{"id":"W2282550052","doi":"10.3233/isb-140464","title":"Exploiting stoichiometric redundancies for computational efficiency and network reduction","year":2015,"lang":"en","type":"review","venue":"In Silico Biology","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flux balance analysis; Reduction (mathematics); Computer science; Steady state (chemistry); Macro; Matrix (chemical analysis); Metabolic network; Mathematical optimization; Network analysis; Topology (electrical circuits); Network topology; Mathematics; Chemistry; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.000603041,0.0002245599,0.0005986444,0.0002384974,0.00005433947,0.00001324718,0.0001102958,0.0003818352,0.000001071548],"category_scores_gemma":[0.0002544778,0.0001982302,0.000102385,0.0004437095,0.00009449218,0.000002266935,0.00006918056,0.0001309265,0.000001802309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003033099,"about_ca_system_score_gemma":0.0001169584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007816892,"about_ca_topic_score_gemma":0.000002001751,"domain_scores_codex":[0.9987155,0.0001070367,0.0003763857,0.0004935271,0.00003970673,0.0002679058],"domain_scores_gemma":[0.99953,0.00002270287,0.0001631724,0.0001582455,0.00008131468,0.00004458478],"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":[0.00001920349,0.00003976349,0.00001150936,0.001453285,0.00005900887,2.871959e-7,0.00002159743,0.00178048,0.004080947,0.0002519001,0.001330027,0.990952],"study_design_scores_gemma":[0.0001851118,0.0002062306,0.000004780464,0.0002847333,0.00005202246,0.00009858909,0.00001038382,0.00004215702,0.00004621721,0.0001584523,0.9986868,0.0002245372],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003548425,0.9925494,0.002351354,0.00001246789,0.0009751496,0.0004788002,0.00004162425,0.00001703647,0.00002580693],"genre_scores_gemma":[0.001740283,0.9931512,0.001975958,0.000009164924,0.001920241,0.0001195837,0.00091726,0.00003068391,0.0001355708],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9973568,"threshold_uncertainty_score":0.8083593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04090579175893761,"score_gpt":0.3370480378731214,"score_spread":0.2961422461141838,"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."}}