TY - JOUR
T1 - Parallelization and High-Performance Computing Enables Automated Statistical Inference of Multi-scale Models
AU - Jagiella, Nick
AU - Rickert, Dennis
AU - Theis, Fabian J.
AU - Hasenauer, Jan
N1 - Publisher Copyright:
© 2017 The Author(s)
PY - 2017/2/22
Y1 - 2017/2/22
N2 - 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.
AB - 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.
KW - Bayesian parameter estimation
KW - approximate Bayesian computation
KW - high-performance computing
KW - model-based data integration
KW - multi-scale modeling
KW - statistical inference
KW - tumor spheroids
UR - http://www.scopus.com/inward/record.url?scp=85009480825&partnerID=8YFLogxK
U2 - 10.1016/j.cels.2016.12.002
DO - 10.1016/j.cels.2016.12.002
M3 - Article
C2 - 28089542
AN - SCOPUS:85009480825
SN - 2405-4712
VL - 4
SP - 194-206.e9
JO - Cell Systems
JF - Cell Systems
IS - 2
ER -