{"id":"W4394762841","doi":"10.1021/acssynbio.4c00019","title":"Metabolic Engineering of <i>Corynebacterium glutamicum</i> for Highly Efficient Production of Ectoine","year":2024,"lang":"en","type":"article","venue":"ACS Synthetic Biology","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Zhejiang University of Technology; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Ectoine; Corynebacterium glutamicum; Metabolic engineering; Fermentation; Biochemistry; Biology; Industrial microbiology; Escherichia coli; Industrial fermentation; Operon; Osmoprotectant; Gene; Amino acid; Proline","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002130998,0.0006848426,0.0003314082,0.0002212984,0.0001834252,0.0004266152,0.0004399381,0.0002974255,0.0002590589],"category_scores_gemma":[0.0002107886,0.000167858,0.0003872521,0.0004608962,0.0001630261,0.0002008714,0.0003903548,0.0003910627,0.0002164648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005690785,"about_ca_system_score_gemma":0.0006285335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004463012,"about_ca_topic_score_gemma":0.005776502,"domain_scores_codex":[0.9997744,0.00002878844,0.00003106471,0.00004301994,0.00007136633,0.00005128024],"domain_scores_gemma":[0.999897,0.00000846384,0.00003788837,0.00001326442,0.00002205285,0.00002125084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003381457,0.00001615373,0.0001632172,0.00004573519,0.000006083341,0.0001008147,0.00001145118,0.0003624797,0.997769,0.0001221144,0.00003399136,0.001335193],"study_design_scores_gemma":[0.0000177603,0.0001376286,0.002467637,0.00001522921,0.00002405298,0.0002782289,0.00004083411,0.002524979,0.9889989,0.00006662615,0.005414431,0.00001361541],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828739,0.0009232825,0.01224953,0.0002013653,0.00006844389,0.0001164158,0.0007341207,0.0001871067,0.002645858],"genre_scores_gemma":[0.9751169,0.000864229,0.02112515,0.00005777127,0.000005040984,0.00004988946,0.0009335035,0.00007666607,0.001770929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004463012,"threshold_uncertainty_score":0.008874059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005030789855927213,"score_gpt":0.2167752896567741,"score_spread":0.2117444998008468,"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."}}