Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity
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{"title"=>"Modular design of artificial tissue homeostasis: Robust control through synthetic cellular heterogeneity", "type"=>"journal", "authors"=>[{"first_name"=>"Miles", "last_name"=>"Miller", "scopus_author_id"=>"55492785200"}, {"first_name"=>"Marc", "last_name"=>"Hafner", "scopus_author_id"=>"35271523600"}, {"first_name"=>"Eduardo", "last_name"=>"Sontag", "scopus_author_id"=>"7102557112"}, {"first_name"=>"Noah", "last_name"=>"Davidsohn", "scopus_author_id"=>"55328052900"}, {"first_name"=>"Sairam", "last_name"=>"Subramanian", "scopus_author_id"=>"7202867732"}, {"first_name"=>"Priscilla E M", "last_name"=>"Purnick", "scopus_author_id"=>"22942380400"}, {"first_name"=>"Douglas", "last_name"=>"Lauffenburger", "scopus_author_id"=>"7101609910"}, {"first_name"=>"Ron", "last_name"=>"Weiss", "scopus_author_id"=>"34573965800"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-84864613352", "pmid"=>"22829755", "pui"=>"365384799", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "sgr"=>"84864613352", "doi"=>"10.1371/journal.pcbi.1002579", "issn"=>"1553734X"}, "id"=>"79912d69-0779-3ccb-8463-abc28e1b7906", "abstract"=>"Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of β-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for 'synthetic cellular heterogeneity' that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a 'phenotypic sensitivity analysis' method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated.", "link"=>"http://www.mendeley.com/research/modular-design-artificial-tissue-homeostasis-robust-control-through-synthetic-cellular-heterogeneity", "reader_count"=>129, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>5, "Librarian"=>1, "Student > Doctoral Student"=>7, "Researcher"=>34, "Student > Ph. D. Student"=>41, "Student > Postgraduate"=>1, "Other"=>9, "Student > Master"=>12, "Student > Bachelor"=>12, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>4}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>5, "Librarian"=>1, "Student > Doctoral Student"=>7, "Researcher"=>34, "Student > Ph. D. Student"=>41, "Student > Postgraduate"=>1, "Other"=>9, "Student > Master"=>12, "Student > Bachelor"=>12, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Agricultural and Biological Sciences"=>59, "Chemical Engineering"=>1, "Chemistry"=>4, "Computer Science"=>8, "Engineering"=>17, "Biochemistry, Genetics and Molecular Biology"=>19, "Mathematics"=>1, "Medicine and Dentistry"=>5, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>2, "Psychology"=>2, "Social Sciences"=>4, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Social Sciences"=>{"Social Sciences"=>4}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>2}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>5}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Chemical Engineering"=>{"Chemical Engineering"=>1}, "Engineering"=>{"Engineering"=>17}, "Chemistry"=>{"Chemistry"=>4}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>59}, "Computer Science"=>{"Computer Science"=>8}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>19}}, "reader_count_by_country"=>{"New Zealand"=>1, "Canada"=>1, "Argentina"=>1, "Latvia"=>1, "Sweden"=>1, "United States"=>14, "United Kingdom"=>2, "Mexico"=>1, "Italy"=>1, "France"=>2, "Portugal"=>1, "Spain"=>1}, "group_count"=>7}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/606922"], "description"=>"<p>(A,G) Circuit diagrams of the genetic components considered in (A) oscillator and (G) throttle optimization. (B,H) The most significant RS-HDMR sensitivity indices, , for parametric variations of the oscillator and throttle, respectively. (C,I) Observed S/N values as a function of randomly sampled rate constant values. Around 2000 different parameter sets were tested, with all oscillator or throttle parameters simultaneously varied. Each point represents an individual parameter set. Warmer colors and contour lines indicate higher point density. (D,J) Inferred first-order RS-HDMR functions describing S/N as a function of the parameters sampled in <i>C</i> and <i>I</i>. (E,K) Heat map of the S/N values against the parameters resulting from the 2000 parameter sets tested in <i>C</i> and <i>I</i>. (F,L) RS-HDMR second-order functions describing the cooperative effects between rate constants, corresponding to <i>E</i> and <i>K</i>. Second-order RS-HDMR functions capture remaining variance after the first-order functions (see <i>D</i> and <i>J</i>) have been subtracted from the data.</p>", "links"=>[], "tags"=>["genetics and genomics", "molecular biology", "Computational biology"], "article_id"=>277408, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics"], "users"=>["Miles Miller", "Marc Hafner", "Eduardo Sontag", "Noah Davidsohn", "Sairam Subramanian", "Priscilla E. M. Purnick", "Douglas Lauffenburger", "Ron Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002579.g007", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parametric_sensitivity_analysis_/277408", "title"=>"Parametric sensitivity analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-19 02:03:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/606419"], "description"=>"<p>(A) Circuit diagram: two Population Control modules (in gray) sense the density of stem- and -cells. The AND gate integrates the output of the modules to induce differentiation. Circles represent intercellular signaling molecules. (B) Two examples of population evolution showing sustained oscillations (point 1 in <i>C</i>) and a stable steady state (point 2 in <i>C</i>), with other parameters fixed (SI Sec. 2 and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002579#pcbi.1002579.s002\" target=\"_blank\">Figure S2</a>). (C) A planar slice of the parameter space where population oscillations occur for System 1.