A Bayesian Approach to the Evolution of Metabolic Networks on a Phylogeny
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{"title"=>"A Bayesian approach to the evolution of metabolic networks on a phylogeny", "type"=>"journal", "authors"=>[{"first_name"=>"Aziz", "last_name"=>"Mithani", "scopus_author_id"=>"26640614700"}, {"first_name"=>"Gail M.", "last_name"=>"Preston", "scopus_author_id"=>"35420703700"}, {"first_name"=>"Jotun", "last_name"=>"Hein", "scopus_author_id"=>"7103275229"}], "year"=>2010, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"78049435631", "doi"=>"10.1371/journal.pcbi.1000868", "pui"=>"359882436", "pmid"=>"20700467", "scopus"=>"2-s2.0-78049435631", "issn"=>"1553734X", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)"}, "id"=>"ebbdc1d1-9291-363b-b969-767f49b0bb79", "abstract"=>"The availability of genomes of many closely related bacteria with diverse metabolic capabilities offers the possibility of tracing metabolic evolution on a phylogeny relating the genomes to understand the evolutionary processes and constraints that affect the evolution of metabolic networks. Using simple (independent loss/gain of reactions) or complex (incorporating dependencies among reactions) stochastic models of metabolic evolution, it is possible to study how metabolic networks evolve over time. Here, we describe a model that takes the reaction neighborhood into account when modeling metabolic evolution. The model also allows estimation of the strength of the neighborhood effect during the course of evolution. We present Gibbs samplers for sampling networks at the internal node of a phylogeny and for estimating the parameters of evolution over a phylogeny without exploring the whole search space by iteratively sampling from the conditional distributions of the internal networks and parameters. The samplers are used to estimate the parameters of evolution of metabolic networks of bacteria in the genus Pseudomonas and to infer the metabolic networks of the ancestral pseudomonads. The results suggest that pathway maps that are conserved across the Pseudomonas phylogeny have a stronger neighborhood structure than those which have a variable distribution of reactions across the phylogeny, and that some Pseudomonas lineages are going through genome reduction resulting in the loss of a number of reactions from their metabolic networks.", "link"=>"http://www.mendeley.com/research/bayesian-approach-evolution-metabolic-networks-phylogeny-6", "reader_count"=>54, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>8, "Researcher"=>17, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>1, "Student > Master"=>9, "Other"=>1, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>8, "Researcher"=>17, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>1, "Student > Master"=>9, "Other"=>1, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>2, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>3, "Mathematics"=>1, "Agricultural and Biological Sciences"=>33, "Medicine and Dentistry"=>1, "Philosophy"=>1, "Physics and Astronomy"=>1, "Computer Science"=>7, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>33}, "Computer Science"=>{"Computer Science"=>7}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>2}, "Environmental Science"=>{"Environmental Science"=>2}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Iran"=>1, "United States"=>4, "Japan"=>1, "Brazil"=>1, "Kenya"=>1, "Germany"=>1, "Spain"=>2}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/416987", "https://ndownloader.figshare.com/files/417006", "https://ndownloader.figshare.com/files/417030", "https://ndownloader.figshare.com/files/417056", "https://ndownloader.figshare.com/files/417077", "https://ndownloader.figshare.com/files/417126", "https://ndownloader.figshare.com/files/417153", "https://ndownloader.figshare.com/files/417174", "https://ndownloader.figshare.com/files/417207", "https://ndownloader.figshare.com/files/417282", "https://ndownloader.figshare.com/files/417335", "https://ndownloader.figshare.com/files/417353", "https://ndownloader.figshare.com/files/417362", "https://ndownloader.figshare.com/files/417386", "https://ndownloader.figshare.com/files/417389", "https://ndownloader.figshare.com/files/417392", "https://ndownloader.figshare.com/files/417394", "https://ndownloader.figshare.com/files/417395"], "description"=>"<div><p>The availability of genomes of many closely related bacteria with diverse metabolic capabilities offers the possibility of tracing metabolic evolution on a phylogeny relating the genomes to understand the evolutionary processes and constraints that affect the evolution of metabolic