Over the last century, preventative approaches have been dramatically more effective than therapeutics at reducing the societal burden of disease. Historic killers such as smallpox, polio, and tuberculosis were beaten not by new therapies, but by vaccines and sanitation improvements that eliminated the context in which infectious diseases flourished. For today’s biggest killers, cardiovascular disease, cancer, and diabetes, we have only the most basic preventative measures. Our best advice—eat well and exercise—would be familiar to doctors in ancient Greece. To continue last century's progress in improving human health, we need to develop better preventative strategies against age-related diseases. We need to identify and disrupt the physiologic processes that, throughout life, increase the risk of disease.
Yet, age-related diseases are difficult to prevent because they have many causes, arising from a complex interaction between many genes, diet, and environment. It is increasingly clear that most age-related diseases do not arise from a linear chain of events involving a small number of molecules. Instead, age-related diseases emerge from interactions among large networks of genes, influenced by environmental factors, whose cumulative contribution throughout life determines the risk of falling ill. To study the complex interaction between many causal factors that contribute to disease risk, we need new methods capable of measuring high-dimensional dynamics of physiologic change during aging.
In this project, we have developed a set of measurement techniques combining transcriptomic profiling, in vivo biosensors, and new imaging technology. We collected across multiple spatial scales—molecules, cells, organs, individuals, and populations—developing 1) a set of transcriptomic and functional genetic approaches to study how gene regulation varies between individuals and is altered by age, 2) a new imaging approach to simultaneously study age-associated changes in behavior and lifespan, and 3) an atlas of inter-individual variation in aging and how it is altered by lifespan-extending interventions.
Combining molecular genetics with theoretic approaches, this project has helped us build better quantitative models of how complex diseases emerge from slow molecular-level changes, and in particular how multi-scale interactions drive differences in lifespan that arise stochastically within isogenic populations.
