{"id":"W4313891634","doi":"10.1101/2023.01.08.523183","title":"Engineering <i>Escherichia coli</i> to produce aromatic chemicals from ethylene glycol","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Research Foundation Singapore; National Research Foundation","keywords":"Escherichia coli; Chemistry; Fermentation; Ethylene glycol; Glycerol; Aromatic amino acids; Hydrolysis; Cellulosic ethanol; Raw material; Organic chemistry; Metabolic engineering; Yield (engineering); Phenylalanine; Substrate (aquarium); Sugar; Biochemistry; Cellulose; Materials science; Amino acid; Enzyme; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004858571,0.0006730107,0.0006038842,0.0001705096,0.00007610855,0.0001355321,0.0006434117,0.0007295739,0.00001158858],"category_scores_gemma":[0.0007883597,0.0007634301,0.0001867597,0.000508837,0.00003907326,0.000007856964,0.0006714448,0.000590039,0.0001911537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009244667,"about_ca_system_score_gemma":0.0002335606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009079972,"about_ca_topic_score_gemma":0.0000022527,"domain_scores_codex":[0.9968747,0.00005990066,0.0005687912,0.00158117,0.0002851046,0.0006302894],"domain_scores_gemma":[0.9974934,0.00001368979,0.0002095265,0.001740178,0.0002023367,0.0003408905],"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":[0.00003196545,0.00006859531,0.0001349755,0.0002544175,0.0002044264,0.000007518379,0.000007073962,0.001790355,0.9944309,0.000004865275,0.003063214,0.000001694563],"study_design_scores_gemma":[0.000246097,0.00004631223,0.007421723,0.0003091609,0.00009695259,1.771986e-8,0.000001085003,0.0001268949,0.9589111,6.538272e-7,0.03199341,0.0008466523],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906567,0.0009624986,0.003454838,0.0004215372,0.002731194,0.0009636568,0.0003212329,0.0004866104,0.000001752549],"genre_scores_gemma":[0.9783877,0.0003151362,0.01695585,0.0002657028,0.003295231,0.0004386503,0.000008992762,0.0002800409,0.00005269154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03551986,"threshold_uncertainty_score":0.9994817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009458266174061243,"score_gpt":0.20463169143327,"score_spread":0.1951734252592088,"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."}}