Temporal Expression-based Analysis of Metabolism
Publication Date
November 29, 2012
Journal
PLOS Computational Biology
Authors
Sara B. Collins, Ed Reznik & Daniel Segrè
Volume
8
Issue
11
Pages
e1002781
DOI
https://dx.plos.org/10.1371/journal.pcbi.1002781
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1002781
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/23209390
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3510039
Europe PMC
http://europepmc.org/abstract/MED/23209390
Web of Science
000311897100039
Scopus
84870689618
Mendeley
http://www.mendeley.com/research/temporal-expressionbased-analysis-metabolism
Events
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CiteULike | Further Information

Mendeley | Further Information

{"title"=>"Temporal Expression-based Analysis of Metabolism", "type"=>"journal", "authors"=>[{"first_name"=>"Sara B.", "last_name"=>"Collins", "scopus_author_id"=>"55385437100"}, {"first_name"=>"Ed", "last_name"=>"Reznik", "scopus_author_id"=>"36239156300"}, {"first_name"=>"Daniel", "last_name"=>"Segrè", "scopus_author_id"=>"7005643974"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-84870689618", "sgr"=>"84870689618", "issn"=>"1553734X", "arxiv"=>"1203.2655", "doi"=>"10.1371/journal.pcbi.1002781", "pmid"=>"23209390", "isbn"=>"0011-3891", "pui"=>"366216175"}, "id"=>"8a0c0117-3241-390d-93b7-549b8d382b6d", "abstract"=>"Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such \"history-dependent\" sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques.", "link"=>"http://www.mendeley.com/research/temporal-expressionbased-analysis-metabolism", "reader_count"=>99, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>6, "Student > Doctoral Student"=>3, "Researcher"=>28, "Student > Ph. D. Student"=>35, "Student > Postgraduate"=>5, "Student > Master"=>11, "Other"=>2, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>6, "Student > Doctoral Student"=>3, "Researcher"=>28, "Student > Ph. D. Student"=>35, "Student > Postgraduate"=>5, "Student > Master"=>11, "Other"=>2, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>8, "Unspecified"=>1, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>14, "Agricultural and Biological Sciences"=>61, "Medicine and Dentistry"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Chemical Engineering"=>1, "Physics and Astronomy"=>4, "Computer Science"=>7}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>8}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>61}, "Computer Science"=>{"Computer Science"=>7}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>14}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Chemical Engineering"=>{"Chemical Engineering"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Sweden"=>1, "Netherlands"=>1, "Iran"=>2, "United States"=>9, "Norway"=>1, "Japan"=>1, "Denmark"=>2, "Slovenia"=>1, "Russia"=>1}, "group_count"=>7}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/533346"], "description"=>"<p>TEAM integrates three types of experimental data: starting media composition, expression data, and biomass data. Pre-calculations include normalization of the gene expression data, interpolation of all data sets, and calculation of gene penalties based on the gene expression data. For a given time interval, TEAM calculates the metabolic flux distribution most consistent with gene expression and biomass data. It applies this result to update media conditions for the subsequent time interval.</p>", "links"=>[], "tags"=>["integrating", "types"], "article_id"=>203840, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Sara B. Collins", "Ed Reznik", "Daniel Segrè"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002781.g001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Workflow_for_integrating_multiple_data_types_with_TEAM_/203840", "title"=>"Workflow for integrating multiple data types with TEAM.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:04:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/533850"], "description"=>"<p>Superposition of metabolic flux onto central carbon metabolism of <i>S. oneidensis</i>. Top panel corresponds to a Type 2 penalty threshold of 65%, and bottom panel to a Type 2 penalty threshold of 85%. Large nodes and edges on the networks represent reactions and small nodes correspond to metabolites. The colors of the large nodes correspond to the penalty associated with that reaction. Colored squares on the network plots identify the transport reactions for each exchange metabolite. A network key and reaction and metabolite details can be found in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002781#pcbi.1002781.s004\" target=\"_blank\">Figure S3</a>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002781#pcbi.1002781.s007\" target=\"_blank\">Table S1</a> and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002781#pcbi.1002781.s008\" target=\"_blank\">Table S2</a> respectively. Detailed time-course flux predictions are provided in Supplementary <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002781#pcbi.1002781.s002\" target=\"_blank\">Dataset S1</a>.</p>", "links"=>[], "tags"=>["flux", "profiles"], "article_id"=>204347, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Sara B. Collins", "Ed Reznik", "Daniel Segrè"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002781.g006", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_comparison_of_internal_flux_profiles_for_two_different_penalty_thresholds_/204347", "title"=>"A comparison of internal flux profiles for two different penalty thresholds.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:12:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/533543"], "description"=>"<p>Distributions calculated using a pooled set of data from M3D <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002781#pcbi.1002781-Faith1\" target=\"_blank\">[17]</a> and time-course experimental data <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002781#pcbi.1002781-Beg1\" target=\"_blank\">[18]</a>. For both example genes, the distribution of gene expression measurements and the corresponding cumulative distribution function (CDF) are shown. For each CDF, individual gene penalty thresholds are found for common percentiles <i>θ</i> = 25%, 50% and 75%. D-lactate dehydrogenase expression measurements (A) have a higher mean expression and a more pronounced peak than acetate kinase (C), which is more uniformly distributed. The corresponding CDFs capture this variation in distribution. D-lactate dehydrogenase expression penalties (B) are higher and less distributed than those of acetate kinase (D). Mean gene expression (E) and standard deviation (F) over all genes for a single time point are also shown. All microarrays contain 4230 gene products, and each individual distribution contains 310 data points; 19 come from the experimental time-course and 281 from M3D.