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

Nick Jagiella1, Dennis Rickert1, Fabian J Theis2

  • 1Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany.

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|January 17, 2017
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Summary
This summary is machine-generated.

We developed a new computational method for parameterizing complex biological models. This approach enables accurate, data-driven modeling of multi-scale biological systems, like tumor growth.

Keywords:
Bayesian parameter estimationapproximate Bayesian computationhigh-performance computingmodel-based data integrationmulti-scale modelingstatistical inferencetumor spheroids

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Biophysics

Background:

  • Understanding multi-scale biological processes, such as cell proliferation, requires sophisticated computational models.
  • Existing tools facilitate model construction and simulation, but statistical inference of unknown model parameters remains a significant challenge.

Purpose of the Study:

  • To present and benchmark a novel parallel approximate Bayesian computation sequential Monte Carlo (pABC SMC) algorithm.
  • To demonstrate the algorithm's capability for automated, reliable parameter estimation and confidence interval generation in high-performance computing environments.

Main Methods:

  • Development and implementation of a parallel approximate Bayesian computation sequential Monte Carlo (pABC SMC) algorithm.
  • Application of the pABC SMC algorithm to parameterize multi-scale models of tumor spheroid growth.
  • Utilizing high-performance computing clusters for extensive algorithm runs (approximately 10^6 hours).

Main Results:

  • The pABC SMC algorithm successfully parameterized multi-scale models, accurately describing in vivo tumor spheroid growth data (quantitative growth curves and histology).
  • The parameterized models captured the hybrid deterministic-stochastic behaviors of large cell populations (10^5-10^6 cells) in dynamic 3D nutrient environments.
  • The algorithm demonstrated reliable convergence to a consistent set of model parameters.

Conclusions:

  • The study provides a proof of principle for robust, data-driven modeling of complex multi-scale biological systems.
  • The developed pABC SMC algorithm offers a feasible and effective solution for multi-scale model parameterization through statistical inference.