{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T05:43:52Z","timestamp":1757310232443,"version":"3.37.3"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"22","license":[{"start":{"date-parts":[[2019,4,17]],"date-time":"2019-04-17T00:00:00Z","timestamp":1555459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nicholson Fellowship"},{"name":"Biotechnology Training Program Fellowship","award":["T32 GM008449"],"award-info":[{"award-number":["T32 GM008449"]}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["CBET-1653315"],"award-info":[{"award-number":["CBET-1653315"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NU-CCNE","award":["U54 CA199091-03"],"award-info":[{"award-number":["U54 CA199091-03"]}]},{"DOI":"10.13039\/100016199","name":"McCormick School of Engineering","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100016199","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>To understand the regulatory pathways underlying diseases, studies often investigate the differential gene expression between genetically or chemically differing cell populations. Differential expression analysis identifies global changes in transcription and enables the inference of functional roles of applied perturbations. This approach has transformed the discovery of genetic drivers of disease and possible therapies. However, differential expression analysis does not provide quantitative predictions of gene expression in untested conditions. We present a hybrid approach, termed Differential Expression in Python (DiffExPy), that uniquely combines discrete, differential expression analysis with in silico differential equation simulations to yield accurate, quantitative predictions of gene expression from time-series data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To demonstrate the distinct insight provided by DiffExpy, we applied it to published, in vitro, time-series RNA-seq data from several genetic PI3K\/PTEN variants of MCF10a cells stimulated with epidermal growth factor. DiffExPy proposed ensembles of several minimal differential equation systems for each differentially expressed gene. These systems provide quantitative models of expression for several previously uncharacterized genes and uncover new regulation by the PI3K\/PTEN pathways. We validated model predictions on expression data from conditions that were not used for model training. Our discrete, differential expression analysis also identified SUZ12 and FOXA1 as possible regulators of specific groups of genes that exhibit late changes in expression. Our work reveals how DiffExPy generates quantitatively predictive models with testable, biological hypotheses from time-series expression data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>DiffExPy is available on GitHub (https:\/\/github.com\/bagherilab\/diffexpy).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz256","type":"journal-article","created":{"date-parts":[[2019,4,12]],"date-time":"2019-04-12T11:25:41Z","timestamp":1555068341000},"page":"4671-4678","source":"Crossref","is-referenced-by-count":3,"title":["Hybrid analysis of gene dynamics predicts context-specific expression and offers regulatory insights"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7296-9855","authenticated-orcid":false,"given":"Justin D","family":"Finkle","sequence":"first","affiliation":[{"name":"Interdisciplinary Biological Sciences, Northwestern University , Evanston, IL 60208, USA"}]},{"given":"Neda","family":"Bagheri","sequence":"additional","affiliation":[{"name":"Interdisciplinary Biological Sciences, Northwestern University , Evanston, IL 60208, USA"},{"name":"Department of Chemical and Biological Engineering, Northwestern University , Evanston, IL 60208, USA"},{"name":"Center for Synthetic Biology, Northwestern University , Evanston, IL 60208, USA"},{"name":"Chemistry of Life Processes, Northwestern University , Evanston, IL 60208, USA"}]}],"member":"286","published-online":{"date-parts":[[2019,4,17]]},"reference":[{"key":"2023013108314175000_btz256-B1","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1093\/bioinformatics\/btl003","article-title":"Inference of gene regulatory networks and compound mode of action from time course gene expression profiles","volume":"22","author":"Bansal","year":"2006","journal-title":"Bioinformatics"},{"key":"2023013108314175000_btz256-B2","doi-asserted-by":"crossref","first-page":"554.","DOI":"10.1038\/onc.2012.62","article-title":"Foxa1 represses the molecular phenotype of basal breast cancer cells","volume":"32","author":"Bernardo","year":"2013","journal-title":"Oncogene"},{"key":"2023013108314175000_btz256-B3","doi-asserted-by":"crossref","first-page":"803.","DOI":"10.1038\/nrc.2016.83","article-title":"Maintaining cell identity: PRC2-mediated regulation of transcription and cancer","volume":"16","author":"Comet","year":"2016","journal-title":"Nat Rev. 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