Columbia University Bio Bytes

In which Dana speaks with Vaibhav and Jason about the principles of single cell analysis and informatics challenges associated with it. Among other things, they discuss manifolds and data topology, Bayesian approaches and considerations in prior distribution selection in biology, matrix factorization and imputing missing cell states with graphical walks through phenotypic space, and the utility of diffusion components when  Principal Component Analysis is insufficient. Dana concludes with some advice for young people interested in Systems and Computational Biology.

In which Vaibhav and Dr. Stockwell talk about how using small molecule screening helped uncover pioneering details about Iron influenced cell death (Ferroptosis), the therapeutic context for Ferroptosis, and ligand prediction with computational chemistry. They conclude with some discussion about Dr. Stockwell's current collaborative SARS-CoV2  antiviral efforts. 

In which Dr. Lappalainen speaks to Vaibhav and Mona about a variety of topics including her latest paper in Science, the various considerations one needs to make when interpreting statistical genomic data, and a graphical representation of genomes as opposed to the traditional linear model. 

In which Vaibhav, Jason, and Professor Xuebing Wu discuss the mechanism and applications of CRISPR-Cas13 and how deep learning might be used to model a unified gene regulatory network. 

In which Vaibhav and Professor Racaniello continue to speak about the epidemic timeline, the use and biology of Anti-Malaria drugs against COVID-19, single cell and CRISPR based antiviral studies, and other topics, like how strong immunity to the virus is (once developed).  

In which Vaibhav speaks to Vincent Racaniello, professor of Virology at Columbia University, about the science behind the current Coronavirus (SARS-CoV-2). Among other topics, they discuss the origins of the virus and the rationale behind current retroviral drug based approaches to treat patients. 

In which Vaibhav and Dr. Azizi continue their discussion on using machine learning to integrate various data modalities into sensible probabilistic and statistical models to better understand cancer genetic networks. 

In which Vaibhav and Dr. Elham Azizi discuss the role of machine learning, AI, and data modeling in cancer biology. 

In which Vaibhav and Jason talk to Dr. Pe'er about the central goals of Systems Biology, the projects that he's most excited about, and what sorts of coursework students should complete to prepare themselves for getting into the field

In which Vaibhav and Jason talk to Dr. It'sik Pe'er, Associate Professor of Computer Science with cross-affiliation in the Systems Biology department, about his current projects and the emergence of computational biology