</p>", "links"=>[], "tags"=>["genes"], "article_id"=>204044, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Sara B. Collins", "Ed Reznik", "Daniel Segrè"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002781.g003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overall_distribution_of_S_oneidensis_gene_expression_measurements_with_two_individual_genes_highlighted_/204044", "title"=>"Overall distribution of <i>S. oneidensis</i> gene expression measurements with two individual genes highlighted.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:07:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/533742"], "description"=>"<p>For all percentage thresholds <i>θ</i> between 1% and 99%, the quality of predictions for (A) pyruvate and (B) acetate secretion and utilization behavior was calculated using the residual sum of squares between the experimental HPLC measurements and the model predictions for all three gene penalty calculation types. Only the Type 2 and 3 penalty thresholds predict the secretion of pyruvate, occurring between <i>θ = </i>55% and <i>θ = </i>82%.</p>", "links"=>[], "tags"=>["predictive", "pyruvate", "acetate", "excretion"], "article_id"=>204239, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Sara B. Collins", "Ed Reznik", "Daniel Segrè"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002781.g005", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_measure_of_predictive_accuracy_between_pyruvate_and_acetate_excretion_behavior_/204239", "title"=>"A measure of predictive accuracy between pyruvate and acetate excretion behavior.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:10:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/533451"], "description"=>"<p>The media contained 36 mM L-Lactate, 13 mM D-lactate, 9 mM ammonium, and other minimally required nutrients. The oxygen concentration was set to 10 mM at each time point, mimicking the controlled 100% dissolved oxygen (DO) concentration from the experiment. The resulting usage dynamics of several metabolites of interest (including combined DL-lactate, ammonium, pyruvate, acetate, formate and glycolate) as predicted by dFBA are compared to experimental data. (A) HPLC Data, (B) dFBA, (C) TEAM with a global penalty threshold (Type 1), (D) TEAM with a gene-specific penalty threshold (Type 2), (E) TEAM with a gene-specific penalty threshold normalized by standard deviation (Type 3). Black dots represent hours when microarray measurements were taken.</p>", "links"=>[], "tags"=>["methods"], "article_id"=>203952, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Sara B. Collins", "Ed Reznik", "Daniel Segrè"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002781.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_comparison_of_results_across_different_methods_for_a_representative_penalty_threshold_/203952", "title"=>"A comparison of results across different methods for a representative penalty threshold.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:05:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/287642", "https://ndownloader.figshare.com/files/287672", "https://ndownloader.figshare.com/files/287715", "https://ndownloader.figshare.com/files/287766", "https://ndownloader.figshare.com/files/287830", "https://ndownloader.figshare.com/files/287870", "https://ndownloader.figshare.com/files/287912", "https://ndownloader.figshare.com/files/287966"], "description"=>"<div><p>Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium <em>Shewanella oneidensis</em> grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter <em>θ</em>, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of <em>θ</em> to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such “history-dependent” sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques.</p> </div>", "links"=>[], "tags"=>["temporal", "expression-based", "metabolism"], "article_id"=>116665, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Sara B. Collins", "Ed Reznik", "Daniel Segrè"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002781.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002781.s002", "https://dx.doi.org/10.1371/journal.pcbi.1002781.s003", "https://dx.doi.org/10.1371/journal.pcbi.1002781.s004", "https://dx.doi.org/10.1371/journal.pcbi.1002781.s005", "https://dx.doi.org/10.1371/journal.pcbi.1002781.s006", "https://dx.doi.org/10.1371/journal.pcbi.1002781.s007", "https://dx.doi.org/10.1371/journal.pcbi.1002781.s008"], "stats"=>{"downloads"=>10, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Temporal_Expression_based_Analysis_of_Metabolism__/116665", "title"=>"Temporal Expression-based Analysis of Metabolism", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-11-29 01:51:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/533619"], "description"=>"<p>(A) Total carbon concentration in media for each penalty threshold <i>θ</i>, summed over all time points. Penalty thresholds between 40% and 75% exhibit enrichment for intermediate carbon sources formate, glycolate, pyruvate. (B) Extinction time of lactate and ammonium in the media. Lactate runs out significantly earlier for intermediate penalty thresholds. Heatmap indicates the total media concentration of secreted carbon sources (acetate, pyruvate, glycolate, formate).</p>", "links"=>[], "tags"=>["gene-specific"], "article_id"=>204120, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Sara B. Collins", "Ed Reznik", "Daniel Segrè"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002781.g004", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sensitivity_analysis_for_Type_2_gene_specific_threshold_/204120", "title"=>"Sensitivity analysis for Type 2 gene-specific threshold.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:08:40"}

PMC Usage Stats | Further Information

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Relative Metric

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