k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design
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{"title"=>"k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design", "type"=>"journal", "authors"=>[{"first_name"=>"Anupam", "last_name"=>"Chowdhury", "scopus_author_id"=>"55481179100"}, {"first_name"=>"Ali R.", "last_name"=>"Zomorrodi", "scopus_author_id"=>"35106394400"}, {"first_name"=>"Costas D.", "last_name"=>"Maranas", "scopus_author_id"=>"7005248717"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"372548698", "sgr"=>"84895756673", "issn"=>"15537358", "pmid"=>"24586136", "scopus"=>"2-s2.0-84895756673", "doi"=>"10.1371/journal.pcbi.1003487", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)"}, "id"=>"da32661c-f158-31ac-8519-3d7206efb48d", "abstract"=>"Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in E. coli and triacetic acid lactone (TAL) in S. cerevisiae revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations. Furthermore, k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometry-alone analysis. This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.", "link"=>"http://www.mendeley.com/research/koptforce-integrating-kinetics-flux-balance-analysis-strain-design-1", "reader_count"=>183, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>6, "Student > Doctoral Student"=>15, "Researcher"=>33, "Student > Ph. D. Student"=>60, "Student > Postgraduate"=>9, "Student > Master"=>29, "Other"=>5, "Student > Bachelor"=>16, "Lecturer"=>1, "Professor"=>6}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>6, "Student > Doctoral Student"=>15, "Researcher"=>33, "Student > Ph. D. Student"=>60, "Student > Postgraduate"=>9, "Student > Master"=>29, "Other"=>5, "Student > Bachelor"=>16, "Lecturer"=>1, "Professor"=>6}, "reader_count_by_subject_area"=>{"Engineering"=>35, "Unspecified"=>10, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>27, "Mathematics"=>2, "Agricultural and Biological Sciences"=>75, "Medicine and Dentistry"=>2, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Chemical Engineering"=>11, "Chemistry"=>2, "Computer Science"=>15, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>35}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Chemistry"=>{"Chemistry"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>75}, "Computer Science"=>{"Computer Science"=>15}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>27}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>10}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}, "Chemical Engineering"=>{"Chemical Engineering"=>11}}, "reader_count_by_country"=>{"United States"=>7, "United Kingdom"=>1, "Portugal"=>2, "Switzerland"=>1, "India"=>1, "Sweden"=>1, "Iran"=>1, "Belgium"=>1, "Mexico"=>1, "France"=>1, "Chile"=>2, "Australia"=>1, "Germany"=>2}, "group_count"=>15}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1392270"], "description"=>"<p>The values in brackets indicate the metabolic flux in mmol GDW<sup>−1</sup>hr<sup>−1</sup> per 100 mmol gDW<sup>−1</sup> hr<sup>−1</sup> glucose uptake.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Metabolic pathways", "Computational biology", "Metabolic networks", "systems biology", "Bioengineering", "Biological systems engineering", "Economics", "Operations research", "Mathematical optimization", "strategies", "optforce", "k-optforce", "overproduction", "l-serine"], "article_id"=>939565, "categories"=>["Biological Sciences", "Engineering", "Sociology"], "users"=>["Anupam Chowdhury", "Ali R. Zomorrodi", "Costas D. Maranas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003487.g007", "stats"=>{"downloads"=>1, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_intervention_strategies_predicted_by_A_regular_OptForce_and_B_k_OptForce_for_overproduction_of_L_serine_in_E_coli_/939565", "title"=>"Comparison of intervention strategies predicted by A. regular OptForce and B. k-OptForce for overproduction of L-serine in <i>E. coli</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 02:49:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392269"], "description"=>"<p>Values on top indicate reduced flux ranges (in mmol gDW<sup>−1</sup> hr<sup>−1</sup>), while values in the bottom indicate the flux ranges (in mmol gDW<sup>−1</sup> hr<sup>−1</sup>) without any kinetic information, for 100 mmol gDW<sup>−1</sup> hr<sup>−1</sup> of glucose uptake.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Metabolic pathways", "Computational biology", "Metabolic networks", "systems biology", "Bioengineering", "Biological systems engineering", "Economics", "Operations research", "Mathematical optimization", "flux", "ranges", "desired", "phenotype", "l-serine", "overproduction", "incorporation", "kinetic"], "article_id"=>939564, "categories"=>["Biological Sciences", "Engineering", "Sociology"], "users"=>["Anupam Chowdhury", "Ali R. Zomorrodi", "Costas D. Maranas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003487.g006", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reduction_in_the_flux_ranges_of_the_desired_phenotype_for_L_serine_overproduction_in_E_coli_after_the_incorporation_of_additional_kinetic_constraints_/939564", "title"=>"Reduction in the flux ranges of the desired phenotype for L-serine overproduction in <i>E. coli</i> after the incorporation of additional kinetic constraints.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 02:49:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392266"], "description"=>"<p>The outer problem maximizes the flux towards the desired chemical while the inner problem simulates the worst-case scenario by minimizing the product flux. Binary variables and propagate the effect of engineering modifications in <b><i>J<sup>kin</sup></i></b> while and do the same in <b><i>J<sup>stoic</sup></i></b>. The fluxes in <b><i>J<sup>kin</sup></i></b> remain unchanged in the inner problem.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Metabolic pathways", "Computational biology", "Metabolic networks", "systems biology", "Bioengineering", "Biological systems engineering", "Economics", "Operations research", "Mathematical optimization", "bilevel", "formulation"], "article_id"=>939561, "categories"=>["Biological Sciences", "Engineering", "Sociology"], "users"=>["Anupam Chowdhury", "Ali R. Zomorrodi", "Costas D. Maranas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003487.g003", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Single_step_bilevel_formulation_for_the_identification_of_FORCE_sets_/939561", "title"=>"Single-step bilevel formulation for the identification of FORCE sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 02:49:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392264"], "description"=>"<p>Note that some metabolites participate in only the stoichiometric part of the model (i.e., <b><i>I<sup>stoic</sup></i></b>) whereas others participate in both (i.e., <b><i>I<sup>kin</sup></i></b>). The flux of reaction <i>j</i> () in the kinetic part of the model (shaded region) is determined by the kinetic formalism, , metabolite concentrations and other kinetic parameters <i>p</i> while the ones in <b><i>J<sup>stoic</sup></i></b> by the mass conservation laws (non-shaded region).</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Metabolic pathways", "Computational biology", "Metabolic networks", "systems biology", "Bioengineering", "Biological systems engineering", "Economics", "Operations research", "Mathematical optimization", "diagram", "partition", "reactions", "ones", "kinetic", "linked", "stoichiometry"], "article_id"=>939559, "categories"=>["Biological Sciences", "Engineering", "Sociology"], "users"=>["Anupam Chowdhury", "Ali R. Zomorrodi", "Costas D. Maranas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003487.g001", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_diagram_showing_the_partition_of_reactions_into_the_ones_with_kinetic_information_J_kin_and_those_linked_by_only_stoichiometry_J_stoic_/939559", "title"=>"Schematic diagram showing the partition of reactions into the ones with kinetic information <i>J<sup>kin</sup></i> and those linked by only stoichiometry <i>J<sup>stoic</sup></i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 02:49:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392265"], "description"=>"<p>(A) The reference phenotype characterization identifies the minimum and maximum flux limits of all reactions consistent with the steady-state reference flux of the reactions in <b><i>J<sup>kin</sup></i></b> and the maximum biomass production . (B) The overproducing phenotype is identified by calculating the minimum and maximum flux limits of all reactions consistent with the kinetic expressions for <b><i>J<sup>kin</sup></i></b>, the minimum target production of desired chemical and biomass . The metabolite concentrations and enzyme activities are allowed to vary within a pre-specified range of their reference values.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Metabolic pathways", "Computational biology", "Metabolic networks", "systems biology", "Bioengineering", "Biological systems engineering", "Economics", "Operations research", "Mathematical optimization", "characterization", "phenotype", "overproduction"], "article_id"=>939560, "categories"=>["Biological Sciences", "Engineering", "Sociology"], "users"=>["Anupam Chowdhury", "Ali R. Zomorrodi", "Costas D. Maranas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003487.g002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Optimization_structure_for_the_characterization_of_the_reference_and_the_phenotype_consistent_with_overproduction_of_target_chemical_/939560", "title"=>"Optimization structure for the characterization of the reference and the phenotype consistent with overproduction of target chemical.