StochPy: A Comprehensive, User-Friendly Tool for Simulating Stochastic Biological Processes
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{"title"=>"StochPy: A comprehensive, user-friendly tool for simulating stochastic biological processes", "type"=>"journal", "authors"=>[{"first_name"=>"Timo R.", "last_name"=>"Maarleveld", "scopus_author_id"=>"55796743800"}, {"first_name"=>"Brett G.", "last_name"=>"Olivier", "scopus_author_id"=>"7101797474"}, {"first_name"=>"Frank J.", "last_name"=>"Bruggeman", "scopus_author_id"=>"6701834840"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"isbn"=>"1932-6203 (Electronic)\r1932-6203 (Linking)", "scopus"=>"2-s2.0-84894181658", "pui"=>"372403023", "doi"=>"10.1371/journal.pone.0079345", "sgr"=>"84894181658", "pmid"=>"24260203", "issn"=>"19326203"}, "id"=>"1d9a5256-bff4-37e2-85bd-404e556a3f18", "abstract"=>"Single-cell and single-molecule measurements indicate the importance of stochastic phenomena in cell biology. Stochasticity creates spontaneous differences in the copy numbers of key macromolecules and the timing of reaction events between genetically-identical cells. Mathematical models are indispensable for the study of phenotypic stochasticity in cellular decision-making and cell survival. There is a demand for versatile, stochastic modeling environments with extensive, preprogrammed statistics functions and plotting capabilities that hide the mathematics from the novice users and offers low-level programming access to the experienced user. Here we present StochPy (Stochastic modeling in Python), which is a flexible software tool for stochastic simulation in cell biology. It provides various stochastic simulation algorithms, SBML support, analyses of the probability distributions of molecule copy numbers and event waiting times, analyses of stochastic time series, and a range of additional statistical functions and plotting facilities for stochastic simulations. We illustrate the functionality of StochPy with stochastic models of gene expression, cell division, and single-molecule enzyme kinetics. StochPy has been successfully tested against the SBML stochastic test suite, passing all tests. StochPy is a comprehensive software package for stochastic simulation of the molecular control networks of living cells. It allows novice and experienced users to study stochastic phenomena in cell biology. The integration with other Python software makes StochPy both a user-friendly and easily extendible simulation tool.", "link"=>"http://www.mendeley.com/research/stochpy-comprehensive-userfriendly-tool-simulating-stochastic-biological-processes", "reader_count"=>53, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>9, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>1, "Student > Master"=>11, "Other"=>2, "Student > Bachelor"=>4, "Professor"=>1, "Lecturer"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>9, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>1, "Student > Master"=>11, "Other"=>2, "Student > Bachelor"=>4, "Professor"=>1, "Lecturer"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>4, "Unspecified"=>1, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>6, "Mathematics"=>4, "Agricultural and Biological Sciences"=>22, "Medicine and Dentistry"=>1, "Physics and Astronomy"=>10, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>4}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>10}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>22}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"United States"=>2, "Mexico"=>1, "Slovenia"=>1, "France"=>1, "Germany"=>3, "India"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1286180"], "description"=>"<p>Summary of features offered in StochPy and other stochastic modeling software.</p><p>•: Feature is present.</p>○<p>: Feature is partially present or requires additional dependencies.</p><p>Notes: 1. Limited ability to parse kinetic laws: Complicated expressions may not parsed. 2. Not all SBML documents can be converted into the StochKit2 model format. 3. Provided as an add-on functionality of StochKit2, whereas with limited options compared to the default installation of StochKit2. 4. Only if proprietary software (MATLAB) is installed.</p>", "links"=>[], "tags"=>["stochpy"], "article_id"=>854989, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Timo R. Maarleveld", "Brett G. Olivier", "Frank J. Bruggeman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0079345.t001", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Feature_comparison_between_StochPy_and_existing_stochastic_software_/854989", "title"=>"Feature comparison between StochPy and existing (stochastic) software.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-11-18 04:01:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/1286179"], "description"=>"<p>Results of benchmarking the direct method of StochPy. Simulation time was divided by the simulation time of the StochPy solvers: StochPy’s solver was faster if the reported ratio’s are larger than one and vice versa. A “−” indicates that short and long simulations were done to illustrate the potential difference between them. N/A is shown if the simulator was not possible to perform the simulation. For parallel simulations, 100 trajectories were done. In each comparison the number of fixed intervals was equal to the number of time steps in the simulation. Simulations were done on a Intel Core i5-2430M CPU 2.40 GHz×4 64-bit with Ubuntu 12.04 LTS as operating system. Stochastic models and a script to simulate these models within StochPy are available in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079345#pone.0079345.s003\" target=\"_blank\">Scripts S2</a>.