## The Insightful Network Structure of Disease

Link to article: http://www.pnas.org/content/104/21/8685.long

The Human Disease Network is one that has an impact on everybody in society, and therefore has been studied very throughly. Although complicated in its core content, the representation of how diseases spread can be modeled using basic graphs that have been introduced in class. These graphs have helped doctors and researchers more thoroughly understand not only the network structure through which diseases travel, but also how diseases interact with each other, along with the probability of being rendered infected within a certain time.

The fundamental graph that represents the network structure of disease interaction is the bipartite graph, which explicitly relates the disease phenome and the disease genome. More specifically, a link (or edge) is placed between a disorder and a disease gene if mutations in that gene will lead to the specific disorder. In addition, the size of the nodes represent the number of genes corresponding to that particular disorder. In summary, this example is inviting into conceptualizing that one bipartite graph is enough to model the fundamental network structure of the disease genome with relation to the disease phenome.

Another useful graph that models disease molecules is the probability graph. This class has seen this type of graph referenced as a graph representing instant messaging, with edges joining two users if they communicated at least once. The probability graphs that model diseases are similar, in that they are very useful and clear. They are again used to model a relationship and study links, but more precisely, they are suited for analyzing the relationships of disease molecules. Graphs can be created that explicitly show the number of observed physical interactions between the products of genes within the same disorder. Additionally, the graphs can also highlight the distribution of the tissue-homogeneity of a disorder. Both of these facets bring to light relationships that were never seen before.

It is simply fascinating how something so complex, something with moving parts that can adapt and change through time, can all be modeled with straightforward graphs that have all been introduced within this class. Taking this complex network structure and looking at the data in a way that can be interpreted and then have the ability to take action has been a driving force in the ongoing fight against disease. It is reassuring to know that these graphs can model this data, because it allows for predictions to be made about potential diseases spreading. Another situation where understanding this network structure through graphs can come into affect is when a disease breaks out. Referencing the graphs, as shown in the article, is the best way to decide the correct action to destroy edges in the graph, and therefore stop the disease from spreading.