 Dear students, in this module I am going to expand on the applications of Bioinformatics for you. As you already know, Bioinformatics has been diversified into several sub-fields such as genomics, proteomics, transcriptomics, metabolomics, systems biology as well as personalized medicine. In these areas, lots of data is currently being produced and it can be a very useful avenue for application of Bioinformatics tools and strategies. So as I just mentioned, the Bioinformatics techniques have enabled us to explore this omics data and to analyze it further. In the following slides, I am going to take a step-by-step approach and look at specific problems within each one of these areas and see how Bioinformatics can be helpful in the endeavor. Starting from small to big, Bioinformatics tools can be applied towards gene prediction, managing the genomic information that is chained out from genomics experiments and then you can move towards structure prediction. Structure can be of an RNA or of a protein. Next, you can look at the interaction between the RNAs and the proteins or even the proteins and proteins or the protein-protein interaction. So once you know which proteins are talking to each other and are interacting, you can have an interaction network. So Bioinformatics techniques, they also enable you to construct these networks. So once you have built these networks by looking at the proteins that are interacting with each other, you can embed these networks into the cells and you can perform modeling and simulation in silico studies on how the cells that contain these networks how they behave and you can predict their behavior as well. All of that can then be looked into from a perspective of cell signaling. So you know that biological processes are a manifestation of complex signaling that is ongoing all the time within a cell. So once you built a cell model, then you can also look at the signaling that is underpinning the cellular function and you can simulate it as well. Once you have developed a cell level simulation, then you can expand it to the tissue level by including several cells within a single simulation and you can look how the tissue evolves or for that matter in case of cancer, how a tumor can evolve. Lastly, the biological networks and these simulations, they can help you to bring up personalized therapeutics into Bioinformatics. You know that these models can integrate the data from various genomic, proteomic, metabolomic as well as the systems biology levels. So you can create models by integrating all of that information and then you can simulate these models to predict what kind of behaviors these models can have. Lastly, once you have these simulations going where you have lots of cells with their protein content, with their genomic content, then you can look at how a disease can be manifested or how a disease can be produced by any abnormality within the genomic or proteomic levels within the cell. In conclusion, Bioinformatics not only organizes and stores the biological omics information data sets but also models it and simulates it towards developing novel drugs and addressing the diseases such as cancer and diabetes.