{"id":"W4413805314","doi":"10.1101/2025.08.28.672878","title":"Evolutionary simulations reveal role for genomic recombination in the evolution of gene regulatory network complexity and robustness","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Michael Smith Health Research BC","keywords":"Robustness (evolution); Gene regulatory network; Biology; Selection (genetic algorithm); Evolutionary biology; Coevolution; Gene; Recombination; Convergence (economics); Computational biology; Computer science; Genetics; Artificial intelligence; Gene expression; Economics","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":[0.0007183234,0.000317425,0.0005031008,0.0005144082,0.0005420253,0.001058291,0.0006823003,0.001165624,0.002424821],"category_scores_gemma":[0.004032392,0.0002702268,0.0005443743,0.0003716686,0.0009123998,0.000881588,0.0006799139,0.0009396256,0.0001595697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001027306,"about_ca_system_score_gemma":0.0006573666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007005285,"about_ca_topic_score_gemma":0.005191613,"domain_scores_codex":[0.9998222,0.00007729475,0.000006467676,0.00002889385,0.00002827984,0.00003694191],"domain_scores_gemma":[0.9987687,0.0008131427,0.000102722,0.0001027445,0.00007289308,0.0001396953],"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":[0.00008024414,0.00006027981,0.00541964,0.00002645846,0.00003964301,0.0001004973,0.00008312119,0.9802762,0.002551125,0.009610215,0.0003591637,0.001393315],"study_design_scores_gemma":[0.0000268378,0.00002550597,0.000904177,0.000004460898,0.000008774154,0.000009379697,0.00002808993,0.9953468,0.0003128294,0.003161359,0.0001650035,0.000006829825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9837776,0.00009121546,0.01068397,0.0005712368,0.00002756167,0.00001350434,0.0001371685,0.00008559834,0.004612115],"genre_scores_gemma":[0.9962925,0.00005034069,0.002966285,0.00006654813,0.000006333444,0.00002329934,0.00006406839,0.00002619878,0.000504394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007005285,"threshold_uncertainty_score":0.01392901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117203415572596,"score_gpt":0.2299873276091884,"score_spread":0.2182669860519288,"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."}}