 
        RESEARCH OVERVIEW
Research laboratories in iGEM are pursuing a Pattern-Process-Prediction-Product (P4) paradigm. We conduct fundamental research to first discover biological patterns and to elucidate processes that have generated these patterns over evolutionary time. Our mission is to harness the knowledge of these patterns and processes in making predictions about genomes, diseases, and biodiversity. Ultimately, we develop new tools and resources that enable scientists to accelerate biological discovery and predictions.
iGEM researchers are pursuing a variety of interdisciplinary research and discovery projects, including:
                GENOMICS OF HEALTH
                
                Our personal genomes differ from each other and many of these differences are known to predispose us
                to diseases and to modulate our response to drugs and medical treatments. iGEM researchers are
                discovering the relationship of natural selection with genetic diseases and developing new methods
                and tools to identify harmful personal differences (mutations).
            
                DISEASE DYNAMICS
                
                Pathogens are constantly evolving to avoid drugs and medicine and to invade new hosts and geographic
                areas. iGEM researchers are investigating the when, how, and why of pathogen evolution by taking
                comparative genomics approaches and developing new analytical frameworks for predictive modelling.
            
                BIOLOGICAL COMPLEXITY
                
                Genome sequencing advances have revolutionized our ability to understand the diversity of life. The
                tree of life scaled to time (the "TimeTree") is the primary component of this diversity and underlies
                much of the evolutionary medicine research. iGEM researchers are conducting genome scale data
                acquisitions and analyses to build the Timetree of Life and developing state-of-the-art methods
                and tools to facilitate big data inferences in this field. 
            
 
             
             
             
            