Treffer: Exploring Chronic Rejection in Organ Transplantation Through Computational Modeling.
Title:
Exploring Chronic Rejection in Organ Transplantation Through Computational Modeling.
Authors:
Casarin S; Center for Precision Surgery, Houston Methodist Research Institute, Houston, TX, USA. scasarin@houstonmethodist.org.; LaSIE, UMR 7356 CNRS, La Rochelle Université, La Rochelle, France. scasarin@houstonmethodist.org.; Department of Surgery, Houston Methodist Hospital, Houston, TX, USA. scasarin@houstonmethodist.org., Serafini E; Center for Precision Surgery, Houston Methodist Research Institute, Houston, TX, USA.; LaSIE, UMR 7356 CNRS, La Rochelle Université, La Rochelle, France.
Source:
Results and problems in cell differentiation [Results Probl Cell Differ] 2026; Vol. 77, pp. 71-91.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 0173555 Publication Model: Print Cited Medium: Print ISSN: 0080-1844 (Print) Linking ISSN: 00801844 NLM ISO Abbreviation: Results Probl Cell Differ Subsets: MEDLINE
Imprint Name(s):
Original Publication: Berlin, New York, Springer-Verlag.
MeSH Terms:
References:
Actor JK (2019) Chapter 11 – Transplantation immunology. In: Actor JK (ed) Introductory immunology, 2nd edn. Academic Press, pp 133–142. (PMID: 10.1016/B978-0-12-816572-0.00011-5)
Alfaro R et al (2021) Computational prediction of biomarkers, pathways, and new target drugs in the pathogenesis of immune-based diseases regarding kidney transplantation rejection. Front Immunol 12:800968. (PMID: 34975915871474510.3389/fimmu.2021.800968)
An G (2015) Introduction of a framework for dynamic knowledge representation of the control structure of transplant immunology: employing the power of abstraction with a solid organ transplant agent-based model. Front Immunol 6:561. (PMID: 26594211463585310.3389/fimmu.2015.00561)
Arciero JC et al (2016) Combining theoretical and experimental techniques to study murine heart transplant rejection. Front Immunol 7:448. (PMID: 27872621509794010.3389/fimmu.2016.00448)
Baldwin WM 3rd, Valujskikh A, Fairchild RL (2016) Mechanisms of antibody-mediated acute and chronic rejection of kidney allografts. Curr Opin Organ Transplant 21(1):7–14. (PMID: 26575854470159710.1097/MOT.0000000000000262)
Black CK et al (2018) Solid organ transplantation in the 21(st) century. Ann Transl Med 6(20):409. (PMID: 30498736623086010.21037/atm.2018.09.68)
Bonin CR et al (2017) Mathematical modeling based on ordinary differential equations: a promising approach to vaccinology. Hum Vaccin Immunother 13(2):484–489. (PMID: 2802700210.1080/21645515.2017.1264774)
Chen S, Zhu H, Jounaidi Y (2024) Comprehensive snapshots of natural killer cells functions, signaling, molecular mechanisms and clinical utilization. Signal Transduct Target Ther 9(1):302. (PMID: 395111391154400410.1038/s41392-024-02005-w)
Colquitt RB, Colquhoun DA, Thiele RH (2011) In silico modelling of physiologic systems. Best Pract Res Clin Anaesthesiol 25(4):499–510. (PMID: 2209991610.1016/j.bpa.2011.08.006)
Colvin MM et al (2024) OPTN/SRTR 2022 annual data report: heart. Am J Transplant 24(2s1):S305–S393. (PMID: 3843136210.1016/j.ajt.2024.01.016)
De Gaetano A et al (2012) Modeling rejection immunity. Theor Biol Med Model 9:18. (PMID: 22607638354873010.1186/1742-4682-9-18)
