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• {"link"=>{"url"=>"http://www.citeulike.org/user/jdreyf/article/10318440"}, "post_time"=>"2013-02-14 18:58:41", "tag"=>"eng", "linkout"=>{"type"=>"DOI", "url"=>"http://dx.doi.org/10.1371/journal.pone.0024162"}, "username"=>"jdreyf", "article_id"=>"10318440"}
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{"title"=>"Large-Scale Bi-Level strain design approaches and Mixed-Integer programming solution techniques", "type"=>"journal", "authors"=>[{"first_name"=>"Joonhoon", "last_name"=>"Kim", "scopus_author_id"=>"36145139000"}, {"first_name"=>"Jennifer L.", "last_name"=>"Reed", "scopus_author_id"=>"56352741300"}, {"first_name"=>"Christos T.", "last_name"=>"Maravelias", "scopus_author_id"=>"7004263098"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"80052573483", "doi"=>"10.1371/journal.pone.0024162", "pui"=>"362523735", "pmid"=>"21949695", "scopus"=>"2-s2.0-80052573483", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)"}, "id"=>"3293bd8d-a666-3395-8029-3b77f0dca88c", "abstract"=>"The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.", "link"=>"http://www.mendeley.com/research/largescale-bilevel-strain-design-approaches-mixedinteger-programming-solution-techniques", "reader_count"=>106, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Student > Doctoral Student"=>5, "Researcher"=>23, "Student > Ph. D. Student"=>28, "Student > Postgraduate"=>8, "Student > Master"=>25, "Other"=>1, "Student > Bachelor"=>4, "Lecturer"=>1, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Student > Doctoral Student"=>5, "Researcher"=>23, "Student > Ph. D. Student"=>28, "Student > Postgraduate"=>8, "Student > Master"=>25, "Other"=>1, "Student > Bachelor"=>4, "Lecturer"=>1, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Engineering"=>24, "Biochemistry, Genetics and Molecular Biology"=>13, "Materials Science"=>1, "Agricultural and Biological Sciences"=>40, "Sports and Recreations"=>1, "Chemical Engineering"=>4, "Social Sciences"=>1, "Computer Science"=>14, "Immunology and Microbiology"=>1, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>24}, "Materials Science"=>{"Materials Science"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>40}, "Computer Science"=>{"Computer Science"=>14}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>13}, "Unspecified"=>{"Unspecified"=>6}, "Chemical Engineering"=>{"Chemical Engineering"=>4}}, "reader_count_by_country"=>{"Canada"=>1, "Sweden"=>1, "United States"=>3, "Denmark"=>1, "Mexico"=>1, "United Kingdom"=>1, "France"=>1, "Portugal"=>2}, "group_count"=>11}

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• http://doi.org/10.1142/S0219720013430063
• http://doi.org/10.1016/j.meteno.2016.05.002
• http://doi.org/10.1039/C7MB00204A
• http://doi.org/10.1007/s11274-020-02946-8
• http://doi.org/10.1016/j.ymben.2012.09.005
• http://doi.org/10.1002/bit.25205
• http://doi.org/10.1016/j.ymben.2016.01.007
• http://doi.org/10.3390/metabo8020033
• http://doi.org/10.1371/journal.pone.0034670
• http://doi.org/10.1016/j.meteno.2014.06.001
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• http://doi.org/10.1007/s00253-012-4543-9
• http://doi.org/10.1016/j.compbiolchem.2016.02.014
• http://doi.org/10.1128/MMBR.00014-15
• http://doi.org/10.1109/TCBB.2016.2576456
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• http://doi.org/10.1186/1752-0509-6-127
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• http://doi.org/10.1016/j.csbj.2015.05.004
• http://doi.org/10.1016/j.synbio.2017.11.002
• http://doi.org/10.1186/gb-2012-13-12-r116
• http://doi.org/10.3389/fphys.2015.00413
• http://doi.org/10.1016/j.compchemeng.2014.06.007
• http://doi.org/10.1016/j.meteno.2015.04.001
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• http://doi.org/10.1093/bib/bbx053
• http://doi.org/10.1371/journal.pcbi.1003487
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• http://doi.org/10.1016/j.ymben.2015.10.005
• http://doi.org/10.1093/bioinformatics/bts255
• http://doi.org/10.1186/s12859-018-2352-6
• http://doi.org/10.1016/j.cbpa.2015.06.026
• http://doi.org/10.3390/pr8060643
• http://doi.org/10.1002/biot.201200316
• http://doi.org/10.1371/journal.pcbi.1008110
• http://doi.org/10.1016/j.copbio.2014.12.019
• http://doi.org/10.1016/j.copbio.2012.03.009
• http://doi.org/10.1186/gb-2012-13-9-r78
• http://doi.org/10.1093/bioinformatics/btt672
• http://doi.org/10.3389/fbioe.2014.00076

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• {"month"=>"8", "year"=>"2020", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"11"}
