Genetic algorithms as a mechanism for modelling gene interactions using time-course data - J. John, David (2019)
Gene interaction models are weighted graphs derived from replicates of gene abundance timecourse data, where each weighted edge is a probability of gene association. These interactionmodels are a tool to assist biological researchers in understanding gene relationships. Twonew genetic algorithms, one fairly traditional and the other based on crossover with infrequentapplication of a chaotic mutation operator, are developed specifically to produce geneinteraction models from sparse time-course abundance data. Both genetic algorithms evolvea new population from a current population of directed acyclic graphs, each representinga Bayesian model for possible gene interaction. The genetic algorithm fitness is the relativeposterior probability that a Bayesian model fits the gene abundance replicates. These Bayesianlikelihoods are computed using one of three analysis techniques: cotemporal, first order nextstate and second order next state. The weighted gene interaction models reflect the directedacyclic graphs and their likelihoods present in the final populations of numerous independentgenetic algorithm executions. Using a simulated set of genes, these two genetic algorithmsfind the embedded signals and are consistent across analysis paradigms. Results from a setof biological gene abundance data, from Arabidopsis thaliana stimulated by the plant hormoneauxin, are modeled.