From Physical Landscapes to Biological Design: Learning Organizing Principles with AI

Date

Feb 27, 2026

Time

11:00 AM - 12:00 PM

Speaker

Fabian Ruehle

Affiliation

Northeastern University, Dana Research Center, Boston

Language

en

Main Topic

Biologie

Host

Stephan Grill

Description

Modern molecular and multicellular biology is confronting the same conceptual bottlenecks that defined the great unsolved problems in theoretical physics: vast structured spaces of possibilities, dynamical principles underlying emergent behavior, and the need for interpretable frameworks rather than purely phenomenological fits. In physics, the string landscape demanded methods to navigate exponentially large solution spaces, effective field theory provided tools to extract governing equations at the right level of description, and dualities revealed that apparently distinct theories can encode the same physics. In biology, similarly enormous combinatorial spaces arise in perturbation screens, gene regulatory networks, tissue morphogenesis, and the design of emergent cellular systems. I will present a research program developed at the interface of mathematics, theoretical physics and machine learning, and argue that its methods are naturally suited to discovering organizing principles in biological data: navigate large landscapes with sparse samples, extract effective, interpretable laws directly from data, recognize equivalence classes of mechanisms rather than unique solutions, and invert design problems to generate candidate protocols for target phenotypes. I will argue that these approaches can be directly applied to key open questions in molecular biology, single-cell dynamics, organoid screens, morphogenesis, and programmable multicellular systems. This framework bridges physics, mathematics, machine learning, and biology, and has immediate opportunities for collaboration with the research groups at MPI-CBG.

Last modified: Feb 21, 2026, 7:36:06 AM