BACKGROUND Human mesenchymal stromal cells (hMSCs) have become attractive candidates for advanced medical cell-based therapies. An in vitro expansion step is routinely used to reach the required clinical quantities. However, this is influenced by many variables including donor characteristics, such as age and gender, and culture conditions, such as cell seeding density and available culture surface area. Computational modeling in general and machine learning in particular could play a significant role in deciphering the relationship between the individual donor characteristics and their growth dynamics. METHODS In this study, hMSCs obtained from 174 male and female donors, between 3 and 64 years of age with passage numbers ranging from 2 to 27, were studied. We applied a Random Forests (RF) technique to model the cell expansion procedure by predicting the population doubling time (PDT) for each passage, taking into account individual donor-related characteristics. RESULTS Using the RF model, the mean absolute error between model predictions and experimental results for the PDT in passage 1 to 4 is significantly lower compared with the errors obtained with theoretical estimates or historical data. Moreover, statistical analysis indicate that the PD and PDT in different age categories are significantly different, especially in the youngest group (younger than 10 years of age) compared with the other age groups. DISCUSSION In summary, we introduce a predictive computational model describing in vitro cell expansion dynamics based on individual donor characteristics, an approach that could greatly assist toward automation of a cell expansion culture process.

中文翻译：

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使用机器学习根据个体供体特征预测体外人类间充质基质细胞扩增

背景人类间充质基质细胞（hMSCs）已成为先进的医学细胞疗法的有吸引力的候选者。体外扩增步骤通常用于达到所需的临床量。然而，这受到许多变量的影响，包括供体特征（如年龄和性别）和培养条件（如细胞接种密度和可用培养表面积）。一般的计算建模，特别是机器学习，可以在破译个体供体特征与其生长动态之间的关系方面发挥重要作用。方法 在这项研究中，研究了从 174 名年龄在 3 到 64 岁之间、传代数从 2 到 27 不等的男性和女性供体中获得的 hMSC。我们应用随机森林 (RF) 技术通过预测每个传代的种群倍增时间 (PDT) 来模拟细胞扩增过程，同时考虑到个体供体相关特征。结果 使用 RF 模型，与理论估计或历史数据获得的误差相比，第 1 至 4 段中 PDT 的模型预测和实验结果之间的平均绝对误差显着更低。此外，统计分析表明，与其他年龄组相比，不同年龄组的PD和PDT有显着差异，尤其是最年轻组（10岁以下）。讨论 总之，我们引入了一个预测计算模型，描述了基于个体供体特征的体外细胞扩增动态，