{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001025124,0.001172431,0.001436245,0.001952341,0.0003012335,0.001341398,0.001685995,0.0009332373,0.003454464],"category_scores_gemma":[0.001673797,0.0006025296,0.001055982,0.002348314,0.00104346,0.002077238,0.0009515356,0.001796435,0.001966812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009842335,"about_ca_system_score_gemma":0.001064468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001156853,"about_ca_topic_score_gemma":0.001324262,"domain_scores_codex":[0.9995912,0.000100984,0.00003311605,0.00008678031,0.0001620987,0.00002584662],"domain_scores_gemma":[0.999483,0.0003117826,0.00003673653,0.00005474458,0.00009600243,0.00001766554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005965784,0.00006573805,0.000328701,0.007229883,0.0002691664,0.0002160154,0.00008407256,0.05413433,0.007509409,0.1992808,0.01203158,0.7187907],"study_design_scores_gemma":[0.00006103596,0.0001642273,0.0007483716,0.001554869,0.0002433874,0.001025422,0.0000789261,0.09177642,0.009300694,0.2437703,0.6511551,0.0001213654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003718732,0.686696,0.2813826,0.001649446,0.0007670979,0.00009400873,0.0002473811,0.0005219468,0.02492283],"genre_scores_gemma":[0.05654145,0.7626218,0.1703717,0.000524013,0.0008905782,0.0003053344,0.0006244602,0.0002035555,0.007917195],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003454464,"threshold_uncertainty_score":0.01155639,"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."}}