A Bayesian Sequential Learning Framework to Parameterise Continuum Models of Melanoma Invasion into Human Skin
A Bayesian Sequential Learning Framework to Parameterise Continuum Models of Melanoma Invasion into Human Skin
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DOI：
10.1007/s11538-018-0532-1
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发表时间：
2019-03-01
影响因子：
3.5
通讯作者：
Simpson, Matthew J.
中科院分区：
文献类型：
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作者：
Browning, Alexander P.;Haridas, Parvathi;Simpson, Matthew J.
我们提出了一个新的框架来参数化细胞入侵的数学模型，该模型描述了一群黑色素瘤细胞如何入侵人类皮肤组织。使用从复杂的实验图像中提取的简单实验数据，我们估计三个模型参数：（i）黑素瘤细胞增殖率;（ii）黑素瘤细胞扩散率D;以及（iii）确定黑素瘤细胞降解皮肤组织的速率的常数。贝叶斯序贯学习框架涉及一系列越来越复杂的实验数据，这些数据来自：（i）空间均匀的细胞增殖测定;（ii）二维圆形屏障测定;和（iii）三维侵入测定。贝叶斯序贯学习方法导致定义良好的参数估计。相比之下，采取尝试从同一实验的单个图像集合估计所有参数的天真方法无法产生有意义的结果。总的来说，我们的推理方法是简单的实现，计算效率高，非常适合于许多细胞生物学现象，可以描述低维连续模型，使用常微分方程和偏微分方程。我们预计，这种贝叶斯顺序学习框架将在其他生物背景下，它是具有挑战性的提取详细的，定量的生物测量从实验图像，所以我们必须依赖于使用相对简单的测量从复杂的图像相关。
We present a novel framework to parameterise a mathematical model of cell invasion that describes how a population of melanoma cells invades into human skin tissue. Using simple experimental data extracted from complex experimental images, we estimate three model parameters: (i) the melanoma cell proliferation rate, ; (ii) the melanoma cell diffusivity, D; and (iii) , a constant that determines the rate that melanoma cells degrade the skin tissue. The Bayesian sequential learning framework involves a sequence of increasingly sophisticated experimental data from: (i) a spatially uniform cell proliferation assay; (ii) a two-dimensional circular barrier assay; and (iii) a three-dimensional invasion assay. The Bayesian sequential learning approach leads to well-defined parameter estimates. In contrast, taking a naive approach that attempts to estimate all parameters from a single set of images from the same experiment fails to produce meaningful results. Overall, our approach to inference is simple-to-implement, computationally efficient, and well suited for many cell biology phenomena that can be described by low-dimensional continuum models using ordinary differential equations and partial differential equations. We anticipate that this Bayesian sequential learning framework will be relevant in other biological contexts where it is challenging to extract detailed, quantitative biological measurements from experimental images and so we must rely on using relatively simple measurements from complex images.