{"id":"W3113064477","doi":"10.1093/synbio/ysaa019","title":"Modular cell-free expression plasmids to accelerate biological design in cells","year":2020,"lang":"en","type":"article","venue":"Synthetic Biology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biological and Environmental Research; Office of Science; U.S. Department of Energy; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Joint Genome Institute; Suncor Energy Incorporated","keywords":"Synthetic biology; Metabolic engineering; Computational biology; Plasmid; Modular design; Biochemical engineering; Biology; Computer science; Expression vector; Biotechnology; DNA; Gene; Genetics; Engineering; Recombinant DNA","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":[0.0008471592,0.0009054733,0.0004694748,0.0004926811,0.0002735774,0.0007813877,0.0008424127,0.0004994243,0.001708403],"category_scores_gemma":[0.000903554,0.0004972357,0.0007077391,0.0005377947,0.0004018712,0.0006643099,0.0007259069,0.001831097,0.001807569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007204823,"about_ca_system_score_gemma":0.0005945154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004124268,"about_ca_topic_score_gemma":0.0005192252,"domain_scores_codex":[0.9993628,0.0001150694,0.00007352691,0.0001239336,0.0002341028,0.00009066021],"domain_scores_gemma":[0.9994171,0.0001349166,0.0001585575,0.0001109698,0.00010132,0.00007722122],"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":[0.00005796771,0.00005350614,0.0002034308,0.000117336,0.00001520751,0.00007935065,0.00005565935,0.001470885,0.9847701,0.00313602,0.0004872757,0.009553209],"study_design_scores_gemma":[0.00001784279,0.0001821043,0.0002346439,0.0000114014,0.00001403654,0.0001005575,0.00001324142,0.00336384,0.9795802,0.000321826,0.01614479,0.00001549805],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1963955,0.0007031704,0.791853,0.0004654994,0.0003295456,0.0007161552,0.001277686,0.004500314,0.003759234],"genre_scores_gemma":[0.4425184,0.001576324,0.5367807,0.0002293519,0.00005720128,0.000653099,0.006187281,0.001504406,0.01049316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001708403,"threshold_uncertainty_score":0.005715251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02526519611047439,"score_gpt":0.2949021593929583,"score_spread":0.2696369632824839,"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."}}