• {"month"=>"9", "year"=>"2020", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"5"}
• {"month"=>"10", "year"=>"2020", "pdf_views"=>"28", "xml_views"=>"0", "html_views"=>"39"}
• {"month"=>"11", "year"=>"2020", "pdf_views"=>"19", "xml_views"=>"1", "html_views"=>"14"}
• {"month"=>"12", "year"=>"2020", "pdf_views"=>"25", "xml_views"=>"0", "html_views"=>"9"}
• {"month"=>"1", "year"=>"2021", "pdf_views"=>"15", "xml_views"=>"0", "html_views"=>"10"}

Figshare

• {"files"=>["https://ndownloader.figshare.com/files/372040", "https://ndownloader.figshare.com/files/372140", "https://ndownloader.figshare.com/files/372196", "https://ndownloader.figshare.com/files/372246", "https://ndownloader.figshare.com/files/372274", "https://ndownloader.figshare.com/files/372378", "https://ndownloader.figshare.com/files/372400"], "description"=>"<div><p>The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.</p> </div>", "links"=>[], "tags"=>["large-scale", "bi-level", "approaches", "mixed-integer", "techniques"], "article_id"=>133484, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Joonhoon Kim", "Jennifer L. Reed", "Christos T. Maravelias"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024162.s001", "https://dx.doi.org/10.1371/journal.pone.0024162.s002", "https://dx.doi.org/10.1371/journal.pone.0024162.s003", "https://dx.doi.org/10.1371/journal.pone.0024162.s004", "https://dx.doi.org/10.1371/journal.pone.0024162.s005", "https://dx.doi.org/10.1371/journal.pone.0024162.s006", "https://dx.doi.org/10.1371/journal.pone.0024162.s007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Large_Scale_Bi_Level_Strain_Design_Approaches_and_Mixed_Integer_Programming_Solution_Techniques/133484", "title"=>"Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-09-09 00:58:04"}
• {"files"=>["https://ndownloader.figshare.com/files/736446"], "description"=>"<p>(A) Simplified representation of the existing OptStrain procedure and an illustrative example. Step 1 adds a minimum number of reactions from a universal database that yields the maximal increase in theoretical maximum production (TMP). Step 2 identifies reaction deletions in the augmented network identified in Step 1 that couple biomass and biochemical production. (B) SimOptStrain with simultaneous gene deletion and non-native reaction addition, and illustrative examples. Solution s1 shows an example of reaction additions, which do not increase the TMP, that improve biochemical production at the maximum growth rate when combined with gene deletions. Solution s2 is an example of reaction additions that yield a suboptimal increase in the TMP, while solution s3 is a case where the number of added reactions is not necessarily the minimum. Solutions s1, s2, and s3 could only be found using SimOptStrain.</p>", "links"=>[], "tags"=>["approaches", "considering", "additions"], "article_id"=>406798, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Joonhoon Kim", "Jennifer L. Reed", "Christos T. Maravelias"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024162.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Bi_level_approaches_considering_additions_and_deletions_/406798", "title"=>"Bi-level approaches considering additions and deletions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-09 01:53:18"}
• {"files"=>["https://ndownloader.figshare.com/files/736533"], "description"=>"<p>The MOMA inner problem, a convex quadratic program, is converted to its optimality conditions using strong duality. The resulting BiMOMA problem is a single level mixed-integer quadratically constrained program.</p>", "links"=>[], "tags"=>["mixed-integer", "quadratic", "bi-level"], "article_id"=>406891, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Joonhoon Kim", "Jennifer L. Reed", "Christos T. Maravelias"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024162.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_BiMOMA_8211_a_direct_mixed_integer_programming_approach_for_quadratic_bi_level_strain_design_/406891", "title"=>"BiMOMA – a direct mixed-integer programming approach for quadratic bi-level strain design.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-09 01:54:51"}
