Improving efficiency within the agricultural industry is vital to maintain the food demands of the increasing population, as well the current preference for a more protein rich diet. One avenue for addressing these issues is to study animal-based growth to determine if the efficiency of the production system can be improved by increasing lean muscle mass. The aim of this thesis is to provide an alternative exploration to experimental work to provide an insight into how muscle metabolism in pigs is altered by the administration of a beta-agonist which induces muscle hypertrophy. This will be incorporated into a wider body of work to determine specific pathways to target for improving feed conversion efficiency, contributing to the necessary research into global food security. We begin by compiling a selection of statistical methods to analyse muscle microarray data, which enables the identification of a selection of genes whose expressions are altered by the exposure to a beta-agonist. These differentially expressed transcripts are then grouped via a k-means algorithm, with log likelihood and the Bayesian Inference Criterion calculations providing an optimal selection of clusters. This results in selecting a group of 51 transcripts and partitioning them into 9 clusters, and identifying several pathways which appear key to the regulation of muscle metabolism in the presence of beta-agonist. We have proceeded to incorporate this information into a mathematical model for glycolysis and the TCA cycle, in an effort to analyse biological hypotheses about how the promoters work. The equations describe the concentrations of metabolites within the cytosolic and mitochondrial compartments of a cell using mass balance ODEs. An initial model is presented, which is then increased in complexity, to keep up with developments in the experimental side of the overarching project. We make use of a selection of methods to analyse the model in an attempt to determine the effects that the different parameters cause. Through steady state analysis, we determine parameter ranges which permit positive steady states. In finding these regions, we also determine the existence of time dependent solutions, which occur when critical values of certain parameters are exceeded, and result in the build up of specific metabolites. We use asymptotic analysis to generate approximate solutions when steady states do not exist. The model parameters of most interest are those which were identified through the microarray work, namely the upregulated transcripts of PCK2 and those within the serine synthesis pathway, the control mechanism for the first half of the TCA cycle, the proportion of GTP producing enzyme from the second half of the TCA cycle, and the flux into the glycolytic pathway. We find that critical values for the glycolytic flux, and the GTP production parameter exist, determining whether the model lies within the steady state regime. In a large number of cases, the parameters we choose to represent the beta-agonist case push the system into the time dependent state. The model does not exhibit any interesting behaviour when the parameter controlling the PCK2 pathway is studied, indicating that initial intuition of the key controlling reaction mechanisms were incomplete. Whilst there are shortfalls in the model, which highlight areas for investigation, the system is set up for validation and parameter fitting when appropriate experimental data become available. We have been able to determine specific metabolic pathways within the cell which may be of significance to improving feed efficiency.