networks. Using simple (independent loss/gain of reactions) or complex (incorporating dependencies among reactions) stochastic models of metabolic evolution, it is possible to study how metabolic networks evolve over time. Here, we describe a model that takes the reaction neighborhood into account when modeling metabolic evolution. The model also allows estimation of the strength of the neighborhood effect during the course of evolution. We present Gibbs samplers for sampling networks at the internal node of a phylogeny and for estimating the parameters of evolution over a phylogeny without exploring the whole search space by iteratively sampling from the conditional distributions of the internal networks and parameters. The samplers are used to estimate the parameters of evolution of metabolic networks of bacteria in the genus <em>Pseudomonas</em> and to infer the metabolic networks of the ancestral pseudomonads. The results suggest that pathway maps that are conserved across the <em>Pseudomonas</em> phylogeny have a stronger neighborhood structure than those which have a variable distribution of reactions across the phylogeny, and that some <em>Pseudomonas</em> lineages are going through genome reduction resulting in the loss of a number of reactions from their metabolic networks.</p></div>", "links"=>[], "tags"=>["bayesian", "metabolic", "networks", "phylogeny"], "article_id"=>142313, "categories"=>["Biological Sciences", "Cancer", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s004", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s005", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s006", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s007", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s008", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s009", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s010", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s011", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s012", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s013", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s014", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s015", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s016", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s017", "https://dx.doi.org/10.1371/journal.pcbi.1000868.s018"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_Bayesian_Approach_to_the_Evolution_of_Metabolic_Networks_on_a_Phylogeny/142313", "title"=>"A Bayesian Approach to the Evolution of Metabolic Networks on a Phylogeny", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-08-05 00:38:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/836966"], "description"=>"<p>(A) Toy networks consisting of 13 nodes. The nodes are labeled from A to M (blue) and the hyperedges are labeled from 1 to 10 (red). The reference network consists of all allowed hyperedges for this example system. Networks , and consist of subsets of the hyperedges from the reference network. (B) A phylogeny connecting the networks , and .</p>", "links"=>[], "tags"=>["networks", "connected"], "article_id"=>507333, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Toy_networks_connected_by_a_phylogeny_/507333", "title"=>"Toy networks connected by a phylogeny.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:02:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/837019"], "description"=>"<p>The tree contains arbitrary networks assigned at the internal nodes. Also shown are the proportion of insertion and deletion events and the proportion of allowed insertion and deletion events while going from various ancestral networks to descendant networks.</p>", "links"=>[], "tags"=>["phylogenetic", "networks", "shown"], "article_id"=>507386, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_sample_phylogenetic_tree_for_the_toy_networks_shown_in_Figure_1_/507386", "title"=>"A sample phylogenetic tree for the toy networks shown in <b>Figure 1</b>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:03:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/837079"], "description"=>"<p>The pseudo-likelihood of going from the network to the network (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi-1000868-g001\" target=\"_blank\">Figure 1</a>) was calculated conditioned on for different sub-network sizes. The times taken for calculating the pseudo-likelihood averaged across three runs are shown in red. The horizontal lines show the average CPU time taken using the MCMC approach based on path sampling described in Mithani <i>et al. </i><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi.1000868-Mithani1\" target=\"_blank\">[9]</a> with 11,000 iterations including first 1,000 iteration as burn-in period (blue dashed line) and by exponentiating the full network (brown dotted line).