Learning developmental mode dynamics from single-cell trajectories DOI Creative Commons
Nicolas Romeo, Alasdair Hastewell, Alexander Mietke

et al.

eLife, Journal Year: 2021, Volume and Issue: 10

Published: Dec. 29, 2021

Embryogenesis is a multiscale process during which developmental symmetry breaking transitions give rise to complex multicellular organisms. Recent advances in high-resolution live-cell microscopy provide unprecedented insights into the collective cell dynamics at various stages of embryonic development. This rapid experimental progress poses theoretical challenge translating high-dimensional imaging data predictive low-dimensional models that capture essential ordering principles governing migration geometries. Here, we combine mode decomposition ideas have proved successful condensed matter physics and turbulence theory with recent sparse dynamical systems inference realize computational framework for learning quantitative continuum from single-cell data. Considering pan-embryo early gastrulation zebrafish as widely studied example, show how trajectory on curved surface can be coarse-grained compressed suitable harmonic basis functions. The resulting representation enables compact characterization direct an interpretable hydrodynamic model, reveals similarities between active Brownian particle surfaces. Due its generic conceptual foundation, expect mode-based model help advance biophysical understanding wide range structure formation processes.

Language: Английский

Achieving designed texture and flows in bulk active nematics using optimal control theory DOI
Saptorshi Ghosh, Aparna Baskaran, Michael F. Hagan

et al.

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(13)

Published: April 1, 2025

Being intrinsically nonequilibrium, active materials can potentially perform functions that would be thermodynamically forbidden in passive materials. However, systems have diverse local attractors correspond to distinct dynamical states, many of which exhibit chaotic turbulent-like dynamics and thus cannot work or useful functions. Designing such a system choose specific state is formidable challenge. Motivated by recent advances enabling optogenetic control experimental materials, we describe an optimal theory framework identifies spatiotemporal sequence light-generated activity drives nematic toward prescribed steady state. Active nematics are unstable spontaneous defect proliferation streaming the absence control. We demonstrate compute fields redirect into variety alternative programs This includes dynamically reconfiguring between selecting stabilizing emergent behaviors do not attractors, