James Crowe
Faculty Member
Last active: 3/31/2020

Hybrid grammar-based approach to nonlinear dynamical system identification from biological time series.

McKinney BA, Crowe JE, Voss HU, Crooke PS, Barney N, Moore JH
Phys Rev E Stat Nonlin Soft Matter Phys. 2006 73 (2 Pt 1): 021912

PMID: 16605367 · DOI:10.1103/PhysRevE.73.021912

We introduce a grammar-based hybrid approach to reverse engineering nonlinear ordinary differential equation models from observed time series. This hybrid approach combines a genetic algorithm to search the space of model architectures with a Kalman filter to estimate the model parameters. Domain-specific knowledge is used in a context-free grammar to restrict the search space for the functional form of the target model. We find that the hybrid approach outperforms a pure evolutionary algorithm method, and we observe features in the evolution of the dynamical models that correspond with the emergence of favorable model components. We apply the hybrid method to both artificially generated time series and experimentally observed protein levels from subjects who received the smallpox vaccine. From the observed data, we infer a cytokine protein interaction network for an individual's response to the smallpox vaccine.

MeSH Terms (12)

Algorithms Animals Computer Simulation Gene Expression Profiling Gene Expression Regulation Humans Models, Biological Nonlinear Dynamics Pattern Recognition, Automated Signal Transduction Time Factors Transcription Factors

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