Special Life Science Seminar: Aviv Regev
Event details
| Date | 17.03.2025 |
| Hour | 11:00 › 12:00 |
| Speaker | Aviv Regev |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
Title: Design for Inference from Atlases to Medicine
Abstract
In many of the most pressing problems in biology, chemistry, and medicine—including the role of somatic mutations in cancer, genetic variants associated with the risk of common diseases, non-additive genetic interactions, possible antibody or functional protein sequences, or the space of drug-like chemical compounds—there exists an enormous space of possibilities, even though only very few of them are realized. In each of these cases, the number of hypothetical possibilities exceeds by many orders of magnitude what can ever be tested in a lab, clinical trial, or even an entire population. Historically, this challenge was tackled by restricting the search space using prior knowledge, a practical approach which nevertheless severely limited our ability to make new discoveries and effective predictions. Dramatic advancements over the past decade in our ability to perform both comprehensive, massively parallel, and high-resolution experiments now offer the hope that it should be possible to tackle these challenges systematically. However, in principle, an astronomical gap remains between the scale of experiments required to exhaustively map this space and those that can be achieved in practice. The dramatic advances across different areas of machine learning, from representation learning to generative AI, now open an extraordinary opportunity to tackle each of these challenges and to transform both basic biology and drug discovery and development. A true impact will require a shift across the field, to become part of a “Lab in a Loop,” where experimental or clinical data is collected in order to train models, the models are used to predict the next set of experiments, and the process is iterated, at scale, both to yield key predictions in any specific project and to improve the model for all projects. In this talk, I will describe how initial Lab in a Loop approaches basic discovery and how we now extend them at Genentech across our target discovery, drug discovery, and drug development efforts to serve patients with autoimmune disease, neurodegeneration, infectious disease, and cancer.
Bio
Aviv Regev is the head of Genentech Research and Early Development. Prior to Genentech, Regev served as Chair of the Faculty, Core Member, and Founding director of the Klarman Cell observatory at the Broad Institute of MIT and Harvard, and as Professor of Biology at MIT and Investigator at the Howard Hughes Medical Institute. She is a founding co-chair of the Human Cell Atlas. Regev has pioneered foundational experimental and computational methods in single-cell genomics, and is a leader in deciphering molecular circuits that govern cells, tissues and organs in health and and disease. Among many honors, she is an elected member of the US National Academy of Sciences and National Academy of Medicine.
The seminar will take place in SV 1717 conf. room from 11:00 - 12:00
Online broadcast
Abstract
In many of the most pressing problems in biology, chemistry, and medicine—including the role of somatic mutations in cancer, genetic variants associated with the risk of common diseases, non-additive genetic interactions, possible antibody or functional protein sequences, or the space of drug-like chemical compounds—there exists an enormous space of possibilities, even though only very few of them are realized. In each of these cases, the number of hypothetical possibilities exceeds by many orders of magnitude what can ever be tested in a lab, clinical trial, or even an entire population. Historically, this challenge was tackled by restricting the search space using prior knowledge, a practical approach which nevertheless severely limited our ability to make new discoveries and effective predictions. Dramatic advancements over the past decade in our ability to perform both comprehensive, massively parallel, and high-resolution experiments now offer the hope that it should be possible to tackle these challenges systematically. However, in principle, an astronomical gap remains between the scale of experiments required to exhaustively map this space and those that can be achieved in practice. The dramatic advances across different areas of machine learning, from representation learning to generative AI, now open an extraordinary opportunity to tackle each of these challenges and to transform both basic biology and drug discovery and development. A true impact will require a shift across the field, to become part of a “Lab in a Loop,” where experimental or clinical data is collected in order to train models, the models are used to predict the next set of experiments, and the process is iterated, at scale, both to yield key predictions in any specific project and to improve the model for all projects. In this talk, I will describe how initial Lab in a Loop approaches basic discovery and how we now extend them at Genentech across our target discovery, drug discovery, and drug development efforts to serve patients with autoimmune disease, neurodegeneration, infectious disease, and cancer.
Bio
Aviv Regev is the head of Genentech Research and Early Development. Prior to Genentech, Regev served as Chair of the Faculty, Core Member, and Founding director of the Klarman Cell observatory at the Broad Institute of MIT and Harvard, and as Professor of Biology at MIT and Investigator at the Howard Hughes Medical Institute. She is a founding co-chair of the Human Cell Atlas. Regev has pioneered foundational experimental and computational methods in single-cell genomics, and is a leader in deciphering molecular circuits that govern cells, tissues and organs in health and and disease. Among many honors, she is an elected member of the US National Academy of Sciences and National Academy of Medicine.
The seminar will take place in SV 1717 conf. room from 11:00 - 12:00
Online broadcast
Practical information
- Informed public
- Free
- This event is internal
Organizer
- Deplancke Lab of Systems Biology and Genetics SV Deanship