Towards Identification of Gene Interaction Networks of Human Cholesterol Biosynthesis

Peter Juvan,1,* Tadeja Režen,1 Damjana Rozman,1 Katalin Monostory,3 Jean-Marc Pascussi4 and Aleš Belič2

1Center for Functional Genomics and Bio-chips, Faculty of Medicine, University of Ljubljana, Zaloška cesta 4, SI-1000 Ljubljana, Slovenia
2Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, SI-1001 Ljubljana, Slovenia
3Chemical Research Center, Hungarian Academy of Sciences, Pusztaszeri 59–67, H-1025 Budapest, Hungary
4INSERM UMR-U632, 1919 route de Mende, F-34293 Montpellier, France; University Montpellier 1, F34000, Montpellier, France
* Corresponding author: E-mail:

It has long been demonstrated that the level of cholesterol in cells regulates the cholesterol biosynthesis through SREBF transcription factors, but lately it has been shown that other factors are also important. To study the system we employed Bayesian network inference and combined it with mathematical modeling and simulation. We constructed a mathematical model of cholesterol biosynthesis and studied its properties through simulation. We measured transcriptional changes of cholesterogenic genes using the Steroltalk microarray and treated human hepatocyte samples. We employed Bayesian inference to identify gene-to-gene interactions from both microarray measurements and simulated data. The inferred networks show that the expression of cholesterogenic genes can be predicted from the expression of 4 key genes, one of them being SREBF2. Networks also indicate a strong interaction between SREBF2 and CYP51A1, but not between SREBF2 and HMGCR, the rate-limiting enzyme of cholesterol biosynthesis. The expression of HMGCR seems to be regulated by other factor(s). Computer simulations of the mathematical model of cholesterol biosynthesis exposed that a large number of perturbations of the system is critical for identification of gene-to-gene interactions, and that differences between human individuals (biological variability) and measurement noise (technical variability) pose a serious problem for their automatic inference from DNA microarray data.

Keywords: Functional genomics, systems biology, gene interaction network, Bayesian inference, mathematical modeling and simulation, human cholesterol biosynthesis