{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T03:02:17Z","timestamp":1770519737943,"version":"3.49.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"S17","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T00:00:00Z","timestamp":1607904000000},"content-version":"vor","delay-in-days":13,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007364","name":"Fondazione CRT","doi-asserted-by":"publisher","award":["2019.2292"],"award-info":[{"award-number":["2019.2292"]}],"id":[{"id":"10.13039\/100007364","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fondi di Ricerca Locale, Univ. degli Studi di Torino","award":["BECM_RILO_19_01"],"award-info":[{"award-number":["BECM_RILO_19_01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Multiple Sclerosis (MS) represents nowadays in Europe the leading cause of non-traumatic disabilities in young adults, with more than 700,000 EU cases. Although huge strides have been made over the years, MS etiology remains partially unknown. Furthermore, the presence of various endogenous and exogenous factors can greatly influence the immune response of different individuals, making it difficult to study and understand the disease. This becomes more evident in a personalized-fashion when medical doctors have to choose the best therapy for patient well-being. In this optics, the use of stochastic models, capable of taking into consideration all the fluctuations due to unknown factors and individual variability, is highly advisable.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We propose a new model to study the immune response in relapsing remitting MS (RRMS), the most common form of MS that is characterized by alternate episodes of symptom exacerbation (relapses) with periods of disease stability (remission). In this new model, both the peripheral lymph node\/blood vessel and the central nervous system are explicitly represented. The model was created and analysed using <jats:italic>Epimod<\/jats:italic>, our recently developed general framework for modeling complex biological systems. Then the effectiveness of our model was shown by modeling the complex immunological mechanisms characterizing RRMS during its course and under the DAC administration.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Simulation results have proven the ability of the model to reproduce in silico the immune T cell balance characterizing RRMS course and the DAC effects. Furthermore, they confirmed the importance of a timely intervention on the disease course.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-020-03823-9","type":"journal-article","created":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T01:02:38Z","timestamp":1607907758000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Computational modeling of the immune response in multiple sclerosis using epimod framework"],"prefix":"10.1186","volume":"21","author":[{"given":"Simone","family":"Pernice","sequence":"first","affiliation":[]},{"given":"Laura","family":"Follia","sequence":"additional","affiliation":[]},{"given":"Alessandro","family":"Maglione","sequence":"additional","affiliation":[]},{"given":"Marzio","family":"Pennisi","sequence":"additional","affiliation":[]},{"given":"Francesco","family":"Pappalardo","sequence":"additional","affiliation":[]},{"given":"Francesco","family":"Novelli","sequence":"additional","affiliation":[]},{"given":"Marinella","family":"Clerico","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6125-9460","authenticated-orcid":false,"given":"Marco","family":"Beccuti","sequence":"additional","affiliation":[]},{"given":"Francesca","family":"Cordero","sequence":"additional","affiliation":[]},{"given":"Simona","family":"Rolla","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,12,14]]},"reference":[{"issue":"1","key":"3823_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.pneurobio.2010.09.005","volume":"93","author":"R Dutta","year":"2011","unstructured":"Dutta R, Trapp BD. Mechanisms of neuronal dysfunction and degeneration in multiple sclerosis. Prog Neurobiol. 