Elevated plasma cholesterol, a well-known risk factor for cardiovascular diseases, is the result of the activity of many genes and their encoded proteins in a complex physiological network. We aim to develop a minimal kinetic computational model for predicting plasma cholesterol levels. To define the scope of this model, it is essential to discriminate between important and less important processes influencing plasma cholesterol levels. To this end, we performed a systematic review of mouse knockout strains and used the resulting dataset, named KOMDIP, for the identification of key genes that determine plasma cholesterol levels. Based on the described phenotype of mouse knockout models, 36 of the 120 evaluated genes were marked as key genes that have a pronounced effect on the plasma cholesterol concentration. The key genes include well-known genes, e.g., Apoe and Ldlr, as well as genes hardly linked to cholesterol metabolism so far, e.g., Plagl2 and Slc37a4. Based on the catalytic function of the genes, a minimal conceptual model was defined. A comparison with nine conceptual models from literature revealed that each of the individual published models is less complete than our model. Concluding, we have developed a conceptual model that can be used to develop a physiologically based kinetic model to quantitatively predict plasma cholesterol levels. © 2010 Elsevier B.V. All rights reserved.