{"id":"W4383742655","doi":"10.1016/j.compchemeng.2023.108350","title":"A Method for tackling multiplicity in dynamic flux balance models by an ellipsoidal reflection operation","year":2023,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Sanofi Pasteur; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Ellipsoid; Flux balance analysis; Interior point method; Mathematical optimization; Linear programming; Flux (metallurgy); Reflection (computer programming); Dynamic programming; Computer science; Algorithm; Interval (graph theory); Mathematics; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001999406,0.0001436664,0.0001402419,0.0000668108,0.00002716512,0.00002235647,0.000108788,0.0001376447,4.277692e-7],"category_scores_gemma":[0.00003201035,0.0001625978,0.00005001131,0.0001751635,0.000006868295,0.00001401623,0.00003983841,0.0001059764,0.000001313297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003461738,"about_ca_system_score_gemma":0.000009689839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001280752,"about_ca_topic_score_gemma":0.00000162125,"domain_scores_codex":[0.9991346,0.00001201987,0.0001779794,0.0003820763,0.00005559947,0.0002377017],"domain_scores_gemma":[0.999724,0.000008527152,0.00002316056,0.0001590603,0.00002997416,0.00005533515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001102984,0.00001096887,0.000002195251,0.00002052764,0.00000638025,1.342913e-7,0.00002058346,0.4388785,0.5586385,0.000006017022,0.0001817398,0.002223472],"study_design_scores_gemma":[0.0002029508,0.00002127432,0.000007150087,0.00001089429,0.000003101316,0.000003278775,0.000002375472,0.5700081,0.4286525,0.000007771205,0.0009735187,0.0001070709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4298144,0.0000800796,0.5696889,0.00003110592,0.0001848149,0.0001141393,0.000007443422,0.00007761073,0.0000015366],"genre_scores_gemma":[0.8873276,0.00003098329,0.1115862,0.00002970571,0.0002916511,0.00003873317,0.0006139803,0.00003243858,0.00004874819],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4581027,"threshold_uncertainty_score":0.6630546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009016912927962249,"score_gpt":0.2651656628522957,"score_spread":0.2561487499243335,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}