Summary
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.
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| Web resources: | https://cordis.europa.eu/project/id/949267 |
| Start date: | 01-01-2021 |
| End date: | 31-12-2025 |
| Total budget - Public funding: | 1 957 751,00 Euro - 1 957 751,00 Euro |
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Original description
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.Status
SIGNEDCall topic
ERC-2020-STGUpdate Date
27-04-2024
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