Bay Area Frontier Research Club #28 | Cell Biology x AI: From Cancer Mechanisms to Virtual Cells (dinner + paper discussion @ Stanford)

How do we go from understanding what goes wrong inside a cell to finding where a drug could intervene—and can AI help us get there faster?
This evening at Stanford brings together a new discovery in colorectal cancer and the effort to build virtual cells that could help researchers decide which interventions to test next.
Ganesh Pusapati, PhD, a Senior Research Scientist in Stanford Biochemistry’s Rohatgi Lab, co-led a study published in Nature Genetics this July that uncovered a new therapeutic vulnerability in colorectal cancer. His team identified roughly 150 previously unreported mutations affecting the destruction complex, the machinery that keeps WNT/β-catenin signaling in check. They then showed that restoring its function reduced cancer-cell growth in the lab. He’ll take us through the experiments that revealed this vulnerability and what the findings could mean for future treatments.
Angela Oliveira Pisco, PhD, is Director of Data Science at the Chan Zuckerberg Initiative and previously led computational biology and machine learning for omics at insitro. At CZ Biohub, she helped build major cell atlases, including Tabula Sapiens. She’ll explore what AI can learn from these vast collections of cellular data—and what evidence we need before trusting a virtual cell to help guide drug discovery.
Join us for dinner, two short research talks, and extended Q&A with both speakers. We’ll discuss how an unexpected biological finding becomes a therapeutic hypothesis, where computational predictions can help, and which experiments would convince us to pursue a target. Bring your questions: most of the evening’s research program is reserved for discussion.

The Frontier Research Club is a curated forum for rigorous, technical discussion at the frontier of AI. We convene researchers from the frontier labs, Stanford, Berkeley, and the teams building in production to examine concrete work — papers, methods, and results — with a bias toward assumptions, evaluation methodolo