Genome-Scale Modeling of Light-Driven Reductant Partitioning and Carbon Fluxes in Diazotrophic Unicellular Cyanobacterium Cyanothece sp. ATCC 51142
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{"title"=>"Genome-scale modeling of light-driven reductant partitioning and carbon fluxes in diazotrophic unicellular cyanobacterium Cyanothece sp. ATCC 51142", "type"=>"journal", "authors"=>[{"first_name"=>"Trang T.", "last_name"=>"Vu", "scopus_author_id"=>"36026924700"}, {"first_name"=>"Sergey M.", "last_name"=>"Stolyar", "scopus_author_id"=>"7004433701"}, {"first_name"=>"Grigoriy E.", "last_name"=>"Pinchuk", "scopus_author_id"=>"8534981200"}, {"first_name"=>"Eric A.", "last_name"=>"Hill", "scopus_author_id"=>"8709201300"}, {"first_name"=>"Leo A.", "last_name"=>"Kucek", "scopus_author_id"=>"55220100900"}, {"first_name"=>"Roslyn N.", "last_name"=>"Brown", "scopus_author_id"=>"7408437356"}, {"first_name"=>"Mary S.", "last_name"=>"Lipton", "scopus_author_id"=>"35966224800"}, {"first_name"=>"Andrei", "last_name"=>"Osterman", "scopus_author_id"=>"24352765400"}, {"first_name"=>"Jim K.", "last_name"=>"Fredrickson", "scopus_author_id"=>"7103231746"}, {"first_name"=>"Allan E.", "last_name"=>"Konopka", "scopus_author_id"=>"7004811061"}, {"first_name"=>"Alexander S.", "last_name"=>"Beliaev", "scopus_author_id"=>"7006151772"}, {"first_name"=>"Jennifer L.", "last_name"=>"Reed", "scopus_author_id"=>"56352741300"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"pmid"=>"22529767", "sgr"=>"84861120186", "doi"=>"10.1371/journal.pcbi.1002460", "scopus"=>"2-s2.0-84861120186", "pui"=>"364830866", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "issn"=>"1553734X"}, "id"=>"10e486dd-4194-3df6-b114-9a7c9d4f306e", "abstract"=>"Genome-scale metabolic models have proven useful for answering fundamental questions about metabolic capabilities of a variety of microorganisms, as well as informing their metabolic engineering. However, only a few models are available for oxygenic photosynthetic microorganisms, particularly in cyanobacteria in which photosynthetic and respiratory electron transport chains (ETC) share components. We addressed the complexity of cyanobacterial ETC by developing a genome-scale model for the diazotrophic cyanobacterium, Cyanothece sp. ATCC 51142. The resulting metabolic reconstruction, iCce806, consists of 806 genes associated with 667 metabolic reactions and includes a detailed representation of the ETC and a biomass equation based on experimental measurements. Both computational and experimental approaches were used to investigate light-driven metabolism in Cyanothece sp. ATCC 51142, with a particular focus on reductant production and partitioning within the ETC. The simulation results suggest that growth and metabolic flux distributions are substantially impacted by the relative amounts of light going into the individual photosystems. When growth is limited by the flux through photosystem I, terminal respiratory oxidases are predicted to be an important mechanism for removing excess reductant. Similarly, under photosystem II flux limitation, excess electron carriers must be removed via cyclic electron transport. Furthermore, in silico calculations were in good quantitative agreement with the measured growth rates whereas predictions of reaction usage were qualitatively consistent with protein and mRNA expression data, which we used to further improve the resolution of intracellular flux values.", "link"=>"http://www.mendeley.com/research/genomescale-modeling-lightdriven-reductant-partitioning-carbon-fluxes-diazotrophic-unicellular-cyano", "reader_count"=>88, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>4, "Librarian"=>1, "Researcher"=>31, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>23, "Student > Postgraduate"=>1, "Student > Master"=>7, "Other"=>2, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>4, "Librarian"=>1, "Researcher"=>31, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>23, "Student > Postgraduate"=>1, "Student > Master"=>7, "Other"=>2, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>16, "Unspecified"=>6, "Environmental Science"=>5, "Biochemistry, Genetics and Molecular Biology"=>8, "Mathematics"=>3, "Agricultural and Biological Sciences"=>44, "Medicine and Dentistry"=>1, "Chemistry"=>1, "Computer Science"=>3, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>16}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>44}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>8}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>6}, "Environmental Science"=>{"Environmental Science"=>5}}, "reader_count_by_country"=>{"Republic of Singapore"=>1, "Netherlands"=>1, "Czech Republic"=>1, "United States"=>9, "Brazil"=>1, "Mexico"=>1, "Malaysia"=>1, "France"=>2, "Thailand"=>1, "Spain"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/657374"], "description"=>"<p><i>Linear photosynthetic electron transfer</i>: electrons from photosystem II (PS II) to photosystem I (PS I) are transferred through plastoquinone (Pq), cytochrome <i>b<sub>6</sub>f</i> complex (Cyt b6f), plastocyanin (Pc) and cytochrome <i>c<sub>6</sub></i> (Cyt c6). From PS I electrons can be transferred to ferredoxin (Fd) <i>via</i> ferredoxin:NADP<sup>+</sup> reductase (FNR) and subsequently to generate reductant in the form of NADPH. <i>Cyclic photosynthetic electron transport</i>: electrons can flow from Fd to Pq (FdPq reaction). <i>Respiratory electron transfer</i>: includes two cytochrome oxidases (COX), two cytochrome-quinol oxidases (QOX), and two types of NADH dehydrogenases (NDH-1 and NDH-2). <i>Alternative sinks for reductant beyond CO<sub>2</sub> fixation</i>: reduced Fd can be used by the nitrogenase (Nif) and by Mehler reactions to reduce O<sub>2</sub>. Bidirectional hydrogenase (Hox) can reversibly produce H<sub>2</sub> using NAD(P)H as an electron donor, while the uptake hydrogenase (Hup) consumes H<sub>2</sub> using Fd as an electron acceptor. Protons transferred across the thylakoid membrane are used by the ATPase to drive ATP synthesis.</p>", "links"=>[], "tags"=>["electron", "reductant", "partitioning", "pathways"], "article_id"=>327843, "categories"=>["Biological Sciences"], "users"=>["Trang T. Vu", "Sergey M. Stolyar", "Grigoriy E. Pinchuk", "Eric A. Hill", "Leo A. Kucek", "Roslyn N. Brown", "Mary S. Lipton", "Andrei Osterman", "Jim K. Fredrickson", "Allan E. Konopka", "Alexander S. Beliaev", "Jennifer L. Reed"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002460.g001", "stats"=>{"downloads"=>2, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_representation_of_the_electron_transport_and_reductant_partitioning_pathways_in_Cyanothece_51142_/327843", "title"=>"Schematic representation of the electron transport and reductant partitioning pathways in <i>Cyanothece</i> 51142.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-05 02:10:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/657883"], "description"=>"<p>(A) Effects of deletions are compared to the cases where no reactions were deleted (red bar), or TPD were used as constraints (green bar). The values represent the average flux span across all reactions in central metabolism. Only deletions which lower the flux span by at least >1 mmol·g<sup>−1</sup> AFDW·h<sup>−1</sup> are presented. (B) Changes in flux spans for specific reactions catalyzed by ribulose bisphosphate carboxylase (RBC) and phosphoglucose isomerase (PGI) between simulations that (i) use TPD data as a constraint (green bars), (ii) delete single reactions (blue and purple bars), (iii) delete two reactions (yellow bar) or (iv) impose no additional constraints (red bars). Reaction abbreviations match those listed in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002460#pcbi.1002460.s005\" target=\"_blank\">Table S1</a>.