Mathematical modelling with Bayesian inference to quantitatively characterize therapeutic cell behaviour in nerve tissue engineering.
Mathematical modelling with Bayesian inference to quantitatively characterize therapeutic cell behaviour in nerve tissue engineering.
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DOI：
10.1098/rsif.2023.0258
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发表时间：
2023-09
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细胞工程化神经组织在改善周围神经修复策略方面具有重要的潜力。传统的方法依赖于使用隔离实验来量化组织行为，这对组织设计的总体框架提出了挑战。相比之下，以实验数据为基准的数学细胞-溶质模型能够进行计算实验，以测试生物/生物物理机制的作用，并探索不同设计情景的影响，从而加速开发新的处理战略。这种模型通常由一组依赖于一些参数和函数形式的连续的、耦合的偏微分方程式组成。它们需要专门的体外实验才能得到信息，而这些实验很少获得，而且往往涉及时空分辨率有限的小数据集，从而产生不确定性。我们解决了这个问题，并提出了一种基于贝叶斯推理的管道，使得能够推导出描述神经组织工程中治疗细胞行为的实验信息的细胞-溶质模型。我们将我们的管道应用于三种相关的细胞类型，并获得可以容易地用于模拟神经修复情景和量化比较治疗细胞的模型。除了参数估计之外，拟议的管道还可以进行模型选择和实验效用量化，目的是改进模型制定和实验设计。
Cellular engineered neural tissues have significant potential to improve peripheral nerve repair strategies. Traditional approaches depend on quantifying tissue behaviours using experiments in isolation, presenting a challenge for an overarching framework for tissue design. By comparison, mathematical cell–solute models benchmarked against experimental data enable computational experiments to be performed to test the role of biological/biophysical mechanisms, as well as to explore the impact of different design scenarios and thus accelerate the development of new treatment strategies. Such models generally consist of a set of continuous, coupled, partial differential equations relying on a number of parameters and functional forms. They necessitate dedicated in vitro experiments to be informed, which are seldom available and often involve small datasets with limited spatio-temporal resolution, generating uncertainties. We address this issue and propose a pipeline based on Bayesian inference enabling the derivation of experimentally informed cell–solute models describing therapeutic cell behaviour in nerve tissue engineering. We apply our pipeline to three relevant cell types and obtain models that can readily be used to simulate nerve repair scenarios and quantitatively compare therapeutic cells. Beyond parameter estimation, the proposed pipeline enables model selection as well as experiment utility quantification, aimed at improving both model formulation and experimental design.
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影响因子：
4.3
作者：
Coy R;Berg M;Phillips JB;Shipley RJ
通讯作者：
Shipley RJ
影响因子：
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Campolongo, Francesca;Cariboni, Jessica;Saltelli, Andrea
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Saltelli, Andrea
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Christofk, Heather R.
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作者：
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通讯作者：
Wu, BM