{"id":"W2097159774","doi":"10.1186/1752-0509-2-41","title":"Extraction of elementary rate constants from global network analysis of E. coli central metabolism","year":2008,"lang":"en","type":"article","venue":"BMC Systems Biology","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"","keywords":"Reaction rate constant; Computer science; Simulated annealing; Applied mathematics; Constant (computer programming); Reaction rate; Estimation theory; Biological system; Algorithm; Statistical physics; Mathematical optimization; Mathematics; Physics; Chemistry; Biology; Kinetics","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":[0.0006518585,0.0009237116,0.0004736016,0.001165815,0.0002398933,0.0006348063,0.000575772,0.0004171155,0.001326056],"category_scores_gemma":[0.002132032,0.0003355919,0.0008283834,0.0005990606,0.0002962513,0.0007202266,0.0004923043,0.0005276419,0.0003267456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008572853,"about_ca_system_score_gemma":0.0007687829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003949108,"about_ca_topic_score_gemma":0.004561407,"domain_scores_codex":[0.999842,0.00004394618,0.00001114969,0.00005268836,0.00003720835,0.00001292876],"domain_scores_gemma":[0.9991995,0.0004957236,0.0001104179,0.00008073847,0.00008936031,0.00002433897],"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":[0.0000293708,0.00003250307,0.002350511,0.0001043434,0.00003865784,0.00006213761,0.00003930191,0.9447102,0.01645262,0.005206309,0.0001752009,0.03079891],"study_design_scores_gemma":[0.000001523403,0.00000988328,0.000696668,0.000003855178,0.000007481452,0.00001418247,0.000006559393,0.9910961,0.003555924,0.004238305,0.0003640462,0.000005378112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0633201,0.0001145583,0.9342648,0.00004666734,0.000005674483,0.00003696116,0.0003117493,0.0004301685,0.001469245],"genre_scores_gemma":[0.6748628,0.0003826319,0.3216744,0.0000234207,0.00001274038,0.0001821856,0.001126938,0.0002240174,0.00151093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003949108,"threshold_uncertainty_score":0.007852256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01310948150018482,"score_gpt":0.2478761742534758,"score_spread":0.234766692753291,"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."}}