2011;93(1):1\u201312.","journal-title":"Prog Neurobiol"},{"issue":"2","key":"3823_CR2","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1084\/jem.20041257","volume":"201","author":"CL Langrish","year":"2005","unstructured":"Langrish CL, Chen Y, Blumenschein WM, Mattson J, Basham B, Sedgwick JD, McClanahan T, Kastelein RA, Cua DJ. Il-23 drives a pathogenic t cell population that induces autoimmune inflammation. J Exp Med. 2005;201(2):233\u201340.","journal-title":"J Exp Med"},{"issue":"10","key":"3823_CR3","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1038\/nm1651","volume":"13","author":"H Kebir","year":"2007","unstructured":"Kebir H, Kreymborg K, Ifergan I, Dodelet-Devillers A, Cayrol R, Bernard M, Giuliani F, Arbour N, Becher B, Prat A. Human th 17 lymphocytes promote blood-brain barrier disruption and central nervous system inflammation. Nat Med. 2007;13(10):1173\u20135.","journal-title":"Nat Med"},{"issue":"5","key":"3823_CR4","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1002\/ana.21652","volume":"65","author":"L Durelli","year":"2009","unstructured":"Durelli L, Conti L, Clerico M, Boselli D, Contessa G, Ripellino P, Ferrero B, Eid P, Novelli F. T-helper 17 cells expand in multiple sclerosis and are inhibited by interferon-\u03b2. Ann Neurol. 2009;65(5):499\u2013509.","journal-title":"Ann Neurol"},{"issue":"6","key":"3823_CR5","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1189\/jlb.5A0813-463RR","volume":"96","author":"S Rolla","year":"2014","unstructured":"Rolla S, Bardina V, De Mercanti S, Quaglino P, De Palma R, Gned D, Brusa D, Durelli L, Novelli F, Clerico M. Th22 cells are expanded in multiple sclerosis and are resistant to ifn-\u03b2. J Leukocyte Biol. 2014;96(6):1155\u201364.","journal-title":"J Leukocyte Biol"},{"issue":"1","key":"3823_CR6","doi-asserted-by":"publisher","first-page":"146","DOI":"10.2353\/ajpath.2008.070690","volume":"172","author":"JS Tzartos","year":"2008","unstructured":"Tzartos JS, Friese MA, Craner MJ, Palace J, Newcombe J, Esiri MM, Fugger L. Interleukin-17 production in central nervous system-infiltrating t cells and glial cells is associated with active disease in multiple sclerosis. Am J Pathol. 2008;172(1):146\u201355.","journal-title":"Am J Pathol"},{"issue":"7","key":"3823_CR7","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1038\/ncpneuro0832","volume":"4","author":"AL Zozulya","year":"2008","unstructured":"Zozulya AL, Wiendl H. The role of regulatory t cells in multiple sclerosis. Nat Clin Pract Neurol. 2008;4(7):384\u201398.","journal-title":"Nat Clin Pract Neurol"},{"key":"3823_CR8","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.1016\/S0140-6736(08)61620-7","volume":"372","author":"A Compston","year":"2008","unstructured":"Compston A, Coles A. Multiple sclerosis. Lancet (Lond, Engl). 2008;372:1502\u201317.","journal-title":"Lancet (Lond, Engl)"},{"issue":"9","key":"3823_CR9","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1038\/nri3871","volume":"15","author":"CA Dendrou","year":"2015","unstructured":"Dendrou CA, Fugger L, Friese MA. Immunopathology of multiple sclerosis. Nat Rev Immunol. 2015;15(9):545\u201358.","journal-title":"Nat Rev Immunol"},{"key":"3823_CR10","first-page":"9","volume":"11","author":"SI Ahmed","year":"2019","unstructured":"Ahmed SI, Aziz K, Gul A, Samar SS, Bareeqa SB. Risk of multiple sclerosis in epstein-barr virus infection. Cureus. 2019;11:9.","journal-title":"Cureus"},{"issue":"4","key":"3823_CR11","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/s40265-017-0708-2","volume":"77","author":"M Shirley","year":"2017","unstructured":"Shirley M. Daclizumab: a review in relapsing multiple sclerosis. Drugs. 2017;77(4):447\u201358.","journal-title":"Drugs"},{"key":"3823_CR12","doi-asserted-by":"crossref","unstructured":"Gold R, Radue E-W, Giovannoni G, Selmaj K, Havrdova EK, Montalban X, Stefoski D, Sprenger T, Robinson RR, Fam10 S. et al. Long-term safety and efficacy of daclizumab beta in relapsing\u2013remitting multiple sclerosis: 6-year results from the selected open-label extension study. J Neurol. 2020.","DOI":"10.1007\/s00415-020-09835-y"},{"key":"3823_CR13","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1186\/1752-0509-5-114","volume":"5","author":"N V\u00e9lez de Mendiz\u00e1bal","year":"2011","unstructured":"V\u00e9lez de Mendiz\u00e1bal N, Carneiro J, Sol\u00e9 RV, Go\u00f1i J, Bragard J, Martinez-Forero I, Martinez-Pasamar S, Sepulcre J, Torrealdea J, Bagnato F, Garcia-Ojalvo J, Villoslada P. Modeling the effector-Regulatory T cell cross-regulation reveals the intrinsic character of relapses in Multiple Sclerosis. BMC Syst Biol. 2011;5:114.","journal-title":"BMC Syst Biol"},{"issue":"Suppl 16","key":"3823_CR14","doi-asserted-by":"publisher","first-page":"S9","DOI":"10.1186\/1471-2105-14-S16-S9","volume":"14","author":"M Pennisi","year":"2013","unstructured":"Pennisi M, Rajput AM, Toldo L, Pappalardo F. Agent based modeling of treg-teff cross regulation in relapsing-remitting multiple sclerosis. BMC Bioinf. 