</p><p>Notes:</p>1<p>StochPy with interfaces to CAIN and StochKit2. Simulation time includes time to parse results into StochPy.</p>2<p>Cain cannot parse events, so the user most specify them in the GUI.</p>3<p>Optimal theoretical result without including time to merge the output of all sequential simulations.</p>", "links"=>[], "tags"=>["benchmark", "stochpy"], "article_id"=>854988, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Timo R. Maarleveld", "Brett G. Olivier", "Frank J. Bruggeman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0079345.t002", "stats"=>{"downloads"=>0, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Speed_performance_benchmark_between_StochPy_and_existing_stochastic_software_/854988", "title"=>"Speed performance benchmark between StochPy and existing (stochastic) software.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-11-18 04:01:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/1286176"], "description"=>"<p>StochPy plots for single-molecule enzyme activity simulations with StochPy simulations (step, markers, blue) and analytical solutions (solid, black) (A–B) time-series data of and , . (C) three time trajectories that fluctuate around the analytical solution. This analytical solution corresponds to the mean rate of formation, which for stochastic simulations can be obtained by taking the average of many generated time trajectories. (D) event waiting times of product formation peak.</p>", "links"=>[], "tags"=>[], "article_id"=>854985, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Timo R. Maarleveld", "Brett G. Olivier", "Frank J. Bruggeman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0079345.g006", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Single_molecule_enzymology_/854985", "title"=>"Single-molecule enzymology.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-18 04:01:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/1286174"], "description"=>"<p>StochPy plots of simulating stochastic gene expression. (A) long lifetimes of both the ON and OFF state. (B) bursty transcription. (C) short lifetimes of both the ON and OFF state. (D) non-bursty transcription.</p>", "links"=>[], "tags"=>["bursty", "non-bursty"], "article_id"=>854983, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Timo R. Maarleveld", "Brett G. Olivier", "Frank J. Bruggeman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0079345.g004", "stats"=>{"downloads"=>2, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Time_series_of_bursty_and_non_bursty_transcription_/854983", "title"=>"Time series of bursty and non-bursty transcription.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-18 04:01:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/1286175"], "description"=>"<p>StochPy plots of simulating stochastic gene expression with StochPy simulations (step, markers, colored) and analytical solutions (solid, black). (A) probability distribution of the mRNA copy numbers. (B) probability distribution of the mRNA synthesis event waiting times.</p>", "links"=>[], "tags"=>["waiting", "times"], "article_id"=>854984, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Timo R. Maarleveld", "Brett G. Olivier", "Frank J. Bruggeman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0079345.g005", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_mRNA_copy_number_and_event_waiting_times_distributions_/854984", "title"=>"mRNA copy number and event waiting times distributions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-18 04:01:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/1286173"], "description"=>"<p>Illustration of several plotting options in StochPy. Colored lines represent StochPy output and black the analytical solutions. (A) species time-series data. (B) propensities time-series data. (C) species distribution. (D) propensities distribution. (E) auto-correlation for different values (0.01, 0.025, 0.05, 0.1, 0.5).</p>", "links"=>[], "tags"=>[], "article_id"=>854982, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Timo R. Maarleveld", "Brett G. Olivier", "Frank J. Bruggeman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0079345.g003", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Propensities_and_auto_correlations_/854982", "title"=>"Propensities and auto-correlations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-18 04:01:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/1286181", "https://ndownloader.figshare.com/files/1286182", "https://ndownloader.figshare.com/files/1286183", "https://ndownloader.figshare.com/files/1286184"], "description"=>"<div><p>Single-cell and single-molecule measurements indicate the importance of stochastic phenomena in cell biology. Stochasticity creates spontaneous differences in the copy numbers of key macromolecules and the timing of reaction events between genetically-identical cells. Mathematical models are indispensable for the study of phenotypic stochasticity in cellular decision-making and cell survival. There is a demand for versatile, stochastic modeling environments with extensive, preprogrammed statistics functions and plotting capabilities that hide the mathematics from the novice users and offers low-level programming access to the experienced user. Here we present StochPy (<i>Stoch</i>astic modeling in <i>Py</i>thon), which is a flexible software tool for stochastic simulation in cell biology. It provides various stochastic simulation algorithms, SBML support, analyses of the probability distributions of molecule copy numbers and event waiting times, analyses of stochastic time series, and a range of additional statistical functions and plotting facilities for stochastic simulations. We illustrate the functionality of StochPy with stochastic models of gene expression, cell division, and single-molecule enzyme kinetics. StochPy has been successfully tested against the SBML stochastic test suite, passing all tests. StochPy is a comprehensive software package for stochastic simulation of the molecular control networks of living cells. It allows novice and experienced users to study stochastic phenomena in cell biology. The integration with other Python software makes StochPy both a user-friendly and easily extendible simulation tool.