{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004574853,0.001000009,0.0006242451,0.0005106963,0.0003565441,0.0006599107,0.0008999989,0.0004941214,0.0008985419],"category_scores_gemma":[0.0008816636,0.00042406,0.001420121,0.000502416,0.00026788,0.0007312483,0.000510007,0.0009638063,0.000277694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008568987,"about_ca_system_score_gemma":0.000765479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008039296,"about_ca_topic_score_gemma":0.008281301,"domain_scores_codex":[0.9997808,0.00005680124,0.00001575128,0.00007466148,0.00004788028,0.00002406546],"domain_scores_gemma":[0.9996412,0.00014022,0.00004454524,0.0000830815,0.00007350856,0.00001746822],"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":[0.0001216985,0.00006001603,0.001931585,0.00009786642,0.00004520313,0.00007843094,0.00004170683,0.9453779,0.04571451,0.001255619,0.0001882229,0.005087189],"study_design_scores_gemma":[0.000009568835,0.00003379259,0.0007564462,0.000003136987,0.00002015211,0.00001475041,0.00001274346,0.987385,0.01040759,0.0005637381,0.0007841701,0.000008903326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5315063,0.0003240481,0.4578591,0.0001503713,0.00004242353,0.0002074732,0.004330517,0.002763826,0.00281594],"genre_scores_gemma":[0.7550653,0.000497735,0.2364315,0.00003527072,0.000005839765,0.0004851869,0.006112066,0.0002446164,0.001122495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008039296,"threshold_uncertainty_score":0.01598501,"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."}}