Using Emulation to Engineer and Understand Simulations of Biological Systems

Modeling and simulation techniques have demonstrated success in studying biological systems. As the drive to better capture biological complexity leads to more sophisticated simulators, it becomes challenging to perform statistical analyses that help translate predictions into increased understandin...

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Vydáno v:IEEE/ACM transactions on computational biology and bioinformatics Ročník 17; číslo 1; s. 302 - 315
Hlavní autoři: Alden, Kieran, Cosgrove, Jason, Coles, Mark, Timmis, Jon
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Analytical models
approximate Bayesian computation
Bayes Theorem
Bayesian analysis
Biological system modeling
Biological systems
Computational modeling
Computer Simulation
Dimensional analysis
Emulation
Emulators
ensemble
Exploitation
Flight simulators
Learning algorithms
Lymphoid tissue
Machine Learning
Mathematical models
mechanistic modeling
Models, Biological
multi-objective optimization
Multiple objective analysis
Optimization
Organogenesis
Parameters
sensitivity analysis
Statistical analysis
Statistics
Systems Biology - methods
Uncertainty
ISSN:1545-5963, 1557-9964, 1557-9964
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Popis
Shrnutí:Modeling and simulation techniques have demonstrated success in studying biological systems. As the drive to better capture biological complexity leads to more sophisticated simulators, it becomes challenging to perform statistical analyses that help translate predictions into increased understanding. These analyses may require repeated executions and extensive sampling of high-dimensional parameter spaces: analyses that may become intractable due to time and resource limitations. Significant reduction in these requirements can be obtained using surrogate models, or emulators, that can rapidly and accurately predict the output of an existing simulator. We apply emulation to evaluate and enrich understanding of a previously published agent-based simulator of lymphoid tissue organogenesis, showing an ensemble of machine learning techniques can reproduce results obtained using a suite of statistical analyses within seconds. This performance improvement permits incorporation of previously intractable analyses, including multi-objective optimization to obtain parameter sets that yield a desired response, and Approximate Bayesian Computation to assess parametric uncertainty. To facilitate exploitation of emulation in simulation-focused studies, we extend our open source statistical package, spartan, to provide a suite of tools for emulator development, validation, and application. Overcoming resource limitations permits enriched evaluation and refinement, easing translation of simulator insights into increased biological understanding.
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2018.2843339