Fayzullin A et al (2024) Towards accurate and efficient diagnoses in nephropathology: an AI-based approach for assessing kidney transplant rejection. Comput Struct Biotechnol J 24:571–582. (PMID: 392582381138506510.1016/j.csbj.2024.08.011)
Goldstein BA et al (2016) Assessment of heart transplant waitlist time and pre- and post-transplant failure: a mixed methods approach. Epidemiology 27(4):469–476. (PMID: 26928705489294110.1097/EDE.0000000000000472)
Hudaib A et al (2025) Exploring the implementation of federated learning in healthcare: a comprehensive review. Clust Comput 28(5):302. (PMID: 10.1007/s10586-024-05014-0)
Ingulli E (2010) Mechanism of cellular rejection in transplantation. Pediatr Nephrol 25(1):61–74. (PMID: 21476231277878510.1007/s00467-008-1020-x)
Khatri P et al (2013) A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation. J Exp Med 210(11):2205–2221. (PMID: 24127489380494110.1084/jem.20122709)
Kim MY, Brennan DC (2021) Therapies for chronic allograft rejection. Front Pharmacol 12:651222. (PMID: 33935762808245910.3389/fphar.2021.651222)
Kveton M et al (2024) Digital pathology in cardiac transplant diagnostics: from biopsies to algorithms. Cardiovasc Pathol 68:107587. (PMID: 3792635110.1016/j.carpath.2023.107587)
Kwong AJ et al (2024) OPTN/SRTR 2022 annual data report: liver. Am J Transplant 24(2s1):S176–S265. (PMID: 3843135910.1016/j.ajt.2024.01.014)
Lentine KL et al (2023) OPTN/SRTR 2021 annual data report: kidney. Am J Transplant 23(2 Suppl 1):S21–S120. (PMID: 37132350997036010.1016/j.ajt.2023.02.004)
Levitsky J et al (2022) Prediction of liver transplant rejection with a biologically relevant gene expression signature. Transplantation 106(5):1004–1011. (PMID: 3434296210.1097/TP.0000000000003895)
Lin Y et al (2021) Multi-omics network characterization reveals novel microRNA biomarkers and mechanisms for diagnosis and subtyping of kidney transplant rejection. J Transl Med 19(1):346. (PMID: 34389032836165510.1186/s12967-021-03025-8)
Modena BD et al (2016) Gene expression in biopsies of acute rejection and interstitial fibrosis/tubular atrophy reveals highly shared mechanisms that correlate with worse long-term outcomes. Am J Transplant 16(7):1982–1998. (PMID: 26990570550199010.1111/ajt.13728)
Moreau, A., et al., Effector mechanisms of rejection. Cold Spring Harb Perspect Med, 2013. 3(11) a015461.
Nakorchevsky A et al (2010) Molecular mechanisms of chronic kidney transplant rejection via large-scale proteogenomic analysis of tissue biopsies. J Am Soc Nephrol 21(2):362–373. (PMID: 20093355283454210.1681/ASN.2009060628)
Ningappa M et al (2022) A network-based approach to identify expression modules underlying rejection in pediatric liver transplantation. Cell Rep Med 3(4):100605. (PMID: 35492246904410210.1016/j.xcrm.2022.100605)
Olawade DB et al (2025) The impact of artificial intelligence and machine learning in organ retrieval and transplantation: a comprehensive review. Curr Res Transl Med 73(2):103493. (PMID: 39792149)
Perch M et al (2022) The International Thoracic Organ Transplant Registry of the International Society for Heart and Lung Transplantation: thirty-ninth adult lung transplantation report-2022; focus on lung transplant recipients with chronic obstructive pulmonary disease. J Heart Lung Transplant 41(10):1335–1347. (PMID: 360502061025798010.1016/j.healun.2022.08.007)
