{"id":"W1588700440","doi":"10.1016/b978-0-444-54298-4.50068-4","title":"Model-driven design based on sensitivity analysis for a synthetic biology application","year":2011,"lang":"en","type":"book-chapter","venue":"Computer-aided chemical engineering/Computer aided chemical engineering","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Synthetic biology; Biochemical engineering; Organism; Sensitivity (control systems); Metabolic engineering; Computer science; Flux (metallurgy); Biological system; Control engineering; Engineering; Biology; Computational biology; Chemistry; Electronic engineering; Gene","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":[0.0007585504,0.001152199,0.0007447344,0.0003593796,0.000323996,0.0007752739,0.0008011383,0.0007478616,0.003574328],"category_scores_gemma":[0.001386302,0.0005872815,0.001191964,0.0002564848,0.0003843478,0.0004296596,0.000582539,0.0009092929,0.0004897371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005682877,"about_ca_system_score_gemma":0.0006396357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00212224,"about_ca_topic_score_gemma":0.001820188,"domain_scores_codex":[0.9996088,0.0001425206,0.00001791728,0.00006172745,0.0001411034,0.00002796079],"domain_scores_gemma":[0.9995278,0.0003148771,0.00003040919,0.00003129992,0.00008574848,0.000009892606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007879847,0.00005012003,0.00009231357,0.0001127874,0.00004176559,0.00004345051,0.00002737142,0.9543059,0.01078054,0.008814752,0.000623751,0.0250283],"study_design_scores_gemma":[0.000005247672,0.00002371494,0.00002833974,0.000003565816,0.000007457883,0.000006465916,0.000001219411,0.9964123,0.00135739,0.001698717,0.0004520628,0.000003566775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006538134,0.0001493825,0.9872699,0.00009352159,0.00004758016,0.00007652755,0.00004571922,0.0007328888,0.005046442],"genre_scores_gemma":[0.6878143,0.000324873,0.304765,0.000170684,0.00004172814,0.0005100117,0.0001686919,0.0003028925,0.005901719],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003574328,"threshold_uncertainty_score":0.01195735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01442035746160976,"score_gpt":0.1989141947887748,"score_spread":0.1844938373271651,"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."}}