• {"files"=>["https://ndownloader.figshare.com/files/736583"], "description"=>"<p>Maximum (downward triangle) and minimum (upward triangle) of observed dual variable values for each reaction sorted by the standard deviation. The values of dual variables were obtained from 1,000,000 samples of 10 gene knockouts in glucose anaerobic condition using the <i>i</i>AF1260 metabolic model of <i>E. coli</i>.</p>", "links"=>[], "tags"=>["dual", "variables", "removals", "lp"], "article_id"=>406937, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Joonhoon Kim", "Jennifer L. Reed", "Christos T. Maravelias"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024162.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Analysis_of_dual_variables_for_reaction_removals_using_dual_LP_of_FBA_/406937", "title"=>"Analysis of dual variables for reaction removals using dual LP of FBA.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-09 01:55:37"}
• {"files"=>["https://ndownloader.figshare.com/files/736653"], "description"=>"<p>(<b>A</b>) Predicted acetate production yields in glucose anaerobic conditions for <i>E. coli</i> strains designed using our MIP techniques or a local search method. (<b>B</b>) Cumulative CPU times for our MIP techniques (○) and a local search method (□). For both panels, cases using α = 0% TMP (light blue) or 0.5% TMP (blue) were solved to optimality and α = 0.005% TMP (dark blue) was solved with a node limit of 10⁴ (TMP = 2.56 mol acetate produced/mol glucose consumed).</p>", "links"=>[], "tags"=>["genetics and genomics", "microbiology", "Computational biology"], "article_id"=>407005, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Joonhoon Kim", "Jennifer L. Reed", "Christos T. Maravelias"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024162.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_different_search_methods_/407005", "title"=>"Performance of different search methods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-09 01:56:45"}
• {"files"=>["https://ndownloader.figshare.com/files/736716"], "description"=>"<p>Different colors indicate OptKnock (light grey), OptGene (grey), and OptORF (dark grey). The numbers on the x-axis correspond to the number of reaction deletions identified (or maximum allowed if no strategy was found) by OptKnock and OptGene <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024162#pone.0024162-Feist2\" target=\"_blank\">[30]</a>, or gene deletions (equivalent reaction deletions listed in parentheses) identified by OptORF (this study). The product yields for OptKnock and OptGene were taken from an earlier study <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024162#pone.0024162-Feist2\" target=\"_blank\">[30]</a> and re-calculated based on TMP values without a minimum growth requirement. A missing bar indicates that no strategy was previously found.</p>", "links"=>[], "tags"=>["yields", "strains", "designed"], "article_id"=>407071, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Joonhoon Kim", "Jennifer L. Reed", "Christos T. Maravelias"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024162.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Product_yields_for_E_coli_strains_designed_to_generate_different_products_/407071", "title"=>"Product yields for <i>E. coli</i> strains designed to generate different products.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-09 01:57:51"}
• {"files"=>["https://ndownloader.figshare.com/files/736786"], "description"=>"<p>The top numbers are for wild-type and the bottom numbers are for a predicted succinate producing strain (<i>ΔsdhC Δgnd ΔglyA</i>+EC 1.2.1.52+EC 2.1.3.1 reactions). (A) Metabolic pathways producing or consuming NADPH are shown. The numbers are percentages of the total NADPH produced or consumed, where 100% is 15.7 mmol gDW⁻¹ h⁻¹ for wild-type (first line) and 7.3 mmol gDW⁻¹ h⁻¹ for succinate producing strain (second line). gDW stands for gram dry weight. Abbreviations of metabolites: Glu, glutamate; Gln, glutamine; Ile, isoleucine; Leu, leucine; Lys, lysine; Thr, threonine; Val, valine. (B) Metabolic fluxes and genes associated with each reaction in the central metabolic networks are shown. Blue arrows indicate removed native <i>E. coli</i> reactions, and red arrows indicate added non-native reactions. The numbers are relative fluxes normalized with respect to the total glucose uptake rate (100% is 10 mmol glucose gDW⁻¹ h⁻¹). Abbreviations of metabolites (‘_ext’ indicates extracellular): AC, acetate; ACCOA, acetyl-CoA; ACTP, acetyl phosphate; AKG, 2-oxoglutarate; CIT, citrate; FUM, fumarate; G6P, glucose 6-phosphate; GLC, glucose; ICIT, isocitrate; MAL, malate; MALCOA, malonyl-CoA; OAA, oxaloacetate; PEP, phosphoenolpyruvate; PYR, pyruvate; SUCC, succinate; SUCCOA, succinyl-CoA.</p>", "links"=>[], "tags"=>["involving", "nadph"], "article_id"=>407140, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Joonhoon Kim", "Jennifer L. Reed", "Christos T. Maravelias"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024162.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fluxes_involving_NADPH_production_consumption_and_central_metabolism_/407140", "title"=>"Fluxes involving NADPH production/consumption and central metabolism.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-09 01:59:00"}