Peyster EG, Madabhushi A, Margulies KB (2018) Advanced morphologic analysis for diagnosing allograft rejection: the case of cardiac transplant rejection. Transplantation 102(8):1230. (PMID: 29570167605999810.1097/TP.0000000000002189)
Peyster EG et al (2021) An automated computational image analysis pipeline for histological grading of cardiac allograft rejection. Eur Heart J 42(24):2356–2369. (PMID: 33982079821672910.1093/eurheartj/ehab241)
Peyster EG et al (2025) Computational pathology assessments of cardiac stromal remodeling: clinical correlates and prognostic implications in heart transplantation. JHLT Open 7:100202. (PMID: 4014482210.1016/j.jhlto.2024.100202)
Pham MX et al (2010) Gene-expression profiling for rejection surveillance after cardiac transplantation. N Engl J Med 362(20):1890–1900. (PMID: 2041360210.1056/NEJMoa0912965)
Ramalhete L et al (2024) Revolutionizing kidney transplantation: connecting machine learning and artificial intelligence with next-generation healthcare—from algorithms to allografts. BioMedInformatics 4(1):673–689. (PMID: 10.3390/biomedinformatics4010037)
Sacreas A et al (2018) The common rejection module in chronic rejection post lung transplantation. PLoS One 13(10):e0205107. (PMID: 30289917617343410.1371/journal.pone.0205107)
Schapranow MP et al (2023) NephroCAGE-German-Canadian Consortium on AI for improved kidney transplantation outcome: protocol for an algorithm development and validation study. JMIR Res Protoc 12:e48892. (PMID: 381339151077079210.2196/48892)
Schettini F et al (2023) Computational reactive–diffusive modeling for stratification and prognosis determination of patients with breast cancer receiving Olaparib. Sci Rep 13(1):11951. (PMID: 374881541036614410.1038/s41598-023-38760-z)
Serafini E et al (2023) An agent-based model of cardiac allograft vasculopathy: toward a better understanding of chronic rejection dynamics. Front Bioeng Biotechnol 11:1190409. (PMID: 377715771052378610.3389/fbioe.2023.1190409)
Serafini E et al (2025) Investigating the relationship between geometry and hemodynamics in an experimentally derived murine coronary computational model. Comput Biol Med 187:109793. (PMID: 3993834110.1016/j.compbiomed.2025.109793)
Sigdel TK et al (2019a) A urinary Common Rejection Module (uCRM) score for non-invasive kidney transplant monitoring. PLoS One 14(7):e0220052. (PMID: 31365568666880210.1371/journal.pone.0220052)
Sigdel T et al (2019b) Assessment of 19 genes and validation of CRM gene panel for quantitative transcriptional analysis of molecular rejection and inflammation in archival kidney transplant biopsies. Front Med (Lausanne) 6:213. (PMID: 3163297610.3389/fmed.2019.00213)
Southern J et al (2008) Multi-scale computational modelling in biology and physiology. Prog Biophys Mol Biol 96(1):60–89. (PMID: 1788850210.1016/j.pbiomolbio.2007.07.019)
Stewart D, Mupfudze T, Klassen D (2023) Does anybody really know what (the kidney median waiting) time is? Am J Transplant 23(2):223–231. (PMID: 3669568810.1016/j.ajt.2022.12.005)
Teo ZL et al (2024) Federated machine learning in healthcare: a systematic review on clinical applications and technical architecture. Cell Rep Med 5(2):101419. (PMID: 383407281089762010.1016/j.xcrm.2024.101419)
Tong J et al (2024) Evaluating site-of-care-related racial disparities in kidney graft failure using a novel federated learning framework. J Am Med Inform Assoc 31(6):1303–1312. (PMID: 387130061110513210.1093/jamia/ocae075)
