{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0001994051,0.0005459831,0.001425138,0.000270922,0.0000471514,0.00001720943,0.0005123593,0.00155616,0.000001833311],"category_scores_gemma":[0.000331032,0.0004482063,0.0004345252,0.0002626382,0.00009867147,0.000004989366,0.0001019514,0.000365711,0.00001875911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002245937,"about_ca_system_score_gemma":0.0001036955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002556802,"about_ca_topic_score_gemma":5.211487e-7,"domain_scores_codex":[0.9980578,0.00002953155,0.0004919522,0.0008826784,0.00005230602,0.0004857123],"domain_scores_gemma":[0.9988419,0.00003004602,0.0002897403,0.0007283322,0.0000652661,0.00004472888],"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":[0.00001826457,0.00003795901,7.21998e-7,0.01261745,0.0003517676,6.475268e-7,9.254994e-7,0.0002176682,0.3643317,0.0006978674,0.0004748172,0.6212503],"study_design_scores_gemma":[0.0002167439,0.0000916577,1.212837e-7,0.0007428188,0.0002850996,0.00003350641,0.000001565528,0.0001387666,0.05426487,0.000002377855,0.9437408,0.0004816668],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0000634258,0.9837111,0.01145748,0.00003574986,0.003276124,0.001143824,0.000175342,0.0001292547,0.000007710313],"genre_scores_gemma":[0.0003684659,0.9925901,0.004290622,0.00001592949,0.00104782,0.0003962912,0.0009169194,0.00008620054,0.0002876632],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.943266,"threshold_uncertainty_score":0.999797,"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."}}