</p>", "links"=>[], "tags"=>["genetics and genomics", "molecular biology", "Computational biology"], "article_id"=>276909, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics"], "users"=>["Miles Miller", "Marc Hafner", "Eduardo Sontag", "Noah Davidsohn", "Sairam Subramanian", "Priscilla E. M. Purnick", "Douglas Lauffenburger", "Ron Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002579.g002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_System_1_/276909", "title"=>"System 1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-19 01:55:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/607111"], "description"=>"<p>(A) Bayesian network inference using oscillator rate constants and phenotypes. (B) Bayesian network inference using throttle rate constants and phenotypes. Black arrows indicate the most direct connections between a node and S/N. The Bayesian inference describes phenotype groupings relevant to state values (blue), timing (yellow), and variability (red), along with the rate constants that control these phenotypes (green).</p>", "links"=>[], "tags"=>["networks", "synthetic", "heterogeneity", "module", "phenotypes", "constants"], "article_id"=>277594, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics"], "users"=>["Miles Miller", "Marc Hafner", "Eduardo Sontag", "Noah Davidsohn", "Sairam Subramanian", "Priscilla E. M. Purnick", "Douglas Lauffenburger", "Ron Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002579.g009", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Bayesian_networks_of_the_impact_of_synthetic_heterogeneity_module_phenotypes_and_rate_constants_on_system_performance_value_S_N_/277594", "title"=>"Bayesian networks of the impact of synthetic heterogeneity module phenotypes and rate constants on system performance value (S/N).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-19 02:06:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/607005"], "description"=>"<p>(A,F) Phenotypic behavior of the oscillator (A) and throttle (F), when isolated from the full system. Roughly 2000 different sets of rate constants were tested, with all oscillator or throttle rate constants simultaneously varied. Module phenotypes were recorded for each set of rate constants. (B) Observed S/N values as a function of variance in the “duration high” of the oscillator. (C) Heat map of the S/N values against the phenotypes resulting from the random parameter sets. (G) Average ‘images’ for the phenotype <i>R7 T to St. St.</i>, observed from the random parameter sets yielding an S/N value of either 5, 15 or 25. Black represents regions where no switch occurs and no value for <i>R7 T to St. St.</i> is recorded. (D,H) The most significant RS-HDMR sensitivity indices, , for phenotypic variations of the oscillator and throttle, respectively (see also Supplementary <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002579#pcbi.1002579.s027\" target=\"_blank\">Table S8</a>). (E,I) For the oscillator and throttle, respectively, RS-HDMR cross-validation predication accuracy using rate constants, phenotypes, or both.</p>", "links"=>[], "tags"=>["genetics and genomics", "molecular biology", "Computational biology"], "article_id"=>277493, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics"], "users"=>["Miles Miller", "Marc Hafner", "Eduardo Sontag", "Noah Davidsohn", "Sairam Subramanian", "Priscilla E. M. Purnick", "Douglas Lauffenburger", "Ron Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002579.g008", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Phenotypic_sensitivity_analysis_/277493", "title"=>"Phenotypic sensitivity analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-19 02:04:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/606616"], "description"=>"<p>(A) Circuit diagram for System 3: in addition to System 2 modules, the AND gate integrates the output of the oscillator (red module) that allows commitment only when peaking. (B) Time trajectories for a simulation starting with a small stem cell population. The oscillator activator () is plotted for some representative stem cells (right axis, a.u.). (C) Individual rows track the single-cell UPC module output (, shown as a heat map) in uncommitted cells within a population. White signifies single-cell commitment, followed by black “null space” that is filled by newly divided uncommitted cells. Due to the oscillator, only a fraction of the cells commit when the concentration is high. (D) Overall system performance, S/N, as a function of the module time-scale for cell communication, . Several hundred different sets of time-scales were tested, with all time-scale parameters simultaneously varied. Each point represents an individual set of time-scales. Color and contour lines indicate point density. (E) Circuit diagram for System 4: a throttle mechanism (red module) activates during a cell's commitment and represses commitment in its neighbors. (F–G) Time trajectories for a simulation starting with a small stem cell population, where <i>B</i> shows the average throttle signaling component () in the external medium (right axis, a.u.) over time. (H) S/N as a function of the module time-scale for cell communication, .</p>", "links"=>[], "tags"=>["genetics and genomics", "molecular biology", "Computational biology"], "article_id"=>277103, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics"], "users"=>["Miles Miller", "Marc Hafner", "Eduardo Sontag", "Noah Davidsohn", "Sairam Subramanian", "Priscilla E. M. Purnick", "Douglas Lauffenburger", "Ron Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002579.g004", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Systems_3_and_4_/277103", "title"=>"Systems 3 and 4.