Tracy M, Cerdá M, Keyes KM (2018) Agent-based modeling in public health: current applications and future directions. Annu Rev Public Health 39:77–94. (PMID: 29328870593754410.1146/annurev-publhealth-040617-014317)
Trieu JA, Bilal M, Hmoud B (2018) Factors associated with waiting time on the liver transplant list: an analysis of the United Network for Organ Sharing (UNOS) database. Ann Gastroenterol 31(1):84–89. (PMID: 29333071)
Vandamme TF (2014) Use of rodents as models of human diseases. J Pharm Bioallied Sci 6(1):2–9. (PMID: 24459397389528910.4103/0975-7406.124301)
Verleden SE et al (2023) Biomarkers for chronic lung allograft dysfunction: ready for prime time? Transplantation 107(2):341–350. (PMID: 3598087810.1097/TP.0000000000004270)
Wang A, Sarwal MM (2015) Computational models for transplant biomarker discovery. Front Immunol 6:458. (PMID: 26441963456179810.3389/fimmu.2015.00458)
Wang Y et al (2023) Identification of PDCD1 as a potential biomarker in acute rejection after kidney transplantation via comprehensive bioinformatic analysis. Front Immunol 13:1076546. (PMID: 36776400991186810.3389/fimmu.2022.1076546)
Wood KJ, Goto R (2012) Mechanisms of rejection: current perspectives. Transplantation 93(1):1–10. (PMID: 2213881810.1097/TP.0b013e31823cab44)
Xia J, Gill EE, Hancock REW (2015) NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc 10(6):823–844. (PMID: 2595023610.1038/nprot.2015.052)
Xu Z et al (2024) Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation. Sci Rep 14(1):24770. (PMID: 394338681149396710.1038/s41598-024-75052-6)
Yen P et al (2011) A two-compartment model of VEGF distribution in the mouse. PLoS One 6(11):e27514. (PMID: 22087332321078810.1371/journal.pone.0027514)
Zarinsefat A et al (2020) Use of the tissue common rejection module score in kidney transplant as an objective measure of allograft inflammation. Front Immunol 11:614343. (PMID: 3361353910.3389/fimmu.2020.614343)
Zhang F et al (2024a) Recent methodological advances in federated learning for healthcare. Patterns 5(6):101006. (PMID: 390054851124017810.1016/j.patter.2024.101006)
Zhang C et al (2024b) Computational identification of novel potential genetic pathogenesis and otherwise biomarkers in acute liver allograft rejection. Heliyon 10(15):e33359. (PMID: 391701151133637110.1016/j.heliyon.2024.e33359)
Alfaro R et al (2021) Computational prediction of biomarkers, pathways, and new target drugs in the pathogenesis of immune-based diseases regarding kidney transplantation rejection. Front Immunol 12:800968. (PMID: 34975915871474510.3389/fimmu.2021.800968)
An G (2015) Introduction of a framework for dynamic knowledge representation of the control structure of transplant immunology: employing the power of abstraction with a solid organ transplant agent-based model. Front Immunol 6:561. (PMID: 26594211463585310.3389/fimmu.2015.00561)
Arciero JC et al (2016) Combining theoretical and experimental techniques to study murine heart transplant rejection. Front Immunol 7:448. (PMID: 27872621509794010.3389/fimmu.2016.00448)
Baldwin WM 3rd, Valujskikh A, Fairchild RL (2016) Mechanisms of antibody-mediated acute and chronic rejection of kidney allografts. Curr Opin Organ Transplant 21(1):7–14. (PMID: 26575854470159710.1097/MOT.0000000000000262)
