Explore the foundations and applications of Generative Flow Networks (GFlowNets) in biological sequence design through this comprehensive lecture. Delve into the theoretical properties of GFlowNets, including their ability to estimate joint probability distributions, represent distributions over composite objects like sets and graphs, and amortize computationally expensive MCMC methods. Learn about variations enabling entropy and mutual information estimation, sampling from Pareto frontiers, and extensions to stochastic environments. Discover how GFlowNets can be applied to de novo biological sequence design, particularly in active learning loops for molecule ideation and wet-lab evaluations. Gain insights into generating diverse batches of useful and informative candidates for biological sequence design tasks. Follow along as the speaker covers fundamental concepts, practical implementations, and real-world applications, concluding with a personal perspective and Q&A session.