{"id":"W4411122517","doi":"10.1016/j.synbio.2025.06.003","title":"Metabolic engineering of Escherichia coli for squalene overproduction","year":2025,"lang":"en","type":"article","venue":"Synthetic and Systems Biotechnology","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Key Technologies Research and Development Program; Science and Technology Commission of Shanghai Municipality; Shanghai Tobacco Group; Canadian Anesthesiologists' Society","keywords":"Overproduction; Squalene; Metabolic engineering; Metabolic regulation; Biotechnology; Chemistry; Mathematics; Biology; Biochemistry; Metabolism","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002283293,0.0001188153,0.0002348043,0.0001324083,0.00003570953,0.000007070464,0.00008357986,0.0002856676,7.737861e-7],"category_scores_gemma":[0.0001871152,0.0001110487,0.00004795464,0.0001506442,0.00007164513,0.000001967356,0.00004160415,0.00006064429,4.475801e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004828913,"about_ca_system_score_gemma":0.00002205422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003019138,"about_ca_topic_score_gemma":0.000001239751,"domain_scores_codex":[0.9992787,0.0000166606,0.0002216617,0.0002970629,0.00003348976,0.0001524683],"domain_scores_gemma":[0.9995719,0.000005230144,0.00006467865,0.0002847316,0.0000555814,0.00001786349],"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":[0.00002458344,0.00001886611,0.0000241467,0.000192514,0.00006503749,5.198637e-8,0.000006643271,0.0002490191,0.9807464,0.01467608,0.0001402253,0.003856421],"study_design_scores_gemma":[0.000202283,0.00007816394,0.000158826,0.00003967514,0.00003866692,0.00001098931,0.00004227011,0.0005558968,0.8493239,0.00002462638,0.1494285,0.00009612204],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9345576,0.0113524,0.05215838,0.0003628742,0.001074573,0.0003692318,0.00002233271,0.0000486412,0.0000539836],"genre_scores_gemma":[0.9971135,0.0007343991,0.001358039,0.00000963164,0.0001367237,0.00004927224,0.00001055188,0.00001189657,0.0005760345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1492883,"threshold_uncertainty_score":0.4528433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004578994065085165,"score_gpt":0.2088123520250259,"score_spread":0.2042333579599407,"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."}}