TI$^2$PS: A Topology-Informed Inverse Design Framework for Stochastic Multicellular Pattern Formation

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.

Machine Learning (cs.LG)

arXiv:2608.27931 [cs.LG]

(or arXiv:2608.27931v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.27931 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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