2013;14(Suppl 16):S9.","journal-title":"BMC Bioinf"},{"key":"3823_CR15","unstructured":"Pappalardo F, Pennisi M, Rajput A-M, Chiacchio F, Motta S. Relapsing-remitting multiple scleroris and the role of vitamin D: an agent based model. In: ACM-BCB, 2014; pp. 744\u2013748"},{"key":"3823_CR16","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/j.jim.2015.08.014","volume":"427","author":"M Pennisi","year":"2015","unstructured":"Pennisi M, Russo G, Motta S, Pappalardo F. Agent based modeling of the effects of potential treatments over the blood-brain barrier in multiple sclerosis. J Immunol Methods. 2015;427:6.","journal-title":"J Immunol Methods"},{"key":"3823_CR17","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s12859-020-03648-6","volume":"21","author":"P Castagno","year":"2020","unstructured":"Castagno P, Pernice S, Ghetti G, Povero M, Pradelli L, Paolotti D, Balbo G, Sereno M, Beccuti M. A computational framework for modeling and studying pertussis epidemiology and vaccination. BMC Bioinf. 2020;21:16.","journal-title":"BMC Bioinf"},{"issue":"6","key":"3823_CR18","first-page":"1","volume":"20","author":"S Pernice","year":"2019","unstructured":"Pernice S, Pennisi M, Romano G, Maglione A, Cutrupi S, Pappalardo F, Balbo G, Beccuti M, Cordero F, Calogero RA. A computational approach based on the colored petri net formalism for studying multiple sclerosis. BMC Bioinf. 2019;20(6):1\u201317.","journal-title":"BMC Bioinf"},{"key":"3823_CR19","unstructured":"Pernice S, Beccuti M, Do\u2019 P, Pennisi M, Pappalardo F. Estimating daclizumab effects in multiple sclerosis using stochastic symmetric nets. In: IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, December 3\u20136, 2018, 2018; pp. 1393\u20131400."},{"key":"3823_CR20","doi-asserted-by":"crossref","unstructured":"Passos GRD, Sato DK, Becker J, Fujihara K. Th17 cells pathways in multiple sclerosis and neuromyelitis optica spectrum disorders: pathophysiological and therapeutic implications. Med Inflam. 2016;2016:","DOI":"10.1155\/2016\/5314541"},{"issue":"25","key":"3823_CR21","doi-asserted-by":"publisher","first-page":"2340","DOI":"10.1021\/j100540a008","volume":"81","author":"DT Gillespie","year":"1977","unstructured":"Gillespie DT. Exact stochastic simulation of coupled chemical reactions. J Phys Chem. 1977;81(25):2340\u201361.","journal-title":"J Phys Chem"},{"key":"3823_CR22","volume-title":"Modelling with generalized stochastic petri nets","author":"MA Marsan","year":"1995","unstructured":"Marsan MA, Balbo G, Conte G, Donatelli S, Franceschinis G. Modelling with generalized stochastic petri nets. New York: Wiley; 1995."},{"key":"3823_CR23","unstructured":"Pernice S, Follia L, Balbo G, Sartini G, Totis N, Li\u00f3 P, Merelli I, Cordero F, Beccuti M. Integrating petri nets and flux balance methods in computational biology models: a methodological and computational practice. Fund Inf, To be published; 2019."},{"issue":"9","key":"3823_CR24","doi-asserted-by":"publisher","first-page":"1876","DOI":"10.1021\/jp993732q","volume":"104","author":"MA Gibson","year":"2000","unstructured":"Gibson MA, Bruck J. Efficient exact stochastic simulation of chemical systems with many species and many channels. J Phys Chem A. 2000;104(9):1876\u201389.","journal-title":"J Phys Chem A"},{"issue":"4","key":"3823_CR25","doi-asserted-by":"publisher","first-page":"1716","DOI":"10.1063\/1.1378322","volume":"115","author":"DT Gillespie","year":"2001","unstructured":"Gillespie DT. Approximate accelerated stochastic simulation of chemically reacting systems. J Chem Phys. 2001;115(4):1716\u201333.","journal-title":"J Chem Phys"},{"issue":"9","key":"3823_CR26","doi-asserted-by":"publisher","first-page":"4059","DOI":"10.1063\/1.1778376","volume":"121","author":"Y Cao","year":"2004","unstructured":"Cao Y, Li H, Petzold L. Efficient formulation of the stochastic simulation algorithm for chemically reacting systems. J Chem Phys. 2004;121(9):4059\u201367.","journal-title":"J Chem Phys"},{"key":"3823_CR27","doi-asserted-by":"crossref","unstructured":"Amparore EG, Balbo G, Beccuti M, Donatelli S, Franceschinis G. 30 years of GreatSPN. In: Principles of performance and reliability modeling and evaluation, pp. 227\u2013254. Springer, Berlin; 2016","DOI":"10.1007\/978-3-319-30599-8_9"},{"issue":"4","key":"3823_CR28","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1111\/j.1365-2567.2008.03027.x","volume":"126","author":"A Poli","year":"2009","unstructured":"Poli A, Michel T, Th\u00e9r\u00e9sine M, Andr\u00e8s E, Hentges F, Zimmer J. Cd56bright natural killer (nk) cells: an important nk cell subset. Immunology. 