{"id":"https://openalex.org/W7123348934","doi":"https://doi.org/10.1109/cdc57313.2025.11312596","title":"STL-based Optimization of Biomolecular Neural Networks for Regression and Control","display_name":"STL-based Optimization of Biomolecular Neural Networks for Regression and Control","publication_year":2025,"publication_date":"2025-12-09","ids":{"openalex":"https://openalex.org/W7123348934","doi":"https://doi.org/10.1109/cdc57313.2025.11312596"},"language":null,"primary_location":{"id":"doi:10.1109/cdc57313.2025.11312596","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc57313.2025.11312596","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 64th Conference on Decision and Control (CDC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005108410","display_name":"Eric Palanques-Tost","orcid":"https://orcid.org/0000-0002-0783-9779"},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eric Palanques-Tost","raw_affiliation_strings":["Boston University,Boston,MA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boston University,Boston,MA,USA","institution_ids":["https://openalex.org/I111088046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065905965","display_name":"Hanna Krasowski","orcid":"https://orcid.org/0000-0002-6730-3802"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hanna Krasowski","raw_affiliation_strings":["University of California,Berkeley,CA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California,Berkeley,CA,USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093988643","display_name":"Murat Arcak","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Murat Arcak","raw_affiliation_strings":["University of California,Berkeley,CA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California,Berkeley,CA,USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103273436","display_name":"Ron J. Weiss","orcid":"https://orcid.org/0000-0003-2010-4053"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ron Weiss","raw_affiliation_strings":["Massachusetts Institute of Technology,Cambridge,MA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology,Cambridge,MA,USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000180146","display_name":"Belta Calin","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Calin Belta","raw_affiliation_strings":["University of Maryland,College Park,MD,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland,College Park,MD,USA","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.75851246,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3276","last_page":"3281"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10621","display_name":"Gene Regulatory Network Analysis","score":0.20389999449253082,"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":0.20389999449253082,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.09000000357627869,"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/T10932","display_name":"Microbial Metabolic Engineering and Bioproduction","score":0.07199999690055847,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.6175000071525574},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5674999952316284},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.5353000164031982},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.47200000286102295},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42719998955726624},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.41850000619888306}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7113999724388123},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6481999754905701},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6175000071525574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6118000149726868},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5674999952316284},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.5353000164031982},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.47200000286102295},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42719998955726624},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.41850000619888306},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.41600000858306885},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.37279999256134033},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.3246000111103058},{"id":"https://openalex.org/C91575142","wikidata":"https://www.wikidata.org/wiki/Q1971426","display_name":"Optimal control","level":2,"score":0.26429998874664307}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cdc57313.2025.11312596","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc57313.2025.11312596","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 64th Conference on Decision and Control (CDC)","raw_type":"proceedings-article"}],"best_oa_location":null,"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":{"Biomolecular":[0],"Neural":[1],"Networks":[2],"(BNNs),":[3],"artificial":[4],"neural":[5],"networks":[6],"with":[7,112],"biologically":[8],"synthesizable":[9],"architectures,":[10],"achieve":[11],"universal":[12],"function":[13],"approximation":[14],"capabilities":[15],"beyond":[16],"simple":[17],"biological":[18,79],"circuits.":[19],"However,":[20],"training":[21,45],"BNNs":[22,71,91],"remains":[23],"challenging":[24],"due":[25],"to":[26,43,72,92,118,125],"the":[27,52,61,108,137],"lack":[28],"of":[29,55,60,96],"target":[30],"data.":[31],"To":[32],"address":[33],"this,":[34],"we":[35,82,89,106],"propose":[36],"leveraging":[37],"Signal":[38],"Temporal":[39],"Logic":[40],"(STL)":[41],"specifications":[42],"define":[44],"objectives":[46],"for":[47],"BNNs.":[48],"We":[49],"build":[50],"on":[51],"quantitative":[53],"semantics":[54],"STL,":[56],"enabling":[57],"gradient-based":[58],"optimization":[59],"BNN":[62,109],"weights,":[63],"and":[64,75,99,140],"introduce":[65],"a":[66,100,113],"learning":[67,117,134],"algorithm":[68],"that":[69,132],"enables":[70],"perform":[73],"regression":[74,85,139],"control":[76,102,141],"tasks":[77,142],"in":[78,87,104,110],"systems.":[80],"Specifically,":[81],"investigate":[83],"two":[84],"problems":[86],"which":[88,105],"train":[90,107],"act":[93],"as":[94],"reporters":[95],"dysregulated":[97],"states,":[98],"feedback":[101],"problem":[103],"closed-loop":[111],"chronic":[114],"disease":[115],"model,":[116],"reduce":[119],"inflammation":[120],"while":[121],"avoiding":[122],"adverse":[123],"responses":[124],"external":[126],"infections.":[127],"Our":[128],"numerical":[129],"experiments":[130],"demonstrate":[131],"STL-based":[133],"can":[135],"solve":[136],"investigated":[138],"efficiently.":[143]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-01-14T00:00:00"}