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-19 01:58:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/606728"], "description"=>"<p>(A) S/N for different cell volume , which corresponds to the number of molecules in each cell. (B) S/N for different ratios of stem cell division rate () and -cell killing rate (). (C–D) RS-HDMR analysis of Systems 2–4 to changes in the reaction time-scales of module components. (C) First- and (D) second-order RS-HDMR component functions describe the relationship between reaction time-scales (normalized to [0,1]) and the corresponding S/N observed in the overall system. (E) Distribution of S/N observed in response to time-scale parameter sampling (black) and RS-HDMR inference accuracy of that variation (blue). (F) Total sensitivity indices () of the module time-scales observed for each system.</p>", "links"=>[], "tags"=>["analyses", "time-scale", "optimization", "systems"], "article_id"=>277209, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics"], "users"=>["Miles Miller", "Marc Hafner", "Eduardo Sontag", "Noah Davidsohn", "Sairam Subramanian", "Priscilla E. M. Purnick", "Douglas Lauffenburger", "Ron Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002579.g005", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Robustness_analyses_and_time_scale_optimization_for_Systems_2_8211_4_/277209", "title"=>"Robustness analyses and time-scale optimization for Systems 2–4.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-19 02:00:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/316872", "https://ndownloader.figshare.com/files/316910", "https://ndownloader.figshare.com/files/316978", "https://ndownloader.figshare.com/files/317016", "https://ndownloader.figshare.com/files/317071", "https://ndownloader.figshare.com/files/317120", "https://ndownloader.figshare.com/files/317163", "https://ndownloader.figshare.com/files/317234", "https://ndownloader.figshare.com/files/317300", "https://ndownloader.figshare.com/files/317518", "https://ndownloader.figshare.com/files/317693", "https://ndownloader.figshare.com/files/317907", "https://ndownloader.figshare.com/files/318015", "https://ndownloader.figshare.com/files/318083", "https://ndownloader.figshare.com/files/318152", "https://ndownloader.figshare.com/files/318205", "https://ndownloader.figshare.com/files/318246", "https://ndownloader.figshare.com/files/318280", "https://ndownloader.figshare.com/files/318337", "https://ndownloader.figshare.com/files/318401", "https://ndownloader.figshare.com/files/318455", "https://ndownloader.figshare.com/files/318489", "https://ndownloader.figshare.com/files/318564", "https://ndownloader.figshare.com/files/318616", "https://ndownloader.figshare.com/files/318664", "https://ndownloader.figshare.com/files/318714", "https://ndownloader.figshare.com/files/318754", "https://ndownloader.figshare.com/files/318796", "https://ndownloader.figshare.com/files/318842"], "description"=>"<div><p>Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of <em>β</em>-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for ‘synthetic cellular heterogeneity’ that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a ‘phenotypic sensitivity analysis’ method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated.</p> </div>", "links"=>[], "tags"=>["modular", "robust", "synthetic", "cellular", "heterogeneity"], "article_id"=>122538, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics"], "users"=>["Miles Miller", "Marc Hafner", "Eduardo Sontag", "Noah Davidsohn", "Sairam Subramanian", "Priscilla E. M. Purnick", "Douglas Lauffenburger", "Ron Weiss"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002579.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s002", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s003", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s004", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s005", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s006", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s007", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s008", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s009", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s010", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s011", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s012", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s013", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s014", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s015", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s016", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s017", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s018", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s019", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s020", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s021", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s022", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s023", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s024", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s025", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s026", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s027", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s028", "https://dx.doi.org/10.1371/journal.pcbi.1002579.s029"], "stats"=>{"downloads"=>131, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Modular_Design_of_Artificial_Tissue_Homeostasis_Robust_Control_through_Synthetic_Cellular_Heterogeneity/122538", "title"=>"Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-07-19 