Problem
Research questions and friction points this paper is trying to address.
multicellular pattern formation
agent-based model
topological characteristics
TI$^2$PS: A Topology-Informed Inverse Design Framework for Stochastic Multicellular Pattern Formation
🤖 AI Summary
该研究提出了一种基于拓扑信息的逆向设计框架TI$^2$PS，通过结合Betti向量和逆向代理建模方法来估计生成目标多细胞模式所需的参数，解决了细胞水平参数估计和定量评估随机增殖与死亡下的多细胞布局拓扑特征的问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Topology-Informed Inverse Design
Betti vectors
Inverse surrogate modeling
Multicellular pattern formation
Agent-based model (ABM)
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