</p>", "links"=>[], "tags"=>["deletions", "flux", "spans", "light-limited"], "article_id"=>328356, "categories"=>["Biological Sciences"], "users"=>["Trang T. Vu", "Sergey M. Stolyar", "Grigoriy E. Pinchuk", "Eric A. Hill", "Leo A. Kucek", "Roslyn N. Brown", "Mary S. Lipton", "Andrei Osterman", "Jim K. Fredrickson", "Allan E. Konopka", "Alexander S. Beliaev", "Jennifer L. Reed"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002460.g006", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effects_of_in_silico_reaction_deletions_on_flux_spans_under_light_limited_conditions_/328356", "title"=>"Effects of <i>in silico</i> reaction deletions on flux spans under light-limited conditions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-05 02:19:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/657797"], "description"=>"<p>The flux values (mmol·g<sup>−1</sup> AFDW·h<sup>−1</sup>) are those where the flux distribution best matches the transcriptome and proteome data (TPD) while also minimizing the magnitude of all fluxes in the network. The flux values in red and green represent ammonia-limited (AL) and light-limited (LL) conditions, respectively. Arrow colors indicate relative flux ratios between AL and LL conditions.</p>", "links"=>[], "tags"=>["chemostat", "flux", "distributions", "metabolism", "transcriptome", "proteome"], "article_id"=>328263, "categories"=>["Biological Sciences"], "users"=>["Trang T. Vu", "Sergey M. Stolyar", "Grigoriy E. Pinchuk", "Eric A. Hill", "Leo A. Kucek", "Roslyn N. Brown", "Mary S. Lipton", "Andrei Osterman", "Jim K. Fredrickson", "Allan E. Konopka", "Alexander S. Beliaev", "Jennifer L. Reed"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002460.g005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_chemostat_flux_distributions_in_central_metabolism_including_transcriptome_and_proteome_data_as_constraints_/328263", "title"=>"Predicted chemostat flux distributions in central metabolism including transcriptome and proteome data as constraints.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-05 02:17:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/337508", "https://ndownloader.figshare.com/files/337535", "https://ndownloader.figshare.com/files/337580", "https://ndownloader.figshare.com/files/337631", "https://ndownloader.figshare.com/files/337681", "https://ndownloader.figshare.com/files/337710", "https://ndownloader.figshare.com/files/337742", "https://ndownloader.figshare.com/files/337763", "https://ndownloader.figshare.com/files/337789", "https://ndownloader.figshare.com/files/337812", "https://ndownloader.figshare.com/files/337837", "https://ndownloader.figshare.com/files/337943", "https://ndownloader.figshare.com/files/337996"], "description"=>"<div><p>Genome-scale metabolic models have proven useful for answering fundamental questions about metabolic capabilities of a variety of microorganisms, as well as informing their metabolic engineering. However, only a few models are available for oxygenic photosynthetic microorganisms, particularly in cyanobacteria in which photosynthetic and respiratory electron transport chains (ETC) share components. We addressed the complexity of cyanobacterial ETC by developing a genome-scale model for the diazotrophic cyanobacterium, <em>Cyanothece</em> sp. ATCC 51142. The resulting metabolic reconstruction, <em>i</em>Cce806, consists of 806 genes associated with 667 metabolic reactions and includes a detailed representation of the ETC and a biomass equation based on experimental measurements. Both computational and experimental approaches were used to investigate light-driven metabolism in <em>Cyanothece</em> sp. ATCC 51142, with a particular focus on reductant production and partitioning within the ETC. The simulation results suggest that growth and metabolic flux distributions are substantially impacted by the relative amounts of light going into the individual photosystems. When growth is limited by the flux through photosystem I, terminal respiratory oxidases are predicted to be an important mechanism for removing excess reductant. Similarly, under photosystem II flux limitation, excess electron carriers must be removed <em>via</em> cyclic electron transport. Furthermore, <em>in silico</em> calculations were in good quantitative agreement with the measured growth rates whereas predictions of reaction usage were qualitatively consistent with protein and mRNA expression data, which we used to further improve the resolution of intracellular flux values.