{"id":"W4408884521","doi":"10.1007/s00449-025-03147-z","title":"Hybrid dynamic flux balance modeling approach for bioprocesses: an E. coli case study","year":2025,"lang":"en","type":"article","venue":"Bioprocess and Biosystems Engineering","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Mitacs","keywords":"Flux balance analysis; Bioprocess; Overfitting; Computer science; Scalability; Biological system; Mathematical optimization; Partial least squares regression; Biochemical engineering; Mathematics; Machine learning; Chemistry; Engineering","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":[0.0008418161,0.001433208,0.001007467,0.0008529389,0.0007670198,0.002103058,0.001268482,0.002227295,0.00219582],"category_scores_gemma":[0.0008936584,0.0004725221,0.00125976,0.0007366195,0.0004117988,0.0008595297,0.001021311,0.0009102017,0.0003007433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001271438,"about_ca_system_score_gemma":0.001213886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02014143,"about_ca_topic_score_gemma":0.01353193,"domain_scores_codex":[0.9997264,0.0001038551,0.00001520642,0.00005469909,0.00006224857,0.00003756955],"domain_scores_gemma":[0.9995816,0.0002789969,0.00002944459,0.00002169235,0.00006696718,0.00002131896],"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":[0.00006100335,0.0001036282,0.0008829185,0.00007720619,0.0000372024,0.000245851,0.00006485956,0.9822341,0.002296213,0.005151763,0.0002203553,0.008625007],"study_design_scores_gemma":[0.000008001938,0.00002223244,0.0001207717,0.000005814709,0.00001215311,0.00002049989,0.00002382876,0.9969918,0.000587733,0.00146594,0.0007344697,0.000006818088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2816353,0.001296537,0.6867551,0.001119904,0.00008067378,0.0002299414,0.0009924291,0.0009498233,0.02694031],"genre_scores_gemma":[0.9249077,0.0005036585,0.06720589,0.00006715671,0.00002284064,0.0001740249,0.00031948,0.00009269403,0.006706487],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02014143,"threshold_uncertainty_score":0.04004836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007780906806598865,"score_gpt":0.2415659220637264,"score_spread":0.2337850152571275,"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."}}