• {"files"=>["https://ndownloader.figshare.com/files/736846"], "description"=>"<p>(A) Pyruvate and (B) Glutamate. The best BiMOMA strategies (○) were identified for k = 1 to 5 using a penalty of 0.5% TMP, and were combined with a local search (□) with search sizes of 2 or 3. BiMOMA+local search size of 2 starts from the best BiMOMA solutions for k = 2 and 3; and BiMOMA+local search size of 3 starts from the best BiMOMA solutions for k = 3, 4, and 5. A sequential search was also performed, which is a local search with search size of 1 starting from the best k = 1 solution.</p>", "links"=>[], "tags"=>["yields", "glucose", "aerobic", "conditions", "strains", "designed"], "article_id"=>407194, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Joonhoon Kim", "Jennifer L. Reed", "Christos T. Maravelias"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024162.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Improvements_in_product_yields_in_glucose_aerobic_conditions_for_E_coli_strains_designed_using_BiMOMA_/407194", "title"=>"Improvements in product yields in glucose aerobic conditions for <i>E. coli</i> strains designed using BiMOMA.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-09 01:59:54"}
• {"files"=>["https://ndownloader.figshare.com/files/736936"], "description"=>"a<p>‘Changes’ refer to the number of genes which are newly introduced in the solution with k deletions or removed from the solution with k–1 deletions.</p>b<p>Yield is reported as % of the TMP for wildtype strain with a maximum glucose uptake rate of 10 mmol gDW⁻¹ h⁻¹ (2.56 mol acetate produced/mol glucose consumed). gDW stands for gram dry weight.</p>", "links"=>[], "tags"=>["deletion", "strategies", "optorf", "mip", "techniques", "acetate", "glucose", "anaerobic"], "article_id"=>407279, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Joonhoon Kim", "Jennifer L. Reed", "Christos T. Maravelias"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024162.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Best_gene_deletion_strategies_identified_by_OptORF_using_our_MIP_techniques_for_acetate_production_under_glucose_anaerobic_condition_/407279", "title"=>"Best gene deletion strategies identified by OptORF using our MIP techniques for acetate production under glucose anaerobic condition.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-09-09 02:01:19"}
• {"files"=>["https://ndownloader.figshare.com/files/736963"], "description"=>"a<p>Enzyme Commission number.</p><p>3.1.3.21 sn-Glycerol 3-phosphate+H₂O< = >Glycerol+Orthophosphate.</p><p>2.7.1.142 sn-Glycerol 3-phosphate+D-Glucose< = >Glycerol+D-Glucose 6-phosphate.</p><p>2.1.3.1 Malonyl-CoA+Pyruvate< = >Acetyl-CoA+Oxaloacetate.</p>b<p>Theoretical maximum production is reported as mol glycerol produced/mol glucose consumed for each strain with non-native reaction additions, but without gene deletions. A maximum glucose uptake rate of 10 mmol gDW⁻¹ h⁻¹ and a maximum oxygen uptake rate of 18.5 mmol gDW⁻¹ h⁻¹ were used.</p>c<p>Yield is reported as % of the TMP for each strain after reactions are added.</p>d<p>Best strategies without addition of non-native reactions (no strategy was found for k = 5 and k′ = 0 because any small production increases (over the k = 3 strategy) were negated by the penalty α = 10⁻⁶).</p>", "links"=>[], "tags"=>["deletion", "strategies", "simoptstrain", "glycerol", "glucose", "aerobic"], "article_id"=>407313, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Joonhoon Kim", "Jennifer L. Reed", "Christos T. Maravelias"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024162.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_deletion_and_reaction_addition_strategies_identified_by_SimOptStrain_for_glycerol_production_under_glucose_aerobic_condition_/407313", "title"=>"Gene deletion and reaction addition strategies identified by SimOptStrain for glycerol production under glucose aerobic condition.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-09-09 02:01:53"}
