用拓扑分析与逆建模，高效反推细胞自组织模式的参数。
TI$^2$PS: A Topology-Informed Inverse Design Framework for Stochastic Multicellular Pattern Formation
- 结合贝蒂向量与逆代理模型，从目标模式反推细胞参数。
- 仅用10%数据即超越使用100%数据的PointNet++。
- 适合研究生物模式形成、细胞行为建模的研究者。
本研究提出一种新框架，通过基于代理的模型（ABM）估算参数以重现目标多细胞模式。多细胞ABM面临两大挑战：细胞层级参数（代理特定变量）的估计，以及在细胞随机增殖与死亡条件下对多细胞排列拓扑特征的定量评估。为此，我们融合两种方法：通过拓扑数据分析获得的贝蒂向量可一致表征多种多细胞空间构型特征；逆代理建模则可直接从目标模式推断对应的ABM参数。我们在斑马鱼色素模式形成这一依赖多细胞相互作用的典型模式形成系统中验证了该框架。结果表明，该框架成功估算出ABM参数，且优于传统方法如PointNet++。值得注意的是，该方法仅使用10%训练数据即在所有评估指标上超越使用100%数据的PointNet++。
原文摘要 · Abstract (English)
This study proposes a novel framework to estimate parameters for reproducing target multicellular patterns using an agent-based model (ABM). Two major challenges in multicellular ABMs are estimating cell-level parameters (agent-specific variables) and quantitatively evaluating the topological characteristics of multicellular arrangements under stochastic cell proliferation and death. To address these challenges, we integrate two approaches: Betti vectors and inverse surrogate modeling. The Betti vectors obtained through topological data analysis can consistently represent features of a wide range of multicellular spatial configurations. The inverse surrogate modeling enables direct inference of the corresponding ABM parameters from the target patterns. We validated the proposed framework using zebrafish pigment pattern formation, a representative model of pattern formation driven by multicellular interactions. The results demonstrate that our framework successfully estimates ABM parameters and outperforms conventional methods such as PointNet++. Notably, the proposed method, which used only 10% of the training data, outperformed PointNet++, which used 100% of the data, across all evaluation metrics.
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