Rapid development of novel technologies, including large-scale genome and proteome analysis, produces simultaneous quantitative dynamic characteristics of most biological molecules in a cell. Mathematical modeling of gene networks dynamics, analysis of their normal and pathological states, diagnostics of mutations in biological systems, and the study of evolution have become topical problems. The mathematical model simulating cholesterol biosynthesis in a cell and its exchange with blood plasma cholesterol was developed earlier with precisely described regulatory mechanisms, included portrayed cholesterol metabolism and additionally verified values of its parameters used for computer analysis of a mutational portrait of this gene network. The sensitivity of free cholesterol stationary content to mutational changes in the rates of molecular processes running within the corresponding gene network was analyzed. It was demonstrated that the mutations hitting regulatory processes changed the free cholesterol stationary content to a large degree. Analysis of mutational portraits of gene networks is an approach allowing the development of optimal strategies for correcting various pathologies taking into account the genotype-specific distinctions of particular individuals and detection of the targets for pharmacological regulation. The model task of simulating evolution of a diploid gene network on cholesterol biosynthesis regulation in a cell has been studied. As was shown, adaptation may touch on a single locus or many loci simultaneously in dependence upon the stringency of alterations of external environment. In the course of this process, adaptation concerns mainly the loci with mutations producing slightly favorable/deleterious or quasi-neutral effects. The loci responsible for functioning of non-limiting stages of gene networks, as well as the loci with mutational alterations exerting strong damaging effects are not crucial for selection. The graphic interface of the gene network and its computer dynamic model can be accessed at http://wwwmgs.bionet.nsc.ru/mgs/gnw/gn_model/.