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Comparing AI versus Optimization Workflows for Simulation-Based Inference of Spatial-Stochastic Systems

MA Ramirez-Sierra, TR Sokolowski - arXiv preprint arXiv:2407.10938, 2024 - arxiv.org
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… Recently, modern deep-learning techniques have been integrated with simulation-based inference, creating an exciting new approach for estimating parameters of such …

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BibTeX

@article{2407.10938v1,
Author        = {Michael A. Ramirez-Sierra and Thomas R. Sokolowski},
Title         = {Comparing AI versus Optimization Workflows for Simulation-Based
  Inference of Spatial-Stochastic Systems},
Eprint        = {2407.10938v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {physics.bio-ph},
Abstract      = {Model parameter inference is a universal problem across science. This
challenge is particularly pronounced in developmental biology, where faithful
mechanistic descriptions require spatial-stochastic models with numerous
parameters, yet quantitative empirical data often lack sufficient granularity
due to experimental limitations. Parameterizing such complex models thus
necessitates methods that elaborate on classical Bayesian inference by
incorporating notions of optimality and goal-orientation through
low-dimensional objective functions that quantitatively capture the target
behavior of the underlying system. In this study, we contrast two such
inference workflows and apply them to biophysics-inspired spatial-stochastic
models. Technically, both workflows are simulation-based inference (SBI)
methods. The first method leverages a modern deep-learning technique known as
sequential neural posterior estimation (SNPE), while the second is based on a
classical optimization technique called simulated annealing (SA). We evaluate
these workflows by inferring the parameters of two complementary models for the
inner cell mass (ICM) lineage differentiation in the blastocyst-stage mouse
embryo. This developmental biology system serves as a paradigmatic example of a
highly robust and reproducible cell-fate proportioning process that
self-organizes under strongly stochastic conditions, such as intrinsic
biochemical noise and cell-cell signaling delays. Our results indicate that
while both methods largely agree in their predictions, the modern SBI workflow
provides substantially richer inferred distributions at an equivalent
computational cost. We identify the computational scenarios that favor the
modern SBI method over its classical counterpart. Finally, we propose a
plausible approach to integrate these two methods, thereby synergistically
exploiting their parameter space exploration capabilities.},
Year          = {2024},
Month         = {Jul},
Url           = {http://arxiv.org/abs/2407.10938v1},
File          = {2407.10938v1.pdf}
}

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