of output variance that can be explained by the value assigned to that parameter; STi (grey): the variance caused by higher-order non-linear effects between that parameter and the others explored. Error bars are standard error over three resample curves. A: Velocity response. B: Displacement response.</p>", "links"=>[], "tags"=>["efast", "partition", "variance", "simulation"], "article_id"=>642078, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Immunology"], "users"=>["Kieran Alden", "Mark Read", "Jon Timmis", "Paul S. Andrews", "Henrique Veiga-Fernandes", "Mark Coles"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002916.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Use_of_eFAST_method_within_spartan_to_partition_variance_in_simulation_results_between_parameters_/642078", "title"=>"Use of eFAST method within <i>spartan</i> to partition variance in simulation results between parameters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-01 12:13:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/973834", "https://ndownloader.figshare.com/files/973835"], "description"=>"<div><p>Integrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of <i>in silico</i> results in terms of the biology. Here we present <i>spartan</i> (<u>S</u>imulation <u>P</u>arameter <u>A</u>nalysis <u>R</u><u>T</u>oolkit <u>A</u>pplicatio<u>N</u>), a package of statistical techniques specifically designed to help researchers understand this relationship and provide novel biological insight. The tools comprising <i>spartan</i> help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity. Statistical analyses reveal the influence that pathways and components have on simulation behaviour, offering valuable biological insight into aspects of the system under study. We demonstrate the power of <i>spartan</i> in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation. <i>Spartan</i> is released under a GPLv2 license, implemented within the open source R statistical environment, and freely available from both the Comprehensive R Archive Network (CRAN) and <a href=\"http://www.cs.york.ac.uk/spartan\" target=\"_blank\">http://www.cs.york.ac.uk/spartan</a>. The techniques within the package can be applied to traditional ordinary or partial differential equation simulations as well as agent-based implementations. Manuals, comprehensive tutorials, and example simulation data upon which <i>spartan</i> can be applied are available from the website.</p> </div>", "links"=>[], "tags"=>["simulations", "systems"], "article_id"=>642079, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Immunology"], "users"=>["Kieran Alden", "Mark Read", "Jon Timmis", "Paul S. Andrews", "Henrique Veiga-Fernandes", "Mark Coles"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002916.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002916.s002"], "stats"=>{"downloads"=>11, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spartan_A_Comprehensive_Tool_for_Understanding_Uncertainty_in_Simulations_of_Biological_Systems__/642079", "title"=>"<em>Spartan</em>: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-03-01 12:15:17"}

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Relative Metric

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