In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical model, then analyzing experimental data in the context of the chosen model. In this work, we present the first application of the sparse identification of non-linear dynamics (SINDy) algorithm to discover cell-cell interaction dynamics in in vitro experimental data, using chimeric antigen receptor (CAR) T-cells and patient-derived glioblastoma cells. By combining the techniques of latent variable analysis and SINDy, we infer key aspects of the interaction dynamics of CAR T-cell populations and cancer. Importantly, we show how the model terms can be interpreted biologically in relation to different CAR T-cell functional responses, single or double CAR T-cell-cancer cell binding models, and density-dependent growth dynamics in either of the CAR T-cell or cancer cell populations. This data-driven model-discover based approach has the potential to improve the implementation and efficacy of CAR T-cell therapy in the clinic through an improved understanding of CAR T-cell dynamics.

中文翻译：

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使用稀疏识别和潜在变量进行 CAR T 细胞杀伤的数据驱动模型发现和解释

在基于细胞的癌症疗法的开发中，细胞相互作用的定量数学模型有助于理解治疗效果。验证和解释癌细胞生长和死亡的数学模型的努力首先取决于提出一个精确的数学模型，然后在所选模型的背景下分析实验数据。在这项工作中，我们提出了非线性动力学稀疏识别 (SINDy) 算法在体外发现细胞间相互作用动力学的首次应用实验数据，使用嵌合抗原受体 (CAR) T 细胞和患者来源的胶质母细胞瘤细胞。通过结合潜变量分析和 SINDy 技术，我们推断了 CAR T 细胞群与癌症相互作用动力学的关键方面。重要的是，我们展示了如何从生物学角度解释模型项与不同 CAR T 细胞功能反应、单或双 CAR T 细胞-癌细胞结合模型以及任一 CAR T 细胞中密度依赖性生长动力学的关系或癌细胞群。这种基于数据驱动模型发现的方法有可能通过更好地了解 CAR T 细胞动力学来改善 CAR T 细胞疗法在临床上的实施和疗效。