Donor-specific digital twin for living donor liver transplant recovery.

IF 1.3 Q3 BIOCHEMICAL RESEARCH METHODS
Biology Methods and Protocols Pub Date : 2025-05-10 eCollection Date: 2025-01-01 DOI:10.1093/biomethods/bpaf037
Suvankar Halder, Michael C Lawrence, Giuliano Testa, Vipul Periwal
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Abstract

The remarkable capacity of the liver to regenerate its lost mass after resection makes living donor liver transplantation a successful treatment option. However, donor heterogeneity significantly influences recovery trajectories, highlighting the need for individualized monitoring. With the rising incidence of liver diseases, safer transplant procedures and improved donor care are urgently needed. Current clinical markers provide only limited snapshots of recovery, making it challenging to predict long-term outcomes. Following partial hepatectomy, precise liver mass recovery requires tightly regulated hepatocyte proliferation. We identified distinct gene expression patterns associated with liver regeneration by analyzing blood-derived gene expression measurements from twelve donors followed over a year. Using a deep learning-based framework, we integrated these patterns with a mathematical model of hepatocyte transitions to develop a personalized, progressive mechanistic digital twin-a virtual liver model that predicts donor-specific recovery trajectories. Central to our approach is a mechanistically identifiable latent space, defined by variables derived from a physiologically grounded differential equation model of liver regeneration, which enables biologically interpretable, bidirectional mapping between gene expression data and model dynamics. This approach integrates clinical genomics and computational modeling to enhance post-surgical care, ensuring safer transplants and improved donor recovery.

用于活体肝移植恢复的供体特异性数字双胞胎。
肝脏在切除后再生其失去的肿块的显著能力使活体供体肝移植成为一种成功的治疗选择。然而,供体的异质性显著影响恢复轨迹,突出了个性化监测的必要性。随着肝病发病率的上升,迫切需要更安全的移植程序和更好的供体护理。目前的临床指标只能提供有限的恢复快照,这使得预测长期结果具有挑战性。肝部分切除术后,精确的肝肿块恢复需要严格调节肝细胞增殖。我们通过分析12名供者一年多的血液来源基因表达测量,确定了与肝脏再生相关的不同基因表达模式。使用基于深度学习的框架,我们将这些模式与肝细胞转化的数学模型结合起来,开发出个性化的、渐进的机械数字双胞胎——一个预测供体特异性恢复轨迹的虚拟肝脏模型。我们的方法的核心是一个机制上可识别的潜在空间,由肝脏再生的生理基础微分方程模型衍生的变量定义,这使得基因表达数据和模型动力学之间的生物学可解释的双向映射成为可能。这种方法结合了临床基因组学和计算模型,以加强术后护理,确保更安全的移植和改善供体恢复。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biology Methods and Protocols
Biology Methods and Protocols Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
3.80
自引率
2.80%
发文量
28
审稿时长
19 weeks
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