{"id":"W2059098646","doi":"10.3389/fbioe.2014.00068","title":"Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling","year":2014,"lang":"en","type":"article","venue":"Frontiers in Bioengineering and Biotechnology","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Bioinformatics Institute; New York University Abu Dhabi; York University; University of Minnesota","keywords":"Metabolic network; Chlamydomonas reinhardtii; Metabolic flux analysis; Metabolic pathway; Computational biology; Flux balance analysis; Cellular metabolism; Metabolomics; Biology; Systems biology; Flux (metallurgy); Metabolic engineering; Computer science; Biological system; Metabolism; Bioinformatics; Biochemistry; Chemistry; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003183954,0.0003153332,0.0004029518,0.000177239,0.0000880357,0.00002160356,0.000181681,0.000498087,0.000002052433],"category_scores_gemma":[0.00007236576,0.0003038583,0.00007095069,0.0003424544,0.0001301338,0.00001123004,0.0001467403,0.0003028832,0.000002097508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001541579,"about_ca_system_score_gemma":0.00002432187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003099995,"about_ca_topic_score_gemma":0.000006172256,"domain_scores_codex":[0.9983532,0.00003178441,0.0003313399,0.0006562789,0.00009888472,0.000528452],"domain_scores_gemma":[0.9994257,0.000002648645,0.0000710706,0.0004108549,0.00003532562,0.00005436275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005614368,0.00004502809,0.0008052703,0.00004109065,0.0001176626,8.79446e-7,0.00001650537,0.09237279,0.8810093,0.01237428,0.003110249,0.0100508],"study_design_scores_gemma":[0.00115235,0.0001626076,0.001386437,0.0000370606,0.00007617177,0.00005759843,0.00003789291,0.04162085,0.7392102,0.001086208,0.2144809,0.0006917592],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4205104,0.01343957,0.5633968,0.0006174518,0.001651872,0.0001912,0.00001349673,0.0001403298,0.00003894555],"genre_scores_gemma":[0.7509365,0.004709912,0.2429495,0.0001533989,0.0008570089,0.00004308622,0.0001163095,0.00004571467,0.0001885519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3304261,"threshold_uncertainty_score":0.9999413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005784636371001047,"score_gpt":0.1874563882477724,"score_spread":0.1816717518767713,"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."}}