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{"title"=>"Enumeration of Smallest Intervention Strategies in Genome-Scale Metabolic Networks", "type"=>"journal", "authors"=>[{"first_name"=>"Axel", "last_name"=>"von Kamp", "scopus_author_id"=>"8937693500"}, {"first_name"=>"Steffen", "last_name"=>"Klamt", "scopus_author_id"=>"6603425714"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84896731390", "doi"=>"10.1371/journal.pcbi.1003378", "pui"=>"372348798", "pmid"=>"24391481", "scopus"=>"2-s2.0-84896731390", "issn"=>"1553734X", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)"}, "id"=>"2a91269f-4f17-3dcb-b7d9-26d57d4d0c12", "abstract"=>"One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal) provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs) which itself is impractical in genome-scale networks. We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions) in genome-scale metabolic network models. For this we combine two approaches, namely (i) the mapping of MCSs to EMs in a dual network, and (ii) a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine) by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth) than reported previously. The strength of the presented approach is that smallest intervention strategies can be quickly calculated and screened with neither network size nor the number of required interventions posing major challenges.", "link"=>"http://www.mendeley.com/research/enumeration-smallest-intervention-strategies-genomescale-metabolic-networks", "reader_count"=>90, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>19, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>35, "Student > Master"=>17, "Other"=>2, "Student > Bachelor"=>6, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>19, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>35, "Student > Master"=>17, "Other"=>2, "Student > Bachelor"=>6, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>12, "Unspecified"=>4, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>12, "Mathematics"=>4, "Agricultural and Biological Sciences"=>38, "Sports and Recreations"=>1, "Physics and Astronomy"=>1, "Chemical Engineering"=>5, "Chemistry"=>1, "Computer Science"=>11}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>12}, "Chemistry"=>{"Chemistry"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>38}, "Computer Science"=>{"Computer Science"=>11}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>12}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>1}, "Chemical Engineering"=>{"Chemical Engineering"=>5}}, "reader_count_by_country"=>{"Republic of Singapore"=>1, "Canada"=>1, "Iran"=>1, "Brazil"=>2, "Mexico"=>1, "United Kingdom"=>1, "France"=>2, "Portugal"=>2, "India"=>1}, "group_count"=>8}

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• {"month"=>"8", "year"=>"2020", "pdf_views"=>"10", "xml_views"=>"1", "html_views"=>"14"}
• {"month"=>"9", "year"=>"2020", "pdf_views"=>"15", "xml_views"=>"0", "html_views"=>"13"}
• {"month"=>"10", "year"=>"2020", "pdf_views"=>"11", "xml_views"=>"1", "html_views"=>"17"}
• {"month"=>"11", "year"=>"2020", "pdf_views"=>"10", "xml_views"=>"0", "html_views"=>"9"}
• {"month"=>"12", "year"=>"2020", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"11"}
• {"month"=>"1", "year"=>"2021", "pdf_views"=>"21", "xml_views"=>"0", "html_views"=>"13"}
• {"month"=>"2", "year"=>"2021", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"14"}