Black CK et al (2018) Solid organ transplantation in the 21(st) century. Ann Transl Med 6(20):409. (PMID: 30498736623086010.21037/atm.2018.09.68)
Bonin CR et al (2017) Mathematical modeling based on ordinary differential equations: a promising approach to vaccinology. Hum Vaccin Immunother 13(2):484–489. (PMID: 2802700210.1080/21645515.2017.1264774)
Chen S, Zhu H, Jounaidi Y (2024) Comprehensive snapshots of natural killer cells functions, signaling, molecular mechanisms and clinical utilization. Signal Transduct Target Ther 9(1):302. (PMID: 395111391154400410.1038/s41392-024-02005-w)
Colquitt RB, Colquhoun DA, Thiele RH (2011) In silico modelling of physiologic systems. Best Pract Res Clin Anaesthesiol 25(4):499–510. (PMID: 2209991610.1016/j.bpa.2011.08.006)
Colvin MM et al (2024) OPTN/SRTR 2022 annual data report: heart. Am J Transplant 24(2s1):S305–S393. (PMID: 3843136210.1016/j.ajt.2024.01.016)
De Gaetano A et al (2012) Modeling rejection immunity. Theor Biol Med Model 9:18. (PMID: 22607638354873010.1186/1742-4682-9-18)
Fayzullin A et al (2024) Towards accurate and efficient diagnoses in nephropathology: an AI-based approach for assessing kidney transplant rejection. Comput Struct Biotechnol J 24:571–582. (PMID: 392582381138506510.1016/j.csbj.2024.08.011)
Goldstein BA et al (2016) Assessment of heart transplant waitlist time and pre- and post-transplant failure: a mixed methods approach. Epidemiology 27(4):469–476. (PMID: 26928705489294110.1097/EDE.0000000000000472)
Hudaib A et al (2025) Exploring the implementation of federated learning in healthcare: a comprehensive review. Clust Comput 28(5):302. (PMID: 10.1007/s10586-024-05014-0)
Ingulli E (2010) Mechanism of cellular rejection in transplantation. Pediatr Nephrol 25(1):61–74. (PMID: 21476231277878510.1007/s00467-008-1020-x)
Khatri P et al (2013) A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation. J Exp Med 210(11):2205–2221. (PMID: 24127489380494110.1084/jem.20122709)
Kim MY, Brennan DC (2021) Therapies for chronic allograft rejection. Front Pharmacol 12:651222. (PMID: 33935762808245910.3389/fphar.2021.651222)
Kveton M et al (2024) Digital pathology in cardiac transplant diagnostics: from biopsies to algorithms. Cardiovasc Pathol 68:107587. (PMID: 3792635110.1016/j.carpath.2023.107587)
Kwong AJ et al (2024) OPTN/SRTR 2022 annual data report: liver. Am J Transplant 24(2s1):S176–S265. (PMID: 3843135910.1016/j.ajt.2024.01.014)
Lentine KL et al (2023) OPTN/SRTR 2021 annual data report: kidney. Am J Transplant 23(2 Suppl 1):S21–S120. (PMID: 37132350997036010.1016/j.ajt.2023.02.004)
Levitsky J et al (2022) Prediction of liver transplant rejection with a biologically relevant gene expression signature. Transplantation 106(5):1004–1011. (PMID: 3434296210.1097/TP.0000000000003895)
Lin Y et al (2021) Multi-omics network characterization reveals novel microRNA biomarkers and mechanisms for diagnosis and subtyping of kidney transplant rejection. J Transl Med 19(1):346. (PMID: 34389032836165510.1186/s12967-021-03025-8)
Modena BD et al (2016) Gene expression in biopsies of acute rejection and interstitial fibrosis/tubular atrophy reveals highly shared mechanisms that correlate with worse long-term outcomes. Am J Transplant 16(7):1982–1998. (PMID: 26990570550199010.1111/ajt.13728)
Moreau, A., et al., Effector mechanisms of rejection. Cold Spring Harb Perspect Med, 2013. 3(11) a015461.
Nakorchevsky A et al (2010) Molecular mechanisms of chronic kidney transplant rejection via large-scale proteogenomic analysis of tissue biopsies. J Am Soc Nephrol 21(2):362–373. (PMID: 20093355283454210.1681/ASN.2009060628)
Ningappa M et al (2022) A network-based approach to identify expression modules underlying rejection in pediatric liver transplantation. Cell Rep Med 3(4):100605. (PMID: 35492246904410210.1016/j.xcrm.2022.100605)
Olawade DB et al (2025) The impact of artificial intelligence and machine learning in organ retrieval and transplantation: a comprehensive review. Curr Res Transl Med 73(2):103493. (PMID: 39792149)
Perch M et al (2022) The International Thoracic Organ Transplant Registry of the International Society for Heart and Lung Transplantation: thirty-ninth adult lung transplantation report-2022; focus on lung transplant recipients with chronic obstructive pulmonary disease. J Heart Lung Transplant 41(10):1335–1347. (PMID: 360502061025798010.1016/j.healun.2022.08.007)
Peyster EG, Madabhushi A, Margulies KB (2018) Advanced morphologic analysis for diagnosing allograft rejection: the case of cardiac transplant rejection. Transplantation 102(8):1230. (PMID: 29570167605999810.1097/TP.0000000000002189)
Peyster EG et al (2021) An automated computational image analysis pipeline for histological grading of cardiac allograft rejection. Eur Heart J 42(24):2356–2369. (PMID: 33982079821672910.1093/eurheartj/ehab241)
Peyster EG et al (2025) Computational pathology assessments of cardiac stromal remodeling: clinical correlates and prognostic implications in heart transplantation. JHLT Open 7:100202. (PMID: 4014482210.1016/j.jhlto.2024.100202)
Pham MX et al (2010) Gene-expression profiling for rejection surveillance after cardiac transplantation. N Engl J Med 362(20):1890–1900. (PMID: 2041360210.1056/NEJMoa0912965)
Ramalhete L et al (2024) Revolutionizing kidney transplantation: connecting machine learning and artificial intelligence with next-generation healthcare—from algorithms to allografts. BioMedInformatics 4(1):673–689. (PMID: 10.3390/biomedinformatics4010037)
Sacreas A et al (2018) The common rejection module in chronic rejection post lung transplantation. PLoS One 13(10):e0205107. (PMID: 30289917617343410.1371/journal.pone.0205107)
Schapranow MP et al (2023) NephroCAGE-German-Canadian Consortium on AI for improved kidney transplantation outcome: protocol for an algorithm development and validation study. JMIR Res Protoc 12:e48892. (PMID: 381339151077079210.2196/48892)
Schettini F et al (2023) Computational reactive–diffusive modeling for stratification and prognosis determination of patients with breast cancer receiving Olaparib. Sci Rep 13(1):11951. (PMID: 374881541036614410.1038/s41598-023-38760-z)
Serafini E et al (2023) An agent-based model of cardiac allograft vasculopathy: toward a better understanding of chronic rejection dynamics. Front Bioeng Biotechnol 11:1190409. (PMID: 377715771052378610.3389/fbioe.2023.1190409)
Serafini E et al (2025) Investigating the relationship between geometry and hemodynamics in an experimentally derived murine coronary computational model. Comput Biol Med 187:109793. (PMID: 3993834110.1016/j.compbiomed.2025.109793)
Sigdel TK et al (2019a) A urinary Common Rejection Module (uCRM) score for non-invasive kidney transplant monitoring. PLoS One 14(7):e0220052. (PMID: 31365568666880210.1371/journal.pone.0220052)
Sigdel T et al (2019b) Assessment of 19 genes and validation of CRM gene panel for quantitative transcriptional analysis of molecular rejection and inflammation in archival kidney transplant biopsies. Front Med (Lausanne) 6:213. (PMID: 3163297610.3389/fmed.2019.00213)
Southern J et al (2008) Multi-scale computational modelling in biology and physiology. Prog Biophys Mol Biol 96(1):60–89. (PMID: 1788850210.1016/j.pbiomolbio.2007.07.019)
Stewart D, Mupfudze T, Klassen D (2023) Does anybody really know what (the kidney median waiting) time is? Am J Transplant 23(2):223–231. (PMID: 3669568810.1016/j.ajt.2022.12.005)
Teo ZL et al (2024) Federated machine learning in healthcare: a systematic review on clinical applications and technical architecture. Cell Rep Med 5(2):101419. (PMID: 383407281089762010.1016/j.xcrm.2024.101419)
