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Parallelization and High-Performance Computing Enables Automated Statistical Inference of Multi-scale Models
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DOI：10.1016/j.cels.2016.12.002.png)
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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 similar to 10(6) 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 10(5)-10(6) 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.
Keyword:
APPROXIMATE BAYESIAN COMPUTATION
MONTE-CARLO
STOCHASTIC SIMULATION
CELL
PREDICTS
ARCHITECTURE
ENVIRONMENT
ALGORITHMS
SELECTION
SYSTEMS
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