Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference

Johannes Heyn, Miguel Atienza Juanatey, Martin Falcke, Joachim Raedler
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Abstract

Trajectories of motile cells represent a rich source of data that provide insights into the mechanisms of cell migration via mathematical modeling and statistical analysis. However, mechanistic models require cell type dependent parameter estimation, which in case of computational simulation is technically challenging due to the nonlinear and inherently stochastic nature of the models. Here, we employ simulation-based inference (SBI) to estimate cell specific model parameters from cell trajectories based on Bayesian inference. Using automated time-lapse image acquisition and image recognition large sets of 1D single cell trajectories are recorded from cells migrating on microfabricated lanes. A deep neural density estimator is trained via simulated trajectories generated from a previously published mechanical model of cell migration. The trained neural network in turn is used to infer the probability distribution of a limited number of model parameters that correspond to the experimental trajectories. Our results demonstrate the efficacy of SBI in discerning properties specific to non-cancerous breast epithelial cell line MCF-10A and cancerous breast epithelial cell line MDA-MB-231. Moreover, SBI is capable of unveiling the impact of inhibitors Latrunculin A and Y-27632 on the relevant elements in the model without prior knowledge of the effect of inhibitors. The proposed approach of SBI based data analysis combined with a standardized migration platform opens new avenues for the installation of cell motility libraries, including cytoskeleton drug efficacies,and may play a role in the evaluation of refined models.
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利用基于模拟的推理,从一维细胞轨迹估算细胞机械参数
运动细胞的轨迹是丰富的数据来源,通过数学建模和统计分析,可以深入了解细胞迁移的机理。然而,机理模型需要根据细胞类型进行参数估计,由于模型的非线性和固有随机性,计算模拟在技术上具有挑战性。在此,我们采用基于模拟推理(SBI)的方法,根据贝叶斯推理从细胞轨迹中估算细胞特定模型参数。利用自动延时图像采集和图像识别技术,我们记录下了细胞在微制造通道上迁移的大量一维单细胞轨迹。通过之前发布的细胞迁移机械模型生成的模拟轨迹,对深度神经密度估算器进行训练。训练好的神经网络反过来用于推断与实验轨迹相对应的有限数量模型参数的概率分布。我们的研究结果表明,SBI 能有效辨别非癌变乳腺上皮细胞系 MCF-10A 和癌变乳腺上皮细胞系 MDA-MB-231。此外,SBI 还能揭示抑制剂 Latrunculin A 和 Y-27632 对模型中相关元素的影响,而无需事先了解抑制剂的影响。所提出的基于SBI的数据分析方法与标准化迁移平台相结合,为建立细胞运动库(包括细胞骨架药物疗效)开辟了新的途径,并可能在评估完善的模型中发挥作用。
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