Evolutionary synthesis of stochastic gene network models using feature-based search spaces
Abstract
A feature-based fitness function is applied in a genetic programming system to synthesize
stochastic gene regulatory network models whose behaviour is defined by a time course of
protein expression levels. Typically, when targeting time series data, the fitness function
is based on a sum-of-errors involving the values of the fluctuating signal. While this approach
is successful in many instances, its performance can deteriorate in the presence of
noise. This thesis explores a fitness measure determined from a set of statistical features
characterizing the time series' sequence of values, rather than the actual values themselves.
Through a series of experiments involving symbolic regression with added noise and gene
regulatory network models based on the stochastic 'if-calculus, it is shown to successfully
target oscillating and non-oscillating signals. This practical and versatile fitness function
offers an alternate approach, worthy of consideration for use in algorithms that evaluate
noisy or stochastic behaviour.