Metabolic Cybernetics

Aside from just sounding cool “Cybernetics” is really another term for systems biology. Originally coined by Norbert Weiner in the 1940s, Cybernetics can be applied to any complex system and it aptly describes our efforts to understand metabolic homeostasis. We seek to understand how systems (cells, organs or animals) respond to perturbations, such as hormones like insulin, exercise or different diets. We are also interested in defining the impact of the genome on these different effects. To achieve this we have developed ways of studying the transcriptome, the metabolome, the proteome and the phosphoproteome as well as other post translational changes since these factors work together to govern the behaviour of the system in response to perturbations. By performing such analyses in carefully controlled animal models that display genetic diversity we can begin to study complex phenotypes as are seen in humans like diabetes, liver disease or cardiovascular disease but with precise control. With these kinds of approaches, we hope to bridge the gene-environment barrier. Our ultimate goal is to delineate the key control points that are likely to be the targets of disease and more importantly the targets of optimal future therapeutic intervention.

Insulin and exercise are two of the most physiologically relevant modulators of metabolism in mammals that affect how we sense and respond to environmental challenges. Our goal is to understand how insulin and exercise change our body at the molecular level so that we can harness this information to improve health. Most notably, insulin resistance, or impaired insulin action, is one of the earliest detectable defects associated with metabolic diseases like type 2 diabetes and so to understand this it is essential to understand how insulin works under normal conditions. To do this we are conducting both fundamental biochemical and cell biological studies as well as more global systematic analyses of system behaviour such as analysis of the cellular phosphoproteome. These experiments often generate large amounts of data and to decrypt this requires deep quantitative approaches. So we are working hard to build an interdisciplinary team that together can solve some of these complex problems.

Find out more on the various projects we are currently working on from here!