</p></div>", "links"=>[], "tags"=>["user-friendly", "simulating", "stochastic"], "article_id"=>854990, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Timo R. Maarleveld", "Brett G. Olivier", "Frank J. Bruggeman"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0079345.s001", "https://dx.doi.org/10.1371/journal.pone.0079345.s002", "https://dx.doi.org/10.1371/journal.pone.0079345.s003", "https://dx.doi.org/10.1371/journal.pone.0079345.s004"], "stats"=>{"downloads"=>25, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_StochPy_A_Comprehensive_User_Friendly_Tool_for_Simulating_Stochastic_Biological_Processes_/854990", "title"=>"StochPy: A Comprehensive, User-Friendly Tool for Simulating Stochastic Biological Processes", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-11-18 04:01:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/1286178"], "description"=>"<p>Both fixed-interval and explicit output have their advantages and disadvantages. The decision whether to use fixed-interval or explicit output depends on the type of analysis.</p>", "links"=>[], "tags"=>["modeling"], "article_id"=>854987, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Timo R. Maarleveld", "Brett G. Olivier", "Frank J. Bruggeman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0079345.g008", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Stochastic_modeling_Decision_Tree_/854987", "title"=>"Stochastic modeling Decision Tree.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-18 04:01:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/1286177"], "description"=>"<p>StochPy plots of simulating stochastic gene expression. Modeling details of cell division periods: Gamma-distributed with scale parameter is 60.0 and shape parameter is 1.0. Implicit and explicit time series of transcription factor copy numbers (A and D), mRNA copy numbers (B and E), and protein copy numbers (C and F). Distributions of protein copy numbers for modeling cell division explicitly and implicitly (G). The model is further described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079345#pone.0079345.s004\" target=\"_blank\">Information S1</a> Section 5.</p>", "links"=>[], "tags"=>["single-cell", "transcription"], "article_id"=>854986, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Timo R. Maarleveld", "Brett G. Olivier", "Frank J. Bruggeman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0079345.g007", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Modeling_single_cell_transcription_and_translation_with_and_without_cell_division_/854986", "title"=>"Modeling single-cell transcription and translation with and without cell division.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-18 04:01:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/1286172"], "description"=>"<p>Hundred stochastic simulations until t = 60.000 min ( time steps) were done with min<sup>−1</sup>, min<sup>−1</sup>, min<sup>−1</sup>, and min<sup>−1</sup>. (A) Accuracy of mean and standard deviation estimates as function of the number of fixed intervals. (B) Simulation time with fixed-interval output increases with the number of fixed intervals. Fixed-interval simulations were done with the StochPy interface to StochKit2 and include the time to calculate the associated probability distributions. (C) The stationary mRNA distribution for fixed intervals (red error bars, 1.96 ) vs. explicit output (blue 95% confidence interval). Note that 1.96 corresponds to a 95% confidence interval. (D) The stationary mRNA distribution for fixed intervals (red error bars, 1.96 ) vs. explicit output (blue 95% confidence interval).</p>", "links"=>[], "tags"=>["simulation"], "article_id"=>854981, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Timo R. Maarleveld", "Brett G. Olivier", "Frank J. Bruggeman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0079345.g002", "stats"=>{"downloads"=>4, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fixed_interval_versus_explicit_simulation_output_/854981", "title"=>"Fixed interval versus explicit simulation output.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-18 04:01:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/1286171"], "description"=>"<p>An example of explicit simulation output of StochPy is shown in a table. It reports the number of molecules of each molecular species and the reaction propensities at each time point when a reaction occurs. The time differences between consecutive rows indicate waiting times between reaction events. In the last column, the waiting times for reaction 4, , are given and they correspond to the time period between consecutive instances of activity of reaction 4.</p>", "links"=>[], "tags"=>["simulation"], "article_id"=>854980, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Timo R. Maarleveld", "Brett G. Olivier", "Frank J. Bruggeman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0079345.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_StochPy_simulation_output_/854980", "title"=>"StochPy simulation output.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-18 04:01:12"}

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Relative Metric

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