{"id":"https://openalex.org/W7140173592","doi":"https://doi.org/10.48550/arxiv.2603.21743","title":"CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning","display_name":"CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7140173592","doi":"https://doi.org/10.48550/arxiv.2603.21743"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.21743","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21743","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":null,"license_id":null,"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.2603.21743","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Wu, Dongxia","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wu, Dongxia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Su, Shiye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Shiye","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhang, Yuhui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yuhui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Sui, Elaine","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sui, Elaine","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Lundberg, Emma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lundberg, Emma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Fox, Emily B.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fox, Emily B.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Yeung-Levy, Serena","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yeung-Levy, Serena","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.8711000084877014,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.8711000084877014,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"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/T10379","display_name":"Cellular Mechanics and Interactions","score":0.011599999852478504,"subfield":{"id":"https://openalex.org/subfields/1307","display_name":"Cell 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/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.00839999970048666,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7146000266075134},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5403000116348267},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.48420000076293945},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.42500001192092896},{"id":"https://openalex.org/keywords/virtual-actor","display_name":"Virtual actor","score":0.37720000743865967},{"id":"https://openalex.org/keywords/virtual-reality","display_name":"Virtual reality","score":0.3472999930381775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7213000059127808},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7146000266075134},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5418000221252441},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5403000116348267},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.48420000076293945},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.4381999969482422},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.42500001192092896},{"id":"https://openalex.org/C150303390","wikidata":"https://www.wikidata.org/wiki/Q1983852","display_name":"Virtual actor","level":3,"score":0.37720000743865967},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3686999976634979},{"id":"https://openalex.org/C194969405","wikidata":"https://www.wikidata.org/wiki/Q170519","display_name":"Virtual reality","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C13687954","wikidata":"https://www.wikidata.org/wiki/Q4826847","display_name":"Autonomous agent","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C2775905019","wikidata":"https://www.wikidata.org/wiki/Q192572","display_name":"In silico","level":3,"score":0.2624000012874603},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.21743","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21743","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.21743","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21743","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Building":[0],"virtual":[1,46,101],"cells":[2],"with":[3,49,89],"generative":[4,25],"models":[5,48],"to":[6,44,78],"simulate":[7],"cellular":[8],"behavior":[9],"in":[10],"silico":[11],"is":[12],"emerging":[13],"as":[14,57],"a":[15,100],"promising":[16],"paradigm":[17],"for":[18],"accelerating":[19],"drug":[20],"discovery.":[21],"However,":[22],"prior":[23],"image-based":[24],"approaches":[26],"can":[27],"produce":[28],"implausible":[29],"cell":[30,47,102],"images":[31],"that":[32,105],"violate":[33],"basic":[34],"physical":[35],"and":[36,70],"biological":[37],"constraints.":[38],"To":[39],"address":[40],"this,":[41],"we":[42],"propose":[43],"post-train":[45],"reinforcement":[50],"learning":[51],"(RL),":[52],"leveraging":[53],"biologically":[54],"meaningful":[55],"evaluators":[56],"reward":[58],"functions.":[59],"We":[60],"design":[61],"seven":[62],"rewards":[63],"spanning":[64],"three":[65],"categories-biological":[66],"function,":[67],"structural":[68],"validity,":[69],"morphological":[71],"correctness-and":[72],"optimize":[73],"the":[74],"state-of-the-art":[75],"CellFlux":[76,85],"model":[77],"yield":[79],"CellFluxRL.":[80],"CellFluxRL":[81],"consistently":[82],"improves":[83],"over":[84],"across":[86],"all":[87],"rewards,":[88],"further":[90],"performance":[91],"boosts":[92],"from":[93],"test-time":[94],"scaling.":[95],"Overall,":[96],"our":[97],"results":[98],"present":[99],"modeling":[103],"framework":[104],"enforces":[106],"physically-based":[107],"constraints":[108],"through":[109],"RL,":[110],"advancing":[111],"beyond":[112],"\"visually":[113],"realistic\"":[114],"generations":[115],"towards":[116],"\"biologically":[117],"meaningful\"":[118],"ones.":[119]},"counts_by_year":[],"updated_date":"2026-04-25T08:17:42.794288","created_date":"2026-03-25T00:00:00"}