2009;126(4):458\u201365.","journal-title":"Immunology"},{"issue":"5","key":"3823_CR29","doi-asserted-by":"publisher","first-page":"1450","DOI":"10.3390\/jcm9051450","volume":"9","author":"A Laroni","year":"2020","unstructured":"Laroni A, Uccelli A. Cd56bright natural killer cells: a possible biomarker of different treatments in multiple sclerosis. J Clin Med. 2020;9(5):1450.","journal-title":"J Clin Med"},{"issue":"3","key":"3823_CR30","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.molmed.2019.11.003","volume":"26","author":"A Bar-Or","year":"2020","unstructured":"Bar-Or A, Pender MP, Khanna R, Steinman L, Hartung H-P, Maniar T, Croze E, Aftab BT, Giovannoni G, Joshi MA. Epstein-barr virus in multiple sclerosis: theory and emerging immunotherapies. Trends Mol Med. 2020;26(3):296\u2013310.","journal-title":"Trends Mol Med"},{"issue":"2","key":"3823_CR31","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1177\/1352458517751049","volume":"24","author":"X Montalban","year":"2018","unstructured":"Montalban X, Gold R, Thompson AJ, Otero-Romero S, Amato MP, Chandraratna D, Clanet M, Comi G, Derfuss T, Fazekas F, et al. Ectrims\/ean guideline on the pharmacological treatment of people with multiple sclerosis. Multiple Scler J. 2018;24(2):96\u2013120.","journal-title":"Multiple Scler J"},{"issue":"6","key":"3823_CR32","doi-asserted-by":"publisher","first-page":"1396","DOI":"10.3390\/cells9061396","volume":"9","author":"S Rolla","year":"2020","unstructured":"Rolla S, Maglione A, De Mercanti SF, Clerico M. The meaning of immune reconstitution after alemtuzumab therapy in multiple sclerosis. Cells. 2020;9(6):1396.","journal-title":"Cells"},{"key":"3823_CR33","doi-asserted-by":"publisher","first-page":"520","DOI":"10.3389\/fimmu.2015.00520","volume":"6","author":"AJ Steelman","year":"2015","unstructured":"Steelman AJ. Infection as an environmental trigger of multiple sclerosis disease exacerbation. Front Immunol. 2015;6:520.","journal-title":"Front Immunol"},{"issue":"5","key":"3823_CR34","first-page":"528","volume":"11","author":"J Oskari Virtanen","year":"2012","unstructured":"Oskari Virtanen J, Jacobson S. CNS & neurological disorders-drug targets (formerly current drug targets-CNS & neurological disorders). Viruses Multiple Scler. 2012;11(5):528\u201344.","journal-title":"Viruses Multiple Scler"},{"key":"3823_CR35","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/S0074-7742(07)79006-2","volume":"79","author":"JE Libbey","year":"2007","unstructured":"Libbey JE, McCoy LL, Fujinami RS. Molecular mimicry in multiple sclerosis. Int Rev Neurobiol. 2007;79:127\u201347.","journal-title":"Int Rev Neurobiol"},{"issue":"1","key":"3823_CR36","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1111\/j.1365-2567.2008.02813.x","volume":"124","author":"DK Sojka","year":"2008","unstructured":"Sojka DK, Huang Y-H, Fowell DJ. Mechanisms of regulatory t-cell suppression-a diverse arsenal for a moving target. Immunology. 2008;124(1):13\u201322.","journal-title":"Immunology"},{"key":"3823_CR37","doi-asserted-by":"crossref","unstructured":"Gharibi T, Babaloo Z, Hosseini A, Marofi F, Ebrahimi-kalan A, Jahandideh S, Baradaran B. The role of b cells in the immunopathogenesis of multiple sclerosis. Immunology. 2020;.","DOI":"10.1111\/imm.13198"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03823-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-020-03823-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03823-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T01:05:05Z","timestamp":1607907905000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-03823-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12]]},"references-count":37,"journal-issue":{"issue":"S17","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["3823"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-03823-9","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12]]},"assertion":[{"value":"14 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"550"}}