{"id":"W2930102738","doi":"10.1016/j.biotechadv.2019.04.001","title":"Systems biology based metabolic engineering for non-natural chemicals","year":2019,"lang":"en","type":"review","venue":"Biotechnology Advances","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Genome Canada","keywords":"Metabolic engineering; Biochemical engineering; Synthetic biology; Context (archaeology); Production (economics); Metabolic pathway; Industrial microbiology; Systems biology; Metabolic flux analysis; Fermentation; Flux (metallurgy); Biotechnology; Biology; Computational biology; Enzyme; Metabolism; Chemistry; Biochemistry; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005265343,0.00164145,0.001466075,0.001756232,0.0002100208,0.001212488,0.001326563,0.001017076,0.003104123],"category_scores_gemma":[0.0003976816,0.0004578014,0.0006086456,0.002718728,0.0005895827,0.001600472,0.001101866,0.001864145,0.002543191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007445446,"about_ca_system_score_gemma":0.0007656468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007548594,"about_ca_topic_score_gemma":0.001259677,"domain_scores_codex":[0.9997812,0.00002328109,0.00001883926,0.00004490831,0.0001031202,0.00002873589],"domain_scores_gemma":[0.999853,0.00006651505,0.00002764287,0.000009043152,0.00002987426,0.00001387382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008895677,0.0001139203,0.000066443,0.01807216,0.00009984887,0.0001726773,0.00002863275,0.000934061,0.01690723,0.008501959,0.01823828,0.9367759],"study_design_scores_gemma":[0.00001702222,0.00009765315,0.0002608894,0.001315866,0.00008356445,0.0004534585,0.00001669917,0.0002950254,0.00475361,0.003126289,0.9895582,0.00002164319],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002043143,0.996672,0.00106131,0.0001328614,0.0002395132,0.000006815999,0.00002791251,0.00001773498,0.00163749],"genre_scores_gemma":[0.001173895,0.9961569,0.0009389757,0.0001186194,0.0001199768,0.00001022834,0.00006074234,0.000004610923,0.001416104],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003104123,"threshold_uncertainty_score":0.01038432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01295443797102214,"score_gpt":0.2887266720280743,"score_spread":0.2757722340570521,"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."}}