report

Boolean network modeling is an accurate form of representing genetic networks of genetic diseases. Boolean network models help put these complex networks of the twenty thousand genes estimated in the human genome into perspective. Because the results of the first edition of SIAN was discrete and deterministic, we decided to integrate the Monte Carlo Algorithm to create a probabilistic program and add higher order networks to produce increasingly realistic results and analysis. In addition to accurately modeling a genetic network in cancer, the newly developed program (SIAN) is capable of “reverse engineering” the trajectory from a genetic network to yield the original network.  It can also determine the most efficient genetic change to bring a network from an abnormal state to a normal state. This economic change can suppress the genes responsible for causing genetic diseases and cancer. The correctness of the code is confirmed through simulation. SIAN not only helps provide efficient, effective therapeutic treatments to cancer patients, but also to patients with other genetic diseases as well. The software enables physicians and gene therapists to pinpoint the cause of the abnormal genetic activities and consequently to design an effective gene therapy procedure for patients with genetic abnormalities.

Project SIAN is subject to further development, including a projected version in the C++ language.