It’s been said that systems biology “is about putting together rather than taking apart.” In the traditional reductionist approach to biology, things are broken down to their simplest parts and studied individually.
In systems biology, scientists try to understand and model complex interactions in a system. These systems can be as simple as a single metabolic pathway or as complex as the behavior of a whole cell or organism. Typically, a theory is developed to describe the system and a computational model is formulated to test hypotheses about the system. These hypotheses are tested experimentally and the results are used to refine both the theory and model. The experimental approach best suited to this is high-throughput techniques such as transcriptomics and proteomics.
Functional genomics is similar, in that techniques such as transcriptomics and proteomics are used to leverage the wealth of available genomic information to better understand gene function and interaction.
Faculty in our department are using mathematical models to describe systems ranging from microbial phenomena to human viral interaction. We use techniques that involve both transcript (micro arrays) and protein (proteomics) to survey global cellular responses. For example, in one project, students are building in silico descriptions of Influenzae A virus to uncover insights into how other similar infections operate and therefore accelerate the development of new antiviral drugs that take advantage of the weaknesses of infection modes. In another project, students are using proteomic analysis to better understand both signal transduction and to discover novel effectors in fungal autophagy.