Topology Informed Surrogate Modeling for Parameter Optimization in Multicellular Models

Andrew Kailiang Jin, Kenji Komiya, Ryo Nishikimi, Kunio Kashino

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Keywords: Multicellular pattern formation, agent-based model, topological data analysis, surrogate modeling

TL;DR: We propose a framework that infers ABM parameters from multicellular patterns using topological data analysis and surrogate modeling. Validated on zebrafish, it also detects unknown mechanisms when inference fails.

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 the proposed framework successfully infers ABM parameters.
Additionally, when we applied the framework to mutant zebrafish pigment patterns,
we estimated parameters with limited similarities to target patterns.
This discrepancy suggests that the framework may also serve as a detection tool for identifying missing or unknown mechanisms in the underlying ABM or biological system.

Track: 7. General Track

Registration Id: 76NKYTCYLGP

Submission Number: 375