Maximally informative next experiments for nonlinear models

Math Biosci. 2018 Aug:302:1-8. doi: 10.1016/j.mbs.2018.04.007. Epub 2018 Apr 27.

Abstract

Mathematical modeling is a powerful tool in systems biology; we focus here on improving the reliability of model predictions by reducing the uncertainty in model dynamics through experimental design. Model-based experimental design is a process by which experiments can be systematically chosen to reduce dynamic uncertainty in a given model. We discuss the Maximally Informative Next Experiment (MINE) method for group-wise selection of points in an experimental design and present a convergence result for MINE with nonlinear models. As an application, we illustrate the method on polynomial regression and an ODE model for immune system dynamics. The MINE criterion sequentially determines experiments that can be conducted to best refine model dynamics.

Keywords: B cell signaling; Computational modeling; Experimental design; Model interpolation; Nonlinear dynamics.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Humans
  • Mathematical Concepts
  • Models, Biological*
  • Models, Immunological
  • NFATC Transcription Factors / immunology
  • Nonlinear Dynamics*
  • Receptors, Antigen, B-Cell / immunology
  • Research Design / statistics & numerical data
  • Signal Transduction / immunology
  • Systems Biology / methods*
  • Systems Biology / statistics & numerical data
  • Uncertainty

Substances

  • NFATC Transcription Factors
  • Receptors, Antigen, B-Cell