title: Parallelization and High-Performance Computing Enables Automated Statistical Inference of Multi-scale Models.
abstract: 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 ∼106hr, 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 105-106of 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.
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany.
authors.forename: Nick
authors.initials: N
authors.lastname: Jagiella
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany.
authors.forename: Dennis
authors.initials: D
authors.lastname: Rickert
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; Chair of Mathematical Modeling of Biological Systems, Center for Mathematics, Technische Universität München, Boltzmannstraße 3, 85748 Garching, Germany.
authors.forename: Fabian J
authors.initials: FJ
authors.lastname: Theis
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; Chair of Mathematical Modeling of Biological Systems, Center for Mathematics, Technische Universität München, Boltzmannstraße 3, 85748 Garching, Germany. Electronic address: jan.hasenauer@helmholtz-muenchen.de.
authors.forename: Jan
authors.initials: J
authors.lastname: Hasenauer
mesh_terms: D001499:Bayes Theorem; D008954:Models, Biological; D009010:Monte Carlo Method; D009369:Neoplasms / Q000378:metabolism / Q000473:pathology; D018874:Spheroids, Cellular / Q000166:cytology / Q000378:metabolism
keywords: Bayesian parameter estimation; approximate Bayesian computation; high-performance computing; model-based data integration; multi-scale modeling; statistical inference; tumor spheroids