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 02:49:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392274"], "description"=>"<p>The graph above shows the variation in the minimum number of interventions required in <b><i>J<sup>kin</sup></i></b> to overproduce TAL with increase in ε. The corresponding table below shows of the average deviation (row 1) and the individual deviation in concentration of metabolites in <b><i>I<sup>kin</sup></i></b> for different values of ε.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Metabolic pathways", "Computational biology", "Metabolic networks", "systems biology", "Bioengineering", "Biological systems engineering", "Economics", "Operations research", "Mathematical optimization", "interventions", "tal", "overproduction"], "article_id"=>939569, "categories"=>["Biological Sciences", "Engineering", "Sociology"], "users"=>["Anupam Chowdhury", "Ali R. Zomorrodi", "Costas D. Maranas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003487.g011", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_interventions_in_J_kin_for_TAL_overproduction_in_S_cerevisiae_as_a_function_of_penalty_factor_/939569", "title"=>"Predicted interventions in <i>J<sup>kin</sup></i> for TAL overproduction in <i>S. cerevisiae</i> as a function of penalty factor (ε).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 02:49:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392272"], "description"=>"<p>The values in brackets indicate the metabolic flux in mmol gDW<sup>−1</sup> hr<sup>−1</sup> per 100 mmol gDW<sup>−1</sup> hr<sup>−1</sup> glucose uptake.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Metabolic pathways", "Computational biology", "Metabolic networks", "systems biology", "Bioengineering", "Biological systems engineering", "Economics", "Operations research", "Mathematical optimization", "strategies", "optforce", "k-optforce", "overproduction", "tal"], "article_id"=>939567, "categories"=>["Biological Sciences", "Engineering", "Sociology"], "users"=>["Anupam Chowdhury", "Ali R. Zomorrodi", "Costas D. Maranas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003487.g009", "stats"=>{"downloads"=>2, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_intervention_strategies_predicted_by_A_regular_OptForce_and_B_k_OptForce_for_overproduction_of_TAL_in_S_cerevisiae_/939567", "title"=>"Comparison of intervention strategies predicted by A. regular OptForce and B. k-OptForce for overproduction of TAL in <i>S. cerevisiae</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 02:49:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392273"], "description"=>"<p>The first value indicates the overproducing strain concentration, while the second value refers to the reference strain concentration.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Metabolic pathways", "Computational biology", "Metabolic networks", "systems biology", "Bioengineering", "Biological systems engineering", "Economics", "Operations research", "Mathematical optimization", "concentrations", "metabolites", "engineered", "overproduction"], "article_id"=>939568, "categories"=>["Biological Sciences", "Engineering", "Sociology"], "users"=>["Anupam Chowdhury", "Ali R. Zomorrodi", "Costas D. Maranas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003487.g010", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_concentrations_in_mM_of_metabolites_in_I_kin_for_the_engineered_and_reference_strain_of_S_cerevisiae_for_overproduction_of_TAL_/939568", "title"=>"Comparison of the concentrations (in mM) of metabolites in <i>I<sup>kin</sup></i> for the engineered and reference strain of <i>S. cerevisiae</i> for overproduction of TAL.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 02:49:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392271"], "description"=>"<p>Pathway for TAL production in <i>S. cerevisiae</i>.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Metabolic pathways", "Computational biology", "Metabolic networks", "systems biology", "Bioengineering", "Biological systems engineering", "Economics", "Operations research", "Mathematical optimization", "tal"], "article_id"=>939566, "categories"=>["Biological Sciences", "Engineering", "Sociology"], "users"=>["Anupam Chowdhury", "Ali R. Zomorrodi", "Costas D. Maranas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003487.g008", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pathway_for_TAL_production_in_S_cerevisiae_/939566", "title"=>"Pathway for TAL production in <i>S. cerevisiae</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 02:49:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392268"], "description"=>"<p>Pathway for L-serine production in <i>E. coli</i>.