• {"files"=>["https://ndownloader.figshare.com/files/736992"], "description"=>"a<p>Enzyme Commission number.</p><p>1.2.1.52 2-Oxoglutarate+CoA+NADP⁺< = >Succinyl-CoA+CO₂+NADPH.</p><p>2.1.3.1 Malonyl-CoA+Pyruvate< = >Acetyl-CoA+Oxaloacetate.</p><p>1.2.1.51 Pyruvate+CoA+NADP⁺< = >Acetyl-CoA+CO₂+NADPH.</p><p>1.4.1.20 L-Phenylalanine+H₂O+NAD⁺< = >Phenylpyruvate+NH₃+NADH.</p><p>1.4.1.9 L-Valine+H₂O+NAD⁺< = >3-Methyl-2-oxobutanoate+NH₃+NADH.</p>b<p>Theoretical maximum production (TMP) is reported as mol succinate produced/mol glucose consumed for each strain with non-native reaction additions, but without gene deletions. A maximum glucose uptake rate of 10 mmol gDW⁻¹ h⁻¹ and a maximum oxygen uptake rate of 18.5 mmol gDW⁻¹ h⁻¹ were used.</p>c<p>Yield is reported as % of the TMP for each strain after reactions are added.</p>d<p>Best strategies without addition of non-native reactions.</p>", "links"=>[], "tags"=>["deletion", "strategies", "simoptstrain", "succinate", "glucose", "aerobic"], "article_id"=>407346, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Joonhoon Kim", "Jennifer L. Reed", "Christos T. Maravelias"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024162.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_deletion_and_reaction_addition_strategies_identified_by_SimOptStrain_for_succinate_production_under_glucose_aerobic_condition_/407346", "title"=>"Gene deletion and reaction addition strategies identified by SimOptStrain for succinate production under glucose aerobic condition.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-09-09 02:02:26"}
• {"files"=>["https://ndownloader.figshare.com/files/737036"], "description"=>"a<p>‘Changes’ refer to the number of genes which are newly introduced in the solution with k deletions or removed from the solution with k–1 deletions.</p>b<p>Yield is reported as % of the TMP (1.15 mol glutamate produced/mol glucose consumed) for wildtype strain with a maximum glucose uptake rate of 10 mmol gDW⁻¹ h⁻¹ and a maximum oxygen uptake rate of 18.5 mmol gDW⁻¹ h⁻¹.</p>", "links"=>[], "tags"=>["deletion", "strategies", "bimoma", "glutamate", "glucose", "aerobic"], "article_id"=>407384, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Joonhoon Kim", "Jennifer L. Reed", "Christos T. Maravelias"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024162.t005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Top_gene_deletion_strategies_identified_by_BiMOMA_k_lt_5_or_BiMOMA_Local_Search_k_gt_5_for_glutamate_production_under_glucose_aerobic_condition_/407384", "title"=>"Top gene deletion strategies identified by BiMOMA (k<5) or BiMOMA+Local Search (k>5) for glutamate production under glucose aerobic condition.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-09-09 02:03:04"}
• {"files"=>["https://ndownloader.figshare.com/files/737068"], "description"=>"a<p>‘Changes’ refer to the number of genes which are newly introduced in the solution with k deletions or removed from the solution with k–1 deletions.</p>b<p>Yield is reported as % of the TMP (2 mol pyruvate produced/mol glucose consumed) for wildtype strain with a maximum glucose uptake rate of 10 mmol gDW⁻¹ h⁻¹ and a maximum oxygen uptake rate of 18.5 mmol gDW⁻¹ h⁻¹.</p>", "links"=>[], "tags"=>["deletion", "strategies", "bimoma", "pyruvate", "glucose", "aerobic"], "article_id"=>407420, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Joonhoon Kim", "Jennifer L. Reed", "Christos T. Maravelias"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024162.t004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Top_gene_deletion_strategies_identified_by_BiMOMA_k_lt_5_or_BiMOMA_Local_Search_k_gt_5_for_pyruvate_production_under_glucose_aerobic_condition_/407420", "title"=>"Top gene deletion strategies identified by BiMOMA (k<5) or BiMOMA+Local Search (k>5) for pyruvate production under glucose aerobic condition.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-09-09 02:03:40"}

PMC Usage Stats | Further Information

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• {"unique-ip"=>"7", "full-text"=>"8", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
• {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
• {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
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• {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
• {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}
• {"unique-ip"=>"13", "full-text"=>"11", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"10"}
• {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"11"}
• {"unique-ip"=>"5", "full-text"=>"8", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"12"}

Relative Metric

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F1000Prime