Figshare

• {"files"=>["https://ndownloader.figshare.com/files/1336382"], "description"=>"<p>The graphics indicates the found cMCSs requiring only three knockouts (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003378#pcbi-1003378-t003\" target=\"_blank\">Table 3</a>). In total, 8 cMCSs were found for scenario 1 and two cMCSs for scenarios 3 and 4 (both being identical for the two scenarios). All these cMCSs cut the reaction with the red cross and one of the two reactions with a blue cross. In addition, for scenario 1, one of the dark green cuts has to be made whereas the two cMCSs for scenario 3 and 4 require the light green cut (see also explanations in the text).</p>", "links"=>[], "tags"=>["mcss", "knockouts", "coupled", "ethanol", "biomass", "anaerobic"], "article_id"=>892418, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Axel von Kamp", "Steffen Klamt"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003378.g001", "stats"=>{"downloads"=>1, "page_views"=>29, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Constrained_MCSs_with_three_reaction_knockouts_leading_to_coupled_ethanol_and_biomass_formation_by_E_coli_under_anaerobic_growth_on_glucose_/892418", "title"=>"Constrained MCSs with three reaction knockouts leading to coupled ethanol and biomass formation by <i>E. coli</i> under anaerobic growth on glucose.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-02 02:43:29"}
• {"files"=>["https://ndownloader.figshare.com/files/1336383"], "description"=>"<p>A: Minimal (guaranteed) ethanol yield (under maximal substrate uptake rate) vs. maximal possible growth rate for each cMCS of scenario 3 in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003378#pcbi-1003378-t003\" target=\"_blank\">Table 3</a>. The size of the cMCSs are indicated by different markers. It becomes apparent that when higher maximal growth rates are required larger cut sets become necessary implying also a decrease in the guaranteed ethanol yield. B: Substrate-specific productivity (SSP) induced by the cut sets of scenario 3 in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003378#pcbi-1003378-t003\" target=\"_blank\">Table 3</a>. Cut sets were ordered with respect to the number of required knockouts. Note that some crosses represent several cMCSs having, for example, the same SSP.</p>", "links"=>[], "tags"=>["measures", "calculated", "designs", "growth-coupled", "ethanol"], "article_id"=>892419, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Axel von Kamp", "Steffen Klamt"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003378.g002", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_measures_of_calculated_strain_designs_for_growth_coupled_ethanol_production_in_E_coli_/892419", "title"=>"Performance measures of calculated strain designs for growth-coupled ethanol production in <i>E.coli</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-02 02:43:29"}
• {"files"=>["https://ndownloader.figshare.com/files/1336384"], "description"=>"<p>MCSs (synthetic reaction lethals) that disable growth in an <i>E. coli</i> genome-scale metabolic network with glucose as sole carbon source. The full/compressed networks contain 1668/562 metabolites and 2382/936 reactions. For computation 12 threads on a cluster node with two Intel Xeon DP X5650 processors (each 6 cores) were used. The computation time given in each row specifies the time needed to calculate the MCSs of the respective size. The total computation time for MCSs of size 1–5 was thus ∼430 h. In order to get comparable run times the SL Finder was executed on the same computer with GAMS 24.1.3 (using CPLEX 12.5.1 as solver). All physical memory was made available and up to 9 GB were used during optimization. The MCSEnumerator calculations were also done on a typical desktop PC with a quad-core CPU (Intel(R) Core(TM) i5-3570, 3.40 GHz) showing that the computation times increase only moderately by approximately 50%.</p>*)<p>Only 226 synthetic triple lethals (which are all contained in the MCSs found by MCSEnumerator) could be calculated after which optimization could not be continued due to numerical problems reported by the solver.</p>", "links"=>[], "tags"=>["smallest", "mcss", "disabling", "genome-scale"], "article_id"=>892420, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Axel von Kamp", "Steffen Klamt"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003378.t002", "stats"=>{"downloads"=>5, "page_views"=>33, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enumeration_of_smallest_MCSs_synthetic_reaction_lethals_disabling_growth_in_a_genome_scale_network_model_of_E_coli_/892420", "title"=>"Enumeration of smallest MCSs (synthetic reaction lethals) disabling growth in a genome-scale network model of <i>E.coli</i>.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-02 02:43:29"}
• {"files"=>["https://ndownloader.figshare.com/files/1336385"], "description"=>"<p>Constrained MCSs up to size 7 that lead to ethanol synthesis with high yield in <i>E.coli</i> while slow growth is possible. Four scenarios were considered differing in the maximal glucose uptake rate (; given in <i>mmol/(gDW⋅h)</i>) or/and in the demanded minimal ethanol yield (; given in molecules ethanol per molecules glucose) in the strain to be constructed. The total number of MCSs (#MCSs) refers to knock-out sets blocking flux vectors with low ethanol yield; the number of constrained MCSs (#cMCSs) refers to the subset of MCSs which allow in addition growth above the minimum threshold (for details see text). For the cMCSs found, the distribution over cut set sizes are also shown (no cMCSs with less than 3 cuts exist; the upper limit of cuts was set to 7).</p><p>The full/reduced networks contain 1668/564 metabolites and 2382/958 reactions (the reactions in the compressed network represent lumped reaction subsets). For computation 12 threads on a cluster node with two Intel Xeon DP X5650 processors (each 6 cores) were used.</p>", "links"=>[], "tags"=>["constrained", "mcss", "coupled", "ethanol", "biomass", "anaerobic"], "article_id"=>892421, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Axel von Kamp", "Steffen Klamt"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003378.t003", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Computation_of_constrained_MCSs_leading_to_coupled_ethanol_and_biomass_formation_by_E_coli_under_anaerobic_growth_on_glucose_/892421", "title"=>"Computation of constrained MCSs leading to coupled ethanol and biomass formation by <i>E. coli</i> under anaerobic growth on glucose.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-02 02:43:29"}
• {"files"=>["https://ndownloader.figshare.com/files/1336386"], "description"=>"<p>Computation times for MCSs that disable growth in an <i>E. coli</i> metabolic network model of the central metabolism under different substrate uptake conditions (cf. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003378#pcbi-1003378-t001\" target=\"_blank\">Table 1</a> in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003378#pcbi.1003378-Ballerstein1\" target=\"_blank\">[22]</a>). The full/compressed networks contain 89/25 metabolites and 106/42 reactions. Conversion of MCSs in the compressed network to those in the full network takes negligible computation time for the cases shown here. For iterative solve (ALGO1) CPLEX dynamic search was used while for populate calls (ALGO2) traditional branch-and-cut was applied. In the fifth problem, only the MCSs up to size 4 were calculated. The computation times for the classical approach (EM+minimal hitting sets) and for the dual approach of Ballerstein et al. in the first four problems are the same as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003378#pcbi.1003378-Ballerstein1\" target=\"_blank\">[22]</a>; note that neither method currently supports multiple threads. For calculations using multiple threads the physical computation time is shown with the sum of computation times (CPU times) over all threads in brackets. The calculations with 1 and 4 threads were performed with an Intel Q9550 desktop processor (4 cores) while for 12 threads a cluster node with two Intel Xeon DP X5650 processors (each 6 cores) was used.</p>", "links"=>[], "tags"=>["mcss", "medium-scale", "metabolic"], "article_id"=>892422, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Axel von Kamp", "Steffen Klamt"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003378.t001", "stats"=>{"downloads"=>6, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enumeration_of_all_MCSs_synthetic_reaction_lethals_in_a_medium_scale_metabolic_model_of_E_coli_/892422", "title"=>"Enumeration of all MCSs (synthetic reaction lethals) in a medium-scale metabolic model of <i>E. coli</i>.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-02 02:43:29"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Biochemistry", "average_usage"=>[282]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[327, 511]}, {"subject_area"=>"/Physical sciences", "average_usage"=>[271]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[286]}]}

F1000Prime