Jason Bazil

Background

About Jason Bazil

My lab is looking for talented graduate students and postdocs with an interest in computational biology. If interesting, please reach out to me.I don't regularly check my LinkedIn account, so please contact me directly via email if you want to reach me.Ischemic heart disease is the leading cause of mortality in the US and worldwide. And a significant portion of deaths are caused by a myocardial infarction which precipitates a lethal injury that involves mitochondria. According to the AHA, every half minute or so an American has a myocardial infarction and dies from one every minute and a half. These alarming statistics demonstrate the need to better understand the conditions surrounding the injury. In particular, the critical events during ischemia and the effects caused by the injury are not well defined. In order to develop preventative and mitigative measures, research efforts must characterize these phenomena in greater detail and identify the constitutive elements and their complex interactions. My lab uses an integrative strategy that combines both computational modeling and experimental physiology to study these phenomena.Mitochondria are known to be involved in many pathologies and injuries. Yet little is known of the precise mechanisms involved. My lab is pursuing therapeutic strategies by first characterizing mechanisms of mitochondrial dysfunction in heart disease and then identifying feasible approaches to prevent it. Much of our work on this topic is focused on cardiac mitochondria. We use state-of-the-art instrumentation to monitor the response of mitochondria to calcium overload and oxidative stress to investigate reperfusion injury. We are also utilizing cryogenic electron tomography to peer deep into mitochondria and develop biophysical mechanisms that link mitochondrial ultrastructure to metabolic function.In addition, my lab is developing methods for large-scale data analysis. The availability of tools used to query so-called Big Data in an informative manner lag far behind our ability to produce these data. Unlike standard approaches which rely on statistical inference and association, our contributions in this area couple dynamical modeling with network inference and reverse engineering. My lab applies these tools to analyze large-scale data and generates predictive models that are useful for emerging applications in pharmacogenomics and systems pharmacology.