Macrophages play an important and multifunctional role in health and disease. The best-known role is in innate immunity where they act as phagocytes and inflammatory cells. In addition, they also act as a lynch pin between innate and adaptive immunity as antigen presenting cells (APC). Macrophages also have a key function in the uptake and clearance of cholesterol interacting with low-density lipoproteins (LDL) as foam cells. Finally, macrophages can also be differentiated to become osteoclasts that function to absorb or degrade bone during remodeling.
Our group is interested in using animal models to study the differentiation of monocytes into macrophages and the subsequent development of macrophages under various physiological conditions. We will utilize genomics, gene expression profiling, proteomics and the tools of systems biology to develop quantitative mathematical and statistical models of macrophage development.
While this process is understood in detail at the cellular or phenotypic level, only a few key molecular players have been identified to date. Our goal is to use global measurements from microarrays and proteomics to infer the underlying molecular program that regulates homeostasis and drives the differentiation. One of the key players in all the processes mentioned above is NFkB; it is activated early in all the development pathways. Therefore, a central question is: how is the specificity of the outcome determined? What set of signals and downstream events drive the macrophage towards any given differentiation program? How robust is this program and how many subsystems are there? How are these signals integrated (spatial compartmentalization and parallel processing), and how do they determine the fate?
In order to generate quantitative models our group is also interested developing algorithms for network inference using graph theoretic approaches and Bayesian statistics. Mathematics to model complex circuits have been previously developed, however, many of these have not been applied to biological networks. Biological systems use feedback loops and other regulatory mechanisms; we will also develop methods to deal with these mathematically. The systems biology approach combines computational biology and bioinformatics, systems and control theory and modeling methods to develop a coherent and causal picture of this important biological phenomenon.
More information is available http://macro.slu.edu