Tong J et al (2024) Evaluating site-of-care-related racial disparities in kidney graft failure using a novel federated learning framework. J Am Med Inform Assoc 31(6):1303–1312. (PMID: 387130061110513210.1093/jamia/ocae075)
Tracy M, Cerdá M, Keyes KM (2018) Agent-based modeling in public health: current applications and future directions. Annu Rev Public Health 39:77–94. (PMID: 29328870593754410.1146/annurev-publhealth-040617-014317)
Trieu JA, Bilal M, Hmoud B (2018) Factors associated with waiting time on the liver transplant list: an analysis of the United Network for Organ Sharing (UNOS) database. Ann Gastroenterol 31(1):84–89. (PMID: 29333071)
Vandamme TF (2014) Use of rodents as models of human diseases. J Pharm Bioallied Sci 6(1):2–9. (PMID: 24459397389528910.4103/0975-7406.124301)
Verleden SE et al (2023) Biomarkers for chronic lung allograft dysfunction: ready for prime time? Transplantation 107(2):341–350. (PMID: 3598087810.1097/TP.0000000000004270)
Wang A, Sarwal MM (2015) Computational models for transplant biomarker discovery. Front Immunol 6:458. (PMID: 26441963456179810.3389/fimmu.2015.00458)
Wang Y et al (2023) Identification of PDCD1 as a potential biomarker in acute rejection after kidney transplantation via comprehensive bioinformatic analysis. Front Immunol 13:1076546. (PMID: 36776400991186810.3389/fimmu.2022.1076546)
Wood KJ, Goto R (2012) Mechanisms of rejection: current perspectives. Transplantation 93(1):1–10. (PMID: 2213881810.1097/TP.0b013e31823cab44)
Xia J, Gill EE, Hancock REW (2015) NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc 10(6):823–844. (PMID: 2595023610.1038/nprot.2015.052)
Xu Z et al (2024) Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation. Sci Rep 14(1):24770. (PMID: 394338681149396710.1038/s41598-024-75052-6)
Yen P et al (2011) A two-compartment model of VEGF distribution in the mouse. PLoS One 6(11):e27514. (PMID: 22087332321078810.1371/journal.pone.0027514)
Zarinsefat A et al (2020) Use of the tissue common rejection module score in kidney transplant as an objective measure of allograft inflammation. Front Immunol 11:614343. (PMID: 3361353910.3389/fimmu.2020.614343)
Zhang F et al (2024a) Recent methodological advances in federated learning for healthcare. Patterns 5(6):101006. (PMID: 390054851124017810.1016/j.patter.2024.101006)
Zhang C et al (2024b) Computational identification of novel potential genetic pathogenesis and otherwise biomarkers in acute liver allograft rejection. Heliyon 10(15):e33359. (PMID: 391701151133637110.1016/j.heliyon.2024.e33359)
Contributed Indexing:
Keywords: Chronic rejection; Computational modeling; Precision medicine; Solid organ transplantation
Entry Date(s):
Date Created: 20260101 Date Completed: 20260102 Latest Revision: 20260101
Update Code:
20260102
DOI:
10.1007/978-3-032-07686-1_3
PMID:
41479021
Database:
MEDLINE
Weitere Informationen
Chronic rejection remains a significant challenge in solid organ transplantation, contributing to graft dysfunction and eventual failure despite advances in immunosuppressive therapies. Computational modeling has emerged as a powerful tool for understanding chronic rejection mechanisms, enhancing diagnostic precision, and identifying novel therapeutic targets. This chapter explores various computational approaches, including artificial intelligence, machine learning, ordinary differential equations, partial differential equations, agent-based models, and gene network analysis applied to solid organ transplantation: kidney, liver, heart, and lung. While computational models offer numerous advantages, including cost-effectiveness and the ability to integrate multi-omics data, challenges remain in terms of data quality, standardization, and clinical validation. Bridging these gaps will require comprehensive longitudinal studies and the development of hybrid models that combine diverse computational techniques. Emerging technologies such as single-cell transcriptomics and spatial genomics hold promise for enhancing predictive accuracy and understanding cellular heterogeneity. As computational methods evolve, their integration with experimental research will be essential for developing precision medicine strategies to improve long-term graft survival.
(© 2026. The Author(s), under exclusive license to Springer Nature Switzerland AG.)