00:42:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/606334"], "description"=>"<p>(A) The general tissue homeostasis design. Proliferation of stem cells (blue) is regulated by their population size through negative feedback (dashed blue line). Sequential differentiation into endodermic, pancreatic, and finally -cells (red) occurs when the stem cell population has sufficient size, and is governed through negative feedback from differentiated cells (dashed red line). (B) Design workflow. Starting with a high-level objective, iterative design proceeds through a top-down decomposition into modules and then basic reactions of the system, followed by analysis and redesign (left). The table columns (right) show the four iterations of system designs presented in this work. Table rows describe the top-down decomposition for each system, and correspond to the workflow at left.</p>", "links"=>[], "tags"=>["genetics and genomics", "molecular biology", "Computational biology"], "article_id"=>276821, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics"], "users"=>["Miles Miller", "Marc Hafner", "Eduardo Sontag", "Noah Davidsohn", "Sairam Subramanian", "Priscilla E. M. Purnick", "Douglas Lauffenburger", "Ron Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002579.g001", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_system_design_/276821", "title"=>"Overview of system design.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-19 01:53:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/606812"], "description"=>"<p>(A) GA optimization progress for three representative generations, using an ODE model of the UPC module. The GA objective function is a three-component step-function, with zero UPC activity below a defined threshold, an ignored transition region, and high activity above the transition region. (B) Gillespie simulations of System 3, corresponding to optimization progress in <i>A</i>. (C) Average UPC module transfer curves when the reverse response is either excluded or included in the subnetwork GA optimization. (D) Full system behavior corresponding by row to the module optimization results in <i>C</i>. (E) Distribution of rate constants for the optimized parameter vectors determined by 75 independent GA runs of 1000 generations each, using both forward and reverse response objective functions. (F) Clustered sensitivity analysis of the UPC Module. Each column corresponds to a “parameter sensitivity signature” for each of the 75 local parameter neighborhoods that we sampled; rows correspond to the analyzed parameters of the UPC module. First-order sensitivity values shown in the heat map range from 0.0 (black) to 0.5 (red).</p>", "links"=>[], "tags"=>["optimization", "upc"], "article_id"=>277304, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics"], "users"=>["Miles Miller", "Marc Hafner", "Eduardo Sontag", "Noah Davidsohn", "Sairam Subramanian", "Priscilla E. M. Purnick", "Douglas Lauffenburger", "Ron Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002579.g006", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parametric_optimization_of_the_UPC_module_/277304", "title"=>"Parametric optimization of the UPC module.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-19 02:01:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/606502"], "description"=>"<p>(A) Circuit diagram: two Population Control modules sense the density of stem and committed cells. The AND gate integrates the output of the modules to induce commitment through the switch state (red module). (B) Deterministic time trajectories for System 2 with two different initial conditions: both converge to the same equilibrium populations. (C) Phase space diagram: all trajectories converge to a unique equilibrium point. Black lines correspond to trajectories plotted in <i>B</i>. See <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002579#pcbi.1002579.s029\" target=\"_blank\">Text S1</a>, Sec. 2 and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002579#pcbi.1002579.s003\" target=\"_blank\">Figure S3</a> for other phase space diagrams. (D) Stochastic trajectories for a simulation starting with a small stem cell population, showing the output of the Committed Population Control module () in representative uncommitted cells (right axis, a.u.). (E) Individual rows track the single-cell UPC module output (, shown as a heat map) in uncommitted cells within a population. White signifies single-cell commitment, followed by black “null space” that is filled by newly divided uncommitted cells. As soon as UPC output is high (yellow), stem cells commit <i>en masse</i>. (F) Overall system performance, S/N, as a function of the module time-scale for cell communication, . Several hundred different sets of time-scales were tested, with all time-scale parameters simultaneously varied. Each point represents an individual set of time-scales. Color and contour lines indicate point density.</p>", "links"=>[], "tags"=>["genetics and genomics", "molecular biology", "Computational biology"], "article_id"=>276991, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics"], "users"=>["Miles Miller", "Marc Hafner", "Eduardo Sontag", "Noah Davidsohn", "Sairam Subramanian", "Priscilla E. M. Purnick", "Douglas Lauffenburger", "Ron Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002579.g003", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_System_2_/276991", "title"=>"System 2.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-19 01:56:31"}

PMC Usage Stats | Further Information

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Relative Metric

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