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Reverse-engineering the development of embryos with physics-informed machine learning

Objective

Embryogenesis is archetypal of a self-organized process, where the emergence of a complex structure stems from the interaction of its elementary parts. Progress in imaging and molecular genetics allow us to delve into embryos at unprecedented spatiotemporal resolutions, but extracting biophysical information from this complex multidimensional data is a highly technical challenge. As a result the principles of multicellular self-organization remain far from understood. DeepEmbryo proposes to fill this gap by pioneering the use of deep learning to reverse-engineer early embryo development directly from high-resolution 3D microscopy movies. Focusing on four animal groups (mammals, ascidians, nematodes and annelids), the project will combine physical modeling and machine learning to tackle three fundamental questions from a unique transversal perspective: Q1 What are the forces shaping early embryos? Using convolutional neural networks, I will develop an automated method to directly infer cell forces from membrane-labeled images of embryos. Q2 How do cells coordinate forces, division and signaling? Regarding cells as dynamical systems, I will model them with minimal neural networks and design a multi-agent embryo model able to learn by reinforcement the fundamental feedback controls between mechanics and fate. Q3 What principles ensure developmental robustness? Using deep generative models, I will infer intra-specie developmental variability to identify robust developmental traits and mechanisms. Using dropout techniques as virtual analog to genetic knockout, I will produce experimentally testable new predictions, refining my inaugural virtual embryos. Pioneering a new field at the frontier of developmental biology, artificial intelligence and physics, DeepEmbryo will uncover the fundamental engineering principles of early embryogenesis, with far-reaching implications in multi-agent modeling, evolutionary biology, physical inference and tissue engineering.

Field of science

  • /natural sciences/biological sciences/genetics and heredity
  • /natural sciences/biological sciences/evolutionary biology
  • /natural sciences/biological sciences/molecular biology/molecular genetics
  • /natural sciences/biological sciences/developmental biology
  • /natural sciences/computer and information sciences/artificial intelligence/computational intelligence
  • /natural sciences/mathematics/applied mathematics/dynamical systems
  • /natural sciences/biological sciences/zoology/invertebrate zoology
  • /medical and health sciences/medical biotechnology/tissue engineering
  • /natural sciences/computer and information sciences/artificial intelligence/machine learning/deep learning
  • /medical and health sciences/clinical medicine/embryology

Call for proposal

ERC-2020-STG
See other projects for this call

Funding Scheme

ERC-STG - Starting Grant

Host institution

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Address
Rue Michel Ange 3
75794 Paris
France
Activity type
Research Organisations
EU contribution
€ 1 957 751

Beneficiaries (1)

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
France
EU contribution
€ 1 957 751
Address
Rue Michel Ange 3
75794 Paris
Activity type
Research Organisations