{"id":"W2606897259","doi":"10.1371/journal.pcbi.1005409","title":"From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints","year":2017,"lang":"en","type":"review","venue":"PLoS Computational Biology","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"German Network for Bioinformatics Infrastructure; Standortagentur Tirol; Bundesministerium für Verkehr, Innovation und Technologie; Max-Planck-Gesellschaft; Bundesministerium für Wissenschaft, Forschung und Wirtschaft; Austrian Science Fund; Bundesministerium für Bildung und Forschung; Amt der NÖ Landesregierung","keywords":"Flux (metallurgy); Flux balance analysis; Constraint (computer-aided design); Metabolic network; Physics; Metabolic flux analysis; Computer science; State (computer science); Applied mathematics; Mathematics; Algorithm; Geometry; Chemistry","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":[0.001522724,0.001558437,0.000848681,0.001712217,0.0005332901,0.002231162,0.001445464,0.0008730553,0.003998234],"category_scores_gemma":[0.003730839,0.0006015478,0.001790549,0.002184007,0.001822842,0.003715351,0.002258526,0.002724471,0.0008142992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001041123,"about_ca_system_score_gemma":0.001097087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002409234,"about_ca_topic_score_gemma":0.001339043,"domain_scores_codex":[0.999265,0.0002992223,0.00003869148,0.0001375631,0.0001885517,0.00007096438],"domain_scores_gemma":[0.9991065,0.0005071525,0.0001086375,0.0001056035,0.0001196968,0.00005237442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005353304,0.00003477162,0.0005310735,0.0002143659,0.00003198848,0.0001275282,0.0001409844,0.2679345,0.002433713,0.6838093,0.001664673,0.04302355],"study_design_scores_gemma":[0.000006927811,0.00002421667,0.0001386915,0.00005058289,0.000009083355,0.00004196517,0.00004219697,0.4064894,0.0007640211,0.5871436,0.005267582,0.00002173426],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.006470046,0.0006080883,0.9879301,0.0001957936,0.00004186974,0.00003050341,0.0002131327,0.0000821424,0.004428344],"genre_scores_gemma":[0.3168243,0.004146778,0.6687294,0.0003210321,0.0002536103,0.0005626463,0.00103907,0.0004268335,0.007696429],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.003998234,"threshold_uncertainty_score":0.0133754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0351518328328449,"score_gpt":0.3072844607807452,"score_spread":0.2721326279479003,"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."}}