Causal modeling is important in biomedicine because it describes a system’s behavior not only under observation but also under intervention. Logic-based causal discovery exploits this expressive power to identify causal models from data sets that may be obtained under different experimental conditions and measure different variables.
Pulmonary hypertension (PH) is a heterogenous collection of conditions characterized by an increase in blood pressure in the pulmonary vasculature. In the Chan lab, we are using both clinical and lab-based approaches to understanding the progression, mechanisms, and causes of PH.
Using NIH tools and expertise can improve odds of a successful application. In this workshop, we will discuss strategies to make the most of NIH staff and resources to plan and prepare a competitive application and manage the post-submission outcome. Bring any questions you have about the NIH and its grant application and review process.
Where do genes come from? Traditionally, most models for new gene evolution invoke modifications of ancestral genes.
The human microbiome (microbial communities and their gene content) is composed of trillions of cells in multiple ecological niches. Although the lung was initially believed to be sterile in the normal host, recent data indicate that microbial communities are detectable in individuals without lung disease.
Bacterial pathogens have evolved a plethora of different adaptations that confer high-level resistance to certain antibiotics. However, it remains unclear whether the observed adaptations are randomly drawn from a much larger set of possibilities or represent the best of only a few options for obtaining resistance.