On the Accessibility of Adaptive Phenotypes of a Bacterial Metabolic Network
Publication Date
August 21, 2009
Journal
PLOS Computational Biology
Authors
Wilfred Ndifon, Joshua B. Plotkin & Jonathan Dushoff
Volume
5
Issue
8
Pages
e1000472
DOI
http://doi.org/10.1371/journal.pcbi.1000472
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1000472
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/19696877
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2716542
Europe PMC
http://europepmc.org/abstract/MED/19696877
Web of Science
000270799700016
Scopus
70149090575
Mendeley
http://www.mendeley.com/research/accessibility-adaptive-phenotypes-bacterial-metabolic-network
Events
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Mendeley | Further Information

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CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/886450"], "description"=>"<p>The fraction <i>P</i>(<i>t</i>) of evolving populations that found the highest-fitness phenotype at (or before) the <i>t</i><sup>th</sup> generation is plotted against <i>t</i>.</p>", "links"=>[], "tags"=>["adaptive", "populations"], "article_id"=>556909, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Wilfred Ndifon", "Joshua B. Plotkin", "Jonathan Dushoff"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000472.g005", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Outcome_of_in_silico_adaptive_evolution_of_E_coli_populations_in_different_environments_/556909", "title"=>"Outcome of <i>in silico</i> adaptive evolution of <i>E. coli</i> populations in different environments.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-21 01:55:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/439520", "https://ndownloader.figshare.com/files/439623", "https://ndownloader.figshare.com/files/439653", "https://ndownloader.figshare.com/files/439685", "https://ndownloader.figshare.com/files/439710"], "description"=>"<div><p>The mechanisms by which adaptive phenotypes spread within an evolving population after their emergence are understood fairly well. Much less is known about the factors that influence the evolutionary accessibility of such phenotypes, a pre-requisite for their emergence in a population. Here, we investigate the influence of environmental quality on the accessibility of adaptive phenotypes of <em>Escherichia coli</em>'s central metabolic network. We used an established flux-balance model of metabolism as the basis for a genotype-phenotype map (GPM). We quantified the effects of seven qualitatively different environments (corresponding to both carbohydrate and gluconeogenic metabolic substrates) on the structure of this GPM. We found that the GPM has a more rugged structure in qualitatively poorer environments, suggesting that adaptive phenotypes could be intrinsically less accessible in such environments. Nevertheless, on average ∼74% of the genotype can be altered by neutral drift, in the environment where the GPM is most rugged; this could allow evolving populations to circumvent such ruggedness. Furthermore, we found that the normalized mutual information (NMI) of genotype differences relative to phenotype differences, which measures the GPM's capacity to transmit information about phenotype differences, is positively correlated with (simulation-based) estimates of the accessibility of adaptive phenotypes in different environments. These results are consistent with the predictions of a simple analytic theory that makes explicit the relationship between the NMI and the speed of adaptation. The results suggest an intuitive information-theoretic principle for evolutionary adaptation; adaptation could be faster in environments where the GPM has a greater capacity to transmit information about phenotype differences. More generally, our results provide insight into fundamental environment-specific differences in the accessibility of adaptive phenotypes, and they suggest opportunities for research at the interface between information theory and evolutionary biology.</p></div>", "links"=>[], "tags"=>["accessibility", "adaptive", "phenotypes", "bacterial", "metabolic"], "article_id"=>146654, "categories"=>["Biological Sciences", "Cancer", "Medicine"], "users"=>["Wilfred Ndifon", "Joshua B. Plotkin", "Jonathan Dushoff"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000472.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000472.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000472.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000472.s004", "https://dx.doi.org/10.1371/journal.pcbi.1000472.s005"], "stats"=>{"downloads"=>5, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/On_the_Accessibility_of_Adaptive_Phenotypes_of_a_Bacterial_Metabolic_Network/146654", "title"=>"On the Accessibility of Adaptive Phenotypes of a Bacterial Metabolic Network", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-08-21 01:50:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/886337"], "description"=>"<p>Shown are the correlation length (CL) of phenotype differences, the normalized mutual information (NMI) of genotype differences relative to phenotype differences, and the number of essential genes (essentiality) found in the metabolic network, under different environmental conditions. The environments are listed in increasing order of quality, except in the case of lactose whose position in the rank-ordering is not known precisely. The NMI was computed as described in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000472#s4\" target=\"_blank\">Materials and Methods</a>, using a mutation rate per genotype position of 0.001. Error bars indicate 95% confidence intervals.