Machine Learning Approaches for Building Virtual Cell Models

Machine Learning Approaches for Building Virtual Cell Models

Cells represent the fundamental building blocks of life, operating as intricate, self-regulating systems that respond dynamically to their environment. A major challenge in modern cell biology is understanding and predicting how these complex systems respond to various perturbations, a capability that would revolutionize drug discovery, disease treatment, and our fundamental understanding of biological processes.

Recent technological breakthroughs now enable researchers to generate vast amounts of single-cell interventional data, capturing cellular responses at unprecedented scale and resolution. However, the sheer complexity of cellular systems and the immense space of possible interventions, makes exhaustive experimental exploration impractical and prohibitively expensive.

This seminar explores the emerging frontier of machine learning models designed to simulate cellular behavior and predict how cells respond to novel perturbations. We will examine recent advances in this rapidly evolving field, including newly proposed generative models, biologically-informed architectures, and methods that aim to make perturbation predictions more explainable.

The seminar consists of an introductory lecture introducing the necessary background, and subsequent weekly meetings with two paper presentations and discussions. Students are expected to read into their assigned paper, the related literature, prepare a talk, as well as a final essay critically discussing the paper.

Requirements: The student has a solid understanding of Machine Learning and feels comfortable with Neural Networks (for example through lectures High Level Computer Vision, Neural Networks: Theory and Implementation, or Machine Learning). Previous biological knowledge is not required, but some interest is preferred.

Places: 12

Dates: To be announced.

Papers: To be announced.