Hybrid grammar-based approach to nonlinear dynamical system identification from biological time series. Academic Article uri icon

Overview

abstract

  • 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.

publication date

  • February 22, 2006

Research

keywords

  • Algorithms
  • Gene Expression Profiling
  • Gene Expression Regulation
  • Models, Biological
  • Signal Transduction
  • Transcription Factors

Identity

Scopus Document Identifier

  • 33644555826

Digital Object Identifier (DOI)

  • 10.1103/PhysRevE.73.021912

PubMed ID

  • 16605367

Additional Document Info

volume

  • 73

issue

  • 2 Pt 1