The work presents a methodology to assess the problems behind static gene expression data modelling and analysis with machine learning techniques. As a case study, transcriptomic data collected during a longitudinal study on the effects of diet on the expression of oxidative phosphorylation genes was used. Data were collected from 60 abdominally overweight men and women after an observation period of eight weeks, whilst they were following three different diets. Real-valued static gene expression data were encoded into spike trains using Gaussian receptive fields for multinomial classification using an evolving spiking neural network (eSNN) model. Results demonstrated that the proposed methodology can be used for predictive modelling of static gene expression data and future works are proposed regarding the application of eSNNs for personalised modelling.