用物理信息神经网络分析膀胱癌治疗中细胞动态变化。
Learning Model Parameter Dynamics in a Combination Therapy for Bladder Cancer from Sparse Biological Data
- 基于物理约束的神经网络，建模治疗下肿瘤与免疫细胞的时变交互。
- 在少量数据下预测未观测时间点的细胞亚群轨迹，符合生物学解释。
- 适合肿瘤动力学研究者，尤其关注干预后动态变化的场景。
在生物个体相互作用的数学模型中，外部干预可能随时间改变系统行为，而传统固定参数模型难以捕捉这种演化动态。在肿瘤学中，实验数据常稀疏，仅包含少数时间点的肿瘤体积。本文提出一种方法，学习膀胱癌肿瘤细胞与免疫细胞之间随时间变化的相互作用及其对联合抗癌治疗的响应，在数据有限条件下实现建模。采用物理信息神经网络（PINN）预测无观测数据的时间点上各亚群的潜在轨迹。结果表明，该方法生成的轨迹与生物学机制一致。本方法为外部干预环境下生物种群间动态交互的学习提供了新框架。
原文摘要 · Abstract (English)
In a mathematical model of interacting biological organisms, where external interventions may alter behavior over time, traditional models that assume fixed parameters usually do not capture the evolving dynamics. In oncology, this is further exacerbated by the fact that experimental data are often sparse and sometimes are composed of a few time points of tumor volume. In this paper, we propose to learn time-varying interactions between cells, such as those of bladder cancer tumors and immune cells, and their response to a combination of anticancer treatments in a limited data scenario. We employ the physics-informed neural network (PINN) approach to predict possible subpopulation trajectories at time points where no observed data are available. We demonstrate that our approach is consistent with the biological explanation of subpopulation trajectories. Our method provides a framework for learning evolving interactions among biological organisms when external interventions are applied to their environment.
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