{"id":"https://openalex.org/W7160634760","doi":"https://doi.org/10.48550/arxiv.2605.06552","title":"Sequential Design of Genetic Circuits Under Uncertainty With Reinforcement Learning","display_name":"Sequential Design of Genetic Circuits Under Uncertainty With Reinforcement Learning","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160634760","doi":"https://doi.org/10.48550/arxiv.2605.06552"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.06552","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06552","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.06552","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114620214","display_name":"Michal Kobiela","orcid":"https://orcid.org/0009-0004-9308-1993"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kobiela, Michal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016017194","display_name":"Diego A. Oyarz\u00fan","orcid":"https://orcid.org/0000-0002-0381-5278"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oyarz\u00fan, Diego A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5038807284","display_name":"Michael U. Gutmann","orcid":"https://orcid.org/0000-0002-5329-9910"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gutmann, Michael U.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10621","display_name":"Gene Regulatory Network Analysis","score":0.9254000186920166,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10621","display_name":"Gene Regulatory Network Analysis","score":0.9254000186920166,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.03229999914765358,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.005499999970197678,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6140000224113464},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.49970000982284546},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4796000123023987},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4456999897956848},{"id":"https://openalex.org/keywords/sequential-analysis","display_name":"Sequential analysis","score":0.4259999990463257},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.3677999973297119},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3675000071525574},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.3619000017642975},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.35600000619888306}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6953999996185303},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6140000224113464},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.49970000982284546},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4796000123023987},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45500001311302185},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4456999897956848},{"id":"https://openalex.org/C80478641","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential analysis","level":2,"score":0.4259999990463257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42590001225471497},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.3677999973297119},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3675000071525574},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.3619000017642975},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.35600000619888306},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3041999936103821},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2822999954223633},{"id":"https://openalex.org/C90559484","wikidata":"https://www.wikidata.org/wiki/Q778379","display_name":"Expression (computer science)","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C110332635","wikidata":"https://www.wikidata.org/wiki/Q629498","display_name":"Genetic programming","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C86426650","wikidata":"https://www.wikidata.org/wiki/Q7452504","display_name":"Sequential estimation","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.26159998774528503},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.25699999928474426},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.06552","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06552","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.06552","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06552","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"design":[1,130],"of":[2,14,39,115],"biological":[3],"systems":[4],"is":[5],"hindered":[6],"by":[7],"uncertainty":[8,80],"arising":[9],"from":[10],"both":[11,37,155],"intrinsic":[12],"stochasticity":[13],"biomolecular":[15],"reactions":[16],"and":[17,91,146,158],"variability":[18],"across":[19,112],"laboratory":[20,68],"or":[21,49],"experimental":[22,96],"conditions.":[23],"In":[24],"this":[25,103],"work,":[26],"we":[27,105],"present":[28],"a":[29,54,113,147],"sequential":[30],"framework":[31,139],"to":[32,66,99],"optimize":[33],"genetic":[34],"circuits":[35],"under":[36],"forms":[38],"uncertainty.":[40],"By":[41],"employing":[42],"simulator":[43],"models":[44,141],"based":[45],"on":[46,140],"differential":[47],"equations":[48],"Markov":[50],"jump":[51],"processes":[52],"alongside":[53],"reinforcement":[55],"learning":[56],"(RL)":[57],"policy-based":[58],"approach,":[59],"our":[60,138],"method":[61],"suggests":[62],"experiments":[63],"that":[64,151],"adapt":[65],"unknown":[67],"conditions":[69],"while":[70],"accounting":[71],"for":[72,124,142],"inherent":[73],"stochasticity.":[74],"While":[75],"previous":[76],"Bayesian":[77],"methods":[78],"address":[79],"through":[81],"iterative":[82],"experiment-inference-optimization":[83],"cycles,":[84],"they":[85],"typically":[86],"require":[87],"computationally":[88],"expensive":[89],"inference":[90,127],"optimization":[92],"steps":[93],"after":[94],"each":[95],"round,":[97],"leading":[98],"delays.":[100],"To":[101],"overcome":[102],"bottleneck,":[104],"propose":[106],"an":[107],"amortized":[108],"approach":[109],"trained":[110],"up-front":[111],"distribution":[114],"possible":[116],"uncertain":[117],"parameters.":[118],"This":[119],"strategy":[120],"sidesteps":[121],"the":[122,129],"need":[123],"explicit":[125],"parameter":[126],"during":[128],"cycle,":[131],"enabling":[132],"immediate,":[133],"observation-based":[134],"adaptation.":[135],"We":[136],"demonstrate":[137],"heterologous":[143],"gene":[144],"expression":[145],"repressilator":[148],"circuit,":[149],"showing":[150],"it":[152],"efficiently":[153],"handles":[154],"molecular":[156],"noise":[157],"cross-laboratory":[159],"variability.":[160]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-09T00:00:00"}
