Parallelization and High-Performance Computing Enables Automated Statistical Inference of Multi-scale Models

Nick Jagiella, Dennis Rickert, Fabian J. Theis, Jan Hasenauer

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

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 ∼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.

Original languageEnglish
Pages (from-to)194-206.e9
JournalCell Systems
Volume4
Issue number2
DOIs
StatePublished - 22 Feb 2017

Keywords

  • Bayesian parameter estimation
  • approximate Bayesian computation
  • high-performance computing
  • model-based data integration
  • multi-scale modeling
  • statistical inference
  • tumor spheroids

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