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Jagiella, N. ; Rickert, D. ; Theis, F.J. ; Hasenauer, J.

Parallelization and high-performance computing enables automated statistical inference of multi-scale models.

Cell Syst. 4, 194–206.e9 (2017)

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Mechanistic understanding of multi-scale biological processes, such as cell proliferation in a changing biological tissue, is readily facilitated by computational models. While tools exist to construct and simulate multi-scale models, the statistical inference of the unknown model parameters remains an open problem. Here, we present and benchmark a parallel approximate Bayesian computation sequential Monte Carlo (pABC SMC) algorithm, tailored for high-performance computing clusters. pABC SMC is fully automated and returns reliable parameter estimates and confidence intervals. By running the pABC SMC algorithm for ∼106 hr, we parameterize multi-scale models that accurately describe quantitative growth curves and histological data obtained in vivo from individual tumor spheroid growth in media droplets. The models capture the hybrid deterministic-stochastic behaviors of 105-106 of cells growing in a 3D dynamically changing nutrient environment. The pABC SMC algorithm reliably converges to a consistent set of parameters. Our study demonstrates a proof of principle for robust, data-driven modeling of multi-scale biological systems and the feasibility of multi-scale model parameterization through statistical inference. A new parallel approximate Bayesian computation sequential Monte Carlo (pABC SMC) algorithm allows for robust, data-driven modeling of multi-scale biological systems and demonstrates the feasibility of multi-scale model parameterization through statistical inference.

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