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Metabolic pathways", "Computational biology", "Metabolic networks", "systems biology", "Bioengineering", "Biological systems engineering", "Economics", "Operations research", "Mathematical optimization", "l-serine"], "article_id"=>939563, "categories"=>["Biological Sciences", "Engineering", "Sociology"], "users"=>["Anupam Chowdhury", "Ali R. Zomorrodi", "Costas D. Maranas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003487.g005", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pathway_for_L_serine_production_in_E_coli_/939563", "title"=>"Pathway for L-serine production in <i>E. coli</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 02:49:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392267"], "description"=>"<p>(A) The first step of the formulation identifies the minimum number of interventions ( and ) in <b><i>J<sup>kin</sup></i></b> required to meet the desired levels of overproduction of target chemical. (B) The second step identifies the additional interventions ( and ) in <b><i>J<sup>stoic</sup></i></b> that would guarantee the desired yield in the worst case scenario.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Metabolic pathways", "Computational biology", "Metabolic networks", "systems biology", "Bioengineering", "Biological systems engineering", "Economics", "Operations research", "Mathematical optimization", "formulation"], "article_id"=>939562, "categories"=>["Biological Sciences", "Engineering", "Sociology"], "users"=>["Anupam Chowdhury", "Ali R. Zomorrodi", "Costas D. Maranas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003487.g004", "stats"=>{"downloads"=>4, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Two_step_formulation_for_the_identification_of_the_FORCE_sets_/939562", "title"=>"Two-step formulation for the identification of the FORCE sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 02:49:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392276"], "description"=>"<div><p>Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in <i>E. coli</i> and triacetic acid lactone (TAL) in <i>S. cerevisiae</i> revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations. Furthermore, k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometry-alone analysis. This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.</p></div>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Metabolic pathways", "Computational biology", "Metabolic networks", "systems biology", "Bioengineering", "Biological systems engineering", "Economics", "Operations research", "Mathematical optimization", "integrating", "kinetics", "flux"], "article_id"=>939571, "categories"=>["Biological Sciences", "Engineering", "Sociology"], "users"=>["Anupam Chowdhury", "Ali R. Zomorrodi", "Costas D. Maranas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003487", "stats"=>{"downloads"=>19, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_k_OptForce_Integrating_Kinetics_with_Flux_Balance_Analysis_for_Strain_Design_/939571", "title"=>"k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-20 02:49:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392275"], "description"=>"<p>The values in brackets indicate the metabolic flux in mmol gDW<sup>−1</sup>hr<sup>−1</sup> per 100 mmol gDW<sup>−1</sup> hr<sup>−1</sup> glucose uptake.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Metabolic pathways", "Computational biology", "Metabolic networks", "systems biology", "Bioengineering", "Biological systems engineering", "Economics", "Operations research", "Mathematical optimization", "interventions", "k-optforce", "overproduction", "tal", "heterologous", "nadph-dependent", "pdh"], "article_id"=>939570, "categories"=>["Biological Sciences", "Engineering", "Sociology"], "users"=>["Anupam Chowdhury", "Ali R. Zomorrodi", "Costas D. Maranas"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003487.g012", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Metabolic_interventions_predicted_by_k_OptForce_for_overproduction_of_TAL_in_S_cerevisiae_on_heterologous_expression_of_nadph_dependent_PDH_complex_from_E_coli_/939570", "title"=>"Metabolic interventions predicted by k-OptForce for overproduction of TAL in <i>S. cerevisiae</i> on heterologous expression of nadph-dependent PDH complex from <i>E. coli</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 02:49:13"}

PMC Usage Stats | Further Information

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Relative Metric

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