</p>", "links"=>[], "tags"=>["cpu", "taken", "pseudo-likelihood"], "article_id"=>507444, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_CPU_time_taken_for_calculating_the_pseudo_likelihood_on_toy_networks_/507444", "title"=>"Average CPU time taken for calculating the pseudo-likelihood on toy networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:04:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/837140"], "description"=>"<p>Boxplots showing the degree distributions of nodes obtained by simulating the evolution for the pentose phosphate pathway, lysine degradation and phenylalanine metabolism maps in <i>P. fluorescens</i> Pf0-1 under (A) the neighbor-dependent model and (B) the hybrid model. The red line plots the actual degree distributions observed in the corresponding pathway map in <i>P. fluorescens</i> Pf0-1.</p>", "links"=>[], "tags"=>["distributions", "nodes", "neighbor-dependent", "hybrid"], "article_id"=>507505, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Degree_distributions_of_nodes_under_the_neighbor_dependent_and_hybrid_models_/507505", "title"=>"Degree distributions of nodes under the neighbor-dependent and hybrid models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:05:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/837240"], "description"=>"<p>Parameters were estimated for the toy network phylogeny shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi-1000868-g001\" target=\"_blank\">Figure 1</a> using the Gibbs sampler described in Section ‘Estimation of parameters’. The values are averaged across three runs containing 60,000 iterations each.</p>", "links"=>[], "tags"=>["computational biology/evolutionary modeling", "computational biology/metabolic networks", "computational biology/systems biology"], "article_id"=>507607, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Autocorrelation_of_rate_parameters_/507607", "title"=>"Autocorrelation of rate parameters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:06:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/837283"], "description"=>"<p>The phylogenies were generated using multi-locus sequence analysis of conserved housekeeping genes (<i>gapA</i>, <i>gltA</i>, <i>rpoD</i> and <i>gyrB</i>). (A) Phylogeny relating the seventeen genome-sequenced <i>Pseudomonas</i> strains. (B) Phylogeny relating the three strains of non pathogenic <i>P. fluorescens</i>. (C) Phylogeny relating the three strains of plant pathogenic <i>P. syringae</i>. Strain abbreviations: pae: <i>P. aeruginosa</i> PAO1, pap: <i>P. aeruginosa</i> PA7, pau: <i>P. aeruginosa</i> PA14, pag: <i>P. aeruginosa</i> LESB58, pen: <i>P. entomophila</i> L48, pfl: <i>P. fluorescens</i> Pf-5, pfo: <i>P. fluorescens</i> Pf0-1, pfs: <i>P. fluorescens</i> SBW25, pmy: <i>P. mendocina</i> ymp, ppf: <i>P. putida</i> F1, ppg: <i>P. putida</i> GB-1 ppu: <i>P. putida</i> KT2440, ppw: <i>P. putida</i> W619, psa: <i>P. stutzeri</i> A1501, psb: <i>P. syringae</i> pv. syringae B728a, psp: <i>P. syringae</i> pv. phaseolicola 1448A, and pst: <i>P. syringae</i> pv. tomato DC3000.</p>", "links"=>[], "tags"=>["connecting", "belonging", "genus"], "article_id"=>507652, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Phylogenies_connecting_bacteria_belonging_to_the_genus_Pseudomonas_/507652", "title"=>"Phylogenies connecting bacteria belonging to the genus <i>Pseudomonas</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:07:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/837348"], "description"=>"<p>The ancestral networks were reconstructed over the <i>Pseudomonas</i> phylogeny shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi-1000868-g006\" target=\"_blank\">Figure 6A</a>. Also shown in the bottom panel is the distribution of reactions across different <i>Pseudomonas</i> strains. (A) Likelihood of being present for alterable reactions at various levels of <i>Pseudomonas</i> phylogeny obtained by calculating the proportion of times each hyperedge was present in the networks sampled by the Gibbs sampler. (B) Reaction status obtained under maximum parsimony calculated using the Fitch algorithm <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi.1000868-Fitch1\" target=\"_blank\">[26]</a>. When assigning the reactions at the ancestral nodes the ties were resolved in favor of presence of reactions. Reactions for which parsimony failed to resolve ancestral predictions at the root are marked with an asterisk (*). Strain abbreviations: pae: <i>P. aeruginosa</i> PAO1, pap: <i>P. aeruginosa</i> PA7, pau: <i>P. aeruginosa</i> PA14, pag: <i>P. aeruginosa</i> LESB58, pen: <i>P. entomophila</i> L48, pfl: <i>P. fluorescens</i> Pf-5, pfo: <i>P. fluorescens</i> Pf0-1, pfs: <i>P. fluorescens</i> SBW25, pmy: <i>P. mendocina</i> ymp, ppf: <i>P. putida</i> F1, ppg: <i>P. putida</i> GB-1 ppu: <i>P. putida</i> KT2440, ppw: <i>P. putida</i> W619, psa: <i>P. stutzeri</i> A1501, psb: <i>P. syringae</i> pv. syringae B728a, psp: <i>P. syringae</i> pv. phaseolicola 1448A, and pst: <i>P. syringae</i> pv. tomato DC3000.</p>", "links"=>[], "tags"=>["reconstruction"], "article_id"=>507720, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ancestral_network_reconstruction_for_the_glycolysis_gluconeogenesis_map_/507720", "title"=>"Ancestral network reconstruction for the glycolysis/gluconeogenesis map.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:08:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/837557"], "description"=>"<p>The ancestral networks were reconstructed over the <i>Pseudomonas</i> phylogeny shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi-1000868-g006\" target=\"_blank\">Figure 6A</a>. Also shown in the bottom panel is the distribution of reactions across different <i>Pseudomonas</i> strains. (A) Likelihood of being present for alterable reactions at various levels of <i>Pseudomonas</i> phylogeny obtained by calculating the proportion of times each hyperedge was present in the networks sampled by the Gibbs sampler. (B) Reaction status obtained under maximum parsimony calculated using the Fitch algorithm <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi.1000868-Fitch1\" target=\"_blank\">[26]</a>. When assigning the reactions at the ancestral nodes the ties were resolved in favor of presence of reactions. Reactions for which parsimony failed to resolve ancestral predictions at the root are marked with an asterisk (*). Strain abbreviations: pae: <i>P. aeruginosa</i> PAO1, pap: <i>P. aeruginosa</i> PA7, pau: <i>P. aeruginosa</i> PA14, pag: <i>P. aeruginosa</i> LESB58, pen: <i>P. entomophila</i> L48, pfl: <i>P. fluorescens</i> Pf-5, pfo: <i>P. fluorescens</i> Pf0-1, pfs: <i>P. fluorescens</i> SBW25, pmy: <i>P. mendocina</i> ymp, ppf: <i>P. putida</i> F1, ppg: <i>P. putida</i> GB-1 ppu: <i>P. putida</i> KT2440, ppw: <i>P. putida</i> W619, psa: <i>P. stutzeri</i> A1501, psb: <i>P. syringae</i> pv. syringae B728a, psp: <i>P. syringae</i> pv. phaseolicola 1448A, and pst: <i>P. syringae</i> pv. tomato DC3000.</p>", "links"=>[], "tags"=>["reconstruction", "pentose", "phosphate", "pathway"], "article_id"=>507924, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.g008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ancestral_network_reconstruction_for_the_pentose_phosphate_pathway_map_/507924", "title"=>"Ancestral network reconstruction for the pentose phosphate pathway map.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:12:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/837669"], "description"=>"<p>The ancestral networks were reconstructed over the <i>Pseudomonas</i> phylogeny shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi-1000868-g006\" target=\"_blank\">Figure 6A</a>. Also shown in the bottom panel is the distribution of reactions across different <i>Pseudomonas</i> strains. (A) Likelihood of being present for alterable reactions at various levels of <i>Pseudomonas</i> phylogeny obtained by calculating the proportion of times each hyperedge was present in the networks sampled by the Gibbs sampler. (B) Reaction status obtained under maximum parsimony calculated using the Fitch algorithm <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi.1000868-Fitch1\" target=\"_blank\">[26]</a>. When assigning the reactions at the ancestral nodes the ties were resolved in favor of presence of reactions. Strain abbreviations: pae: <i>P. aeruginosa</i> PAO1, pap: <i>P. aeruginosa</i> PA7, pau: <i>P. aeruginosa</i> PA14, pag: <i>P. aeruginosa</i> LESB58, pen: <i>P. entomophila</i> L48, pfl: <i>P. fluorescens</i> Pf-5, pfo: <i>P. fluorescens</i> Pf0-1, pfs: <i>P. fluorescens</i> SBW25, pmy: <i>P. mendocina</i> ymp, ppf: <i>P. putida</i> F1, ppg: <i>P. putida</i> GB-1 ppu: <i>P. putida</i> KT2440, ppw: <i>P. putida</i> W619, psa: <i>P. stutzeri</i> A1501, psb: <i>P. syringae</i> pv. syringae B728a, psp: <i>P. syringae</i> pv. phaseolicola 1448A, and pst: <i>P. syringae</i> pv. tomato DC3000.</p>", "links"=>[], "tags"=>["reconstruction", "lysine", "degradation"], "article_id"=>508034, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.g009"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ancestral_network_reconstruction_for_the_lysine_degradation_map_/508034", "title"=>"Ancestral network reconstruction for the lysine degradation map.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:13:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/837766"], "description"=>"<p>The ancestral networks were reconstructed over the <i>Pseudomonas</i> phylogeny shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi-1000868-g006\" target=\"_blank\">Figure 6A</a>. Also shown in the bottom panel is the distribution of reactions across different <i>Pseudomonas</i> strains. (A) Likelihood of being present for alterable reactions at various levels of <i>Pseudomonas</i> phylogeny obtained by calculating the proportion of times each hyperedge was present in the networks sampled by the Gibbs sampler. (B) Reaction status obtained under maximum parsimony calculated using the Fitch algorithm <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi.1000868-Fitch1\" target=\"_blank\">[26]</a>. When assigning the reactions at the ancestral nodes the ties were resolved in favor of presence of reactions. Strain abbreviations: pae: <i>P. aeruginosa</i> PAO1, pap: <i>P. aeruginosa</i> PA7, pau: <i>P. aeruginosa</i> PA14, pag: <i>P. aeruginosa</i> LESB58, pen: <i>P. entomophila</i> L48, pfl: <i>P. fluorescens</i> Pf-5, pfo: <i>P. fluorescens</i> Pf0-1, pfs: <i>P. fluorescens</i> SBW25, pmy: <i>P. mendocina</i> ymp, ppf: <i>P. putida</i> F1, ppg: <i>P. putida</i> GB-1 ppu: <i>P. putida</i> KT2440, ppw: <i>P. putida</i> W619, psa: <i>P. stutzeri</i> A1501, psb: <i>P. syringae</i> pv. syringae B728a, psp: <i>P. syringae</i> pv. phaseolicola 1448A, and pst: <i>P. syringae</i> pv. tomato DC3000.</p>", "links"=>[], "tags"=>["reconstruction", "histidine", "metabolism"], "article_id"=>508137, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.g010"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ancestral_network_reconstruction_for_the_histidine_metabolism_map_/508137", "title"=>"Ancestral network reconstruction for the histidine metabolism map.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:15:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/837865"], "description"=>"<p>The ancestral networks were reconstructed over the <i>Pseudomonas</i> phylogeny shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi-1000868-g006\" target=\"_blank\">Figure 6A</a>. Also shown in the bottom panel is the distribution of reactions across different <i>Pseudomonas</i> strains. (A) Likelihood of being present for alterable reactions at various levels of <i>Pseudomonas</i> phylogeny obtained by calculating the proportion of times each hyperedge was present in the networks sampled by the Gibbs sampler. (B) Reaction status obtained under maximum parsimony calculated using the Fitch algorithm <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi.1000868-Fitch1\" target=\"_blank\">[26]</a>. When assigning the reactions at the ancestral nodes the ties were resolved in favor of presence of reactions. Strain abbreviations: pae: <i>P. aeruginosa</i> PAO1, pap: <i>P. aeruginosa</i> PA7, pau: <i>P. aeruginosa</i> PA14, pag: <i>P. aeruginosa</i> LESB58, pen: <i>P. entomophila</i> L48, pfl: <i>P. fluorescens</i> Pf-5, pfo: <i>P. fluorescens</i> Pf0-1, pfs: <i>P. fluorescens</i> SBW25, pmy: <i>P. mendocina</i> ymp, ppf: <i>P. putida</i> F1, ppg: <i>P. putida</i> GB-1 ppu: <i>P. putida</i> KT2440, ppw: <i>P. putida</i> W619, psa: <i>P. stutzeri</i> A1501, psb: <i>P. syringae</i> pv. syringae B728a, psp: <i>P. syringae</i> pv. phaseolicola 1448A, and pst: <i>P. syringae</i> pv. tomato DC3000.</p>", "links"=>[], "tags"=>["reconstruction", "phenylalanine", "metabolism"], "article_id"=>508230, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.g011"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ancestral_network_reconstruction_for_the_phenylalanine_metabolism_map_/508230", "title"=>"Ancestral network reconstruction for the phenylalanine metabolism map.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:17:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/837971"], "description"=>"<p>The ancestral networks were reconstructed over the <i>Pseudomonas</i> phylogeny shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi-1000868-g006\" target=\"_blank\">Figure 6A</a>. Also shown in the bottom panel is the distribution of reactions across different <i>Pseudomonas</i> strains. (A) Likelihood of being present for alterable reactions at various levels of <i>Pseudomonas</i> phylogeny obtained by calculating the proportion of times each hyperedge was present in the networks sampled by the Gibbs sampler. (B) Reaction status obtained under maximum parsimony calculated using the Fitch algorithm <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi.1000868-Fitch1\" target=\"_blank\">[26]</a>. When assigning the reactions at the ancestral nodes the ties were resolved in favor of presence of reactions. Strain abbreviations: pae: <i>P. aeruginosa</i> PAO1, pap: <i>P. aeruginosa</i> PA7, pau: <i>P. aeruginosa</i> PA14, pag: <i>P. aeruginosa</i> LESB58, pen: <i>P. entomophila</i> L48, pfl: <i>P. fluorescens</i> Pf-5, pfo: <i>P. fluorescens</i> Pf0-1, pfs: <i>P. fluorescens</i> SBW25, pmy: <i>P. mendocina</i> ymp, ppf: <i>P. putida</i> F1, ppg: <i>P. putida</i> GB-1 ppu: <i>P. putida</i> KT2440, ppw: <i>P. putida</i> W619, psa: <i>P. stutzeri</i> A1501, psb: <i>P. syringae</i> pv. syringae B728a, psp: <i>P. syringae</i> pv. phaseolicola 1448A, and pst: <i>P. syringae</i> pv. tomato DC3000.</p>", "links"=>[], "tags"=>["reconstruction", "pyruvate", "metabolism"], "article_id"=>508342, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.g012"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ancestral_network_reconstruction_for_the_pyruvate_metabolism_map_/508342", "title"=>"Ancestral network reconstruction for the pyruvate metabolism map.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:19:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/838072"], "description"=>"<p>The maximum likelihood estimates (MLEs) of the parameter values (: insertion rate, : deletion rate and : neighbor dependence probability), maximum log-likelihoods, likelihood (LH) ratios, and the -values for different pathway maps in <i>P. fluorescens</i> Pf0-1 used in this analysis. The MLEs were obtained using the Gibbs sampler described by Mithani <i>et al.</i><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi.1000868-Mithani1\" target=\"_blank\">[9]</a> by evolving the networks from <i>P. fluorescens</i> Pf-5 to <i>P. fluorescens</i> Pf0-1. The equilibrium probability of a network was used as the likelihood of observing the network. The low -values for all the pathway maps suggest a better fit for the hybrid model compared to the neighbor-dependent model.</p>", "links"=>[], "tags"=>["neighbor-dependent", "hybrid", "models", "metabolic"], "article_id"=>508442, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Likelihood_ratio_test_between_the_neighbor_dependent_and_hybrid_models_of_metabolic_evolution_/508442", "title"=>"Likelihood ratio test between the neighbor-dependent and hybrid models of metabolic evolution.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-08-05 02:20:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/838121"], "description"=>"<p>Posterior expectation and variance of parameter values (: neighbor dependence probability, : insertion rate and : deletion rate) estimated using the Gibbs sampler under the hybrid model for the phylogenies relating the bacteria belonging to genus <i>Pseudomonas</i> (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi-1000868-g006\" target=\"_blank\">Figure 6</a>). Hyperedges common to all seventeen genome-sequenced strains were defined as core and hyperedges missing in all seventeen strains were defined as prohibited hyperedges. The values are averaged over three runs of 60,000 iterations for <i>P. fluorescens</i> and <i>P. syringae</i> phylogenies, and 110,000 iterations for the phylogeny connecting the seventeen <i>Pseudomonas</i> strains with the first 10,000 iterations regarded as burn-in period in each case. Samples were collected every iteration. The codes MAPxxxxx correspond to the respective KEGG pathway codes <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000868#pcbi.1000868-Kanehisa1\" target=\"_blank\">[22]</a>. Strain abbreviations: pfl: <i>P. fluorescens</i> Pf-5, pfo: <i>P. fluorescens</i> Pf0-1, pfs: <i>P. fluorescens</i> SBW25, psb: <i>P. syringae</i> pv. syringae B728a, psp: <i>P. syringae</i> pv. phaseolicola 1448A, and pst: <i>P. syringae</i> pv. tomato DC3000.</p>", "links"=>[], "tags"=>["variance", "parameters", "gibbs", "sampler", "hybrid"], "article_id"=>508485, "categories"=>["Computational Biology", "Infectious Diseases", "Medicine"], "users"=>["Aziz Mithani", "Gail M. Preston", "Jotun Hein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000868.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Posterior_expectation_and_variance_of_evolution_parameters_estimated_using_the_Gibbs_sampler_under_the_hybrid_model_/508485", "title"=>"Posterior expectation and variance of evolution parameters estimated using the Gibbs sampler under the hybrid model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-08-05 02:21:25"}

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