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Abstract：
Computational mechanistic models constitute powerful tools for summarizing our knowledge in quantitative terms, providing mechanistic understanding, and generating new hypotheses. The present review emphasizes the advantages of reusing publicly available computational models as a way to capitalize on existing knowledge, reduce the number of parameters that need to be adjusted to experimental data, and facilitate hypothesis generation. Finally, it includes a step-by-step example of the reuse and adaptation of an existing model of immune responses to tuberculosis, tumor growth, and blood pathogens, to study donor-specific antibody (DSA) responses. This review aims to illustrate the benefit of leveraging the currently available computational models in immunology to accelerate the study of alloimmune responses, and to encourage modelers to share their models to further advance our understanding of transplant immunology.
摘要：
计算机械模型构成了以定量方式总结我们知识的强大工具，提供机械理解，并产生新的假设。本综述强调了重用公开可用的计算模型作为利用现有知识的一种方式的优势，减少需要根据实验数据调整的参数数量，并促进假设的产生。最后,它包括一个逐步重复使用和适应现有结核病免疫反应模型的例子，肿瘤生长，和血液病原体，研究供体特异性抗体（DSA）反应。这篇综述旨在说明利用免疫学中当前可用的计算模型来加速同种免疫反应研究的好处。并鼓励建模者分享他们的模型，以进一步促进我们对移植免疫学的理解。