</p>", "links"=>[], "tags"=>["metabolic"], "article_id"=>556794, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Wilfred Ndifon", "Joshua B. Plotkin", "Jonathan Dushoff"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000472.g003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_statistics_on_the_structure_of_E_coli_s_metabolic_network_GPM_/556794", "title"=>"Summary statistics on the structure of <i>E. coli's</i> metabolic network GPM.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-21 01:53:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/886372"], "description"=>"<p>Neutral walks were performed as described in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000472#s4\" target=\"_blank\">Materials and Methods</a>. The length of a neutral walk corresponds to the Hamming distance between the final and starting genotypes associated with the walk.</p>", "links"=>[], "tags"=>["lengths", "walks"], "article_id"=>556838, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Wilfred Ndifon", "Joshua B. Plotkin", "Jonathan Dushoff"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000472.g004", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_the_lengths_of_neutral_walks_in_different_environments_/556838", "title"=>"Distribution of the lengths of neutral walks in different environments.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-21 01:53:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/886144"], "description"=>"<p>The PPD was computed in acetate and glucose environments.</p>", "links"=>[], "tags"=>["probability", "phenotype", "differences"], "article_id"=>556601, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Wilfred Ndifon", "Joshua B. Plotkin", "Jonathan Dushoff"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000472.g002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Conditional_probability_of_phenotype_differences_PPD_/556601", "title"=>"Conditional probability of phenotype differences (PPD).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-21 01:50:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/886073"], "description"=>"<p>The protein products of the genes b0116, b0726, and b0727 combine to form a protein complex that catalyzes production of succinate coenzyme A (SUCCOA) from alpha-ketoglutarate (AKG) and coenzyme A, with the concomitant reduction of nicotinamide adenine dinucleotide (NAD) and release of carbon dioxide (CO<sub>2</sub>). A matrix <i>S</i> of the stoichiometries of the reactants, and a vector <i>V</i> of fluxes are shown. <i>v</i>, <i>b</i><sub>1</sub>, <i>b</i><sub>2</sub>, <i>b</i><sub>3</sub>, <i>b</i><sub>4</sub>, <i>b</i><sub>5</sub>, and <i>b</i><sub>6</sub> denote the rates of the above reaction, the production of AKG, NAD, and COA, and the utilization of CO<sub>2</sub>, NADH, and SUCCOA, respectively (Note that this is a simplification of the way the reaction is actually represented in our model). At steady state <i>S</i>·<i>V</i> = 0. In the event that one of the genes catalyzing the above reaction is turned off by mutation, the reaction flux <i>v</i> is set to 0. Abbreviations (gene/protein product): b0116/LpdA, dihydrolipoamide dehydrogenase; b0726/SucAec, alpha-ketoglutarate decarboxylase; b0727/SucBec, dihydrolipoamide acetyltransferase.</p>", "links"=>[], "tags"=>["rules", "metabolic"], "article_id"=>556534, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Wilfred Ndifon", "Joshua B. Plotkin", "Jonathan Dushoff"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000472.g001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_example_of_the_interaction_rules_found_in_the_E_coli_metabolic_network_/556534", "title"=>"An example of the interaction rules found in the <i>E. coli</i> metabolic network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-21 01:48:54"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"1", "full-text"=>"0", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2012", "month"=>"11"}
  • {"unique-ip"=>"3", "full-text"=>"2", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"2"}
  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"3"}
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  • {"unique-ip"=>"1", "full-text"=>"0", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"5", "cited-by"=>"0", "year"=>"2013", "month"=>"8"}
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  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"11"}
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  • {"unique-ip"=>"2", "full-text"=>"1", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"5"}
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Relative Metric

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