</p> </div>", "links"=>[], "tags"=>["genome-scale", "modeling", "light-driven", "reductant", "partitioning", "carbon", "fluxes", "diazotrophic", "unicellular", "cyanobacterium", "atcc", "51142"], "article_id"=>126701, "categories"=>["Biological Sciences"], "users"=>["Trang T. Vu", "Sergey M. Stolyar", "Grigoriy E. Pinchuk", "Eric A. Hill", "Leo A. Kucek", "Roslyn N. Brown", "Mary S. Lipton", "Andrei Osterman", "Jim K. Fredrickson", "Allan E. Konopka", "Alexander S. Beliaev", "Jennifer L. Reed"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002460.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002460.s002", "https://dx.doi.org/10.1371/journal.pcbi.1002460.s003", "https://dx.doi.org/10.1371/journal.pcbi.1002460.s004", "https://dx.doi.org/10.1371/journal.pcbi.1002460.s005", "https://dx.doi.org/10.1371/journal.pcbi.1002460.s006", "https://dx.doi.org/10.1371/journal.pcbi.1002460.s007", "https://dx.doi.org/10.1371/journal.pcbi.1002460.s008", "https://dx.doi.org/10.1371/journal.pcbi.1002460.s009", "https://dx.doi.org/10.1371/journal.pcbi.1002460.s010", "https://dx.doi.org/10.1371/journal.pcbi.1002460.s011", "https://dx.doi.org/10.1371/journal.pcbi.1002460.s012", "https://dx.doi.org/10.1371/journal.pcbi.1002460.s013"], "stats"=>{"downloads"=>68, "page_views"=>31, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Genome_Scale_Modeling_of_Light_Driven_Reductant_Partitioning_and_Carbon_Fluxes_in_Diazotrophic_Unicellular_Cyanobacterium_Cyanothece_sp_ATCC_51142/126701", "title"=>"Genome-Scale Modeling of Light-Driven Reductant Partitioning and Carbon Fluxes in Diazotrophic Unicellular Cyanobacterium <em>Cyanothece</em> sp. ATCC 51142", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-04-05 01:51:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/657604"], "description"=>"<p>(A) 2-D phenotypic phase plane (PhPP) displaying maximum growth rates for varying photon uptake rates. The PhPP has 3 distinct regions – in regions 1 and 3, flux through a single photosystem limit growth rates, whereas in region 2 flux increases through either photosystem will increase growth rate. (B) Pathway maps of electron transfer reactions in different PhPP regions. PhPP flux variability analysis was performed to see which flux is always required (red arrows), optional (green arrows), and blocked (blue arrows) across each of the three PhPP regions.</p>", "links"=>[], "tags"=>["varying", "photon", "uptake", "rates", "electron"], "article_id"=>328079, "categories"=>["Biological Sciences"], "users"=>["Trang T. Vu", "Sergey M. Stolyar", "Grigoriy E. Pinchuk", "Eric A. Hill", "Leo A. Kucek", "Roslyn N. Brown", "Mary S. Lipton", "Andrei Osterman", "Jim K. Fredrickson", "Allan E. Konopka", "Alexander S. Beliaev", "Jennifer L. Reed"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002460.g003", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_effects_of_varying_photon_uptake_rates_on_growth_and_electron_transport_pathways_/328079", "title"=>"Predicted effects of varying photon uptake rates on growth and electron transport pathways.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-05 02:14:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/657958"], "description"=>"<p>Flux variability analysis for model simulations in light-limited and ammonium-limited chemostat conditions.</p>", "links"=>[], "tags"=>["variability", "simulations", "light-limited", "ammonium-limited", "chemostat"], "article_id"=>328433, "categories"=>["Biological Sciences"], "users"=>["Trang T. Vu", "Sergey M. Stolyar", "Grigoriy E. Pinchuk", "Eric A. Hill", "Leo A. Kucek", "Roslyn N. Brown", "Mary S. Lipton", "Andrei Osterman", "Jim K. Fredrickson", "Allan E. Konopka", "Alexander S. Beliaev", "Jennifer L. Reed"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002460.t002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Flux_variability_analysis_for_model_simulations_in_light_limited_and_ammonium_limited_chemostat_conditions_/328433", "title"=>"Flux variability analysis for model simulations in light-limited and ammonium-limited chemostat conditions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-04-05 02:20:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/657708"], "description"=>"<p>Effect of nutrient limitation on biomass composition (normalized to ash-free dry weight).</p>", "links"=>[], "tags"=>["biomass", "ash-free"], "article_id"=>328183, "categories"=>["Biological Sciences"], "users"=>["Trang T. Vu", "Sergey M. Stolyar", "Grigoriy E. Pinchuk", "Eric A. Hill", "Leo A. Kucek", "Roslyn N. Brown", "Mary S. Lipton", "Andrei Osterman", "Jim K. Fredrickson", "Allan E. Konopka", "Alexander S. Beliaev", "Jennifer L. Reed"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002460.g004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_nutrient_limitation_on_biomass_composition_normalized_to_ash_free_dry_weight_/328183", "title"=>"Effect of nutrient limitation on biomass composition (normalized to ash-free dry weight).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-05 02:16:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/657490"], "description"=>"<p>(A) Effects of removing cyclic photosynthesis (<i>via</i> NDH-1, NDH-2, FdPq, G3PD_PQ, and SUCD_PQ) and alternative reductant sinks (H<sub>2</sub> production, COX, QOX, and Mehler reactions). (B) Effect of removing alternative reductant sinks but including all routes for cyclic photosynthesis. Shaded regions indicate that multiple flux values can achieve maximal growth rate. (C) All photosynthetic and respiratory electron flow routes operate, except H<sub>2</sub> production.</p>", "links"=>[], "tags"=>["electron", "pathways", "metabolism"], "article_id"=>327960, "categories"=>["Biological Sciences"], "users"=>["Trang T. Vu", "Sergey M. Stolyar", "Grigoriy E. Pinchuk", "Eric A. Hill", "Leo A. Kucek", "Roslyn N. Brown", "Mary S. Lipton", "Andrei Osterman", "Jim K. Fredrickson", "Allan E. Konopka", "Alexander S. Beliaev", "Jennifer L. Reed"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002460.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_electron_transport_pathways_on_growth_and_metabolism_of_Cyanothece_51142_/327960", "title"=>"Impact of electron transport pathways on growth and metabolism of <i>Cyanothece</i> 51142.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-05 02:12:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/658009"], "description"=>"*<p>Average and standard deviation of the instantaneously measured growth rate and photon uptake rates were calculated over the first 5 hours. (See <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002460#pcbi.1002460.s013\" target=\"_blank\">Text S1</a> for more detail about batch growth rate simulation).</p>**<p>For computational predictions of the growth rate for batches 6–10, the total photon uptake flux measurements at 630 nm and 680 nm was used to constrain the total photon uptake flux in the model (EX_photon PSI_e+EX_photon PSII_e).</p>#<p>Experimental photon uptake and growth rates from batches 1–5 were used to calculate ATP requirement parameters GAR and NGAR.</p>", "links"=>[], "tags"=>["rates", "simulation", "experimentally", "batch"], "article_id"=>328484, "categories"=>["Biological Sciences"], "users"=>["Trang T. Vu", "Sergey M. Stolyar", "Grigoriy E. Pinchuk", "Eric A. Hill", "Leo A. Kucek", "Roslyn N. Brown", "Mary S. Lipton", "Andrei Osterman", "Jim K. Fredrickson", "Allan E. Konopka", "Alexander S. Beliaev", "Jennifer L. Reed"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002460.t001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_growth_rates_predicted_by_simulation_model_to_those_experimentally_measured_in_batch_cultures_/328484", "title"=>"Comparison of growth rates predicted by simulation model to those experimentally measured in batch cultures.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-04-05 02:21:24"}

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Relative Metric

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