The team is identifying the genes central to stabilization in order to engineer this trait into other organisms' tissue and cell types. Another second-level bioinformatics task is piecing information together. At this stage the algorithms and mathematics are the most sophisticated, requiring both discrete and continuous algorithms to make the connection between the genetic sequence and the biological function, and to create models that describe and predict interactions at the level of the cell cycle. The difference between the second and third level is that at the second level a scientist may observe that disease occurs when a specific gene is missing or flawed. At the third level, we have an explanation of how the missing or flawed gene results in a physiological phenomenon.
Treatment is still possible at the second level.
We may know that a certain substance prevents or eases the symptoms of Parkinson's disease without knowing why, for instance. In particular, we need computational tools to predict cell physiology from the wiring diagrams' of underlying molecular control systems. He is now preparing a new evolution of the program that will be able to simulate more complete models joining the biochemical and gene expression knowledge. This will be the required tool to integrate the experimental observations into predictive models in the computer. This will happen at high-throughput and it will require development of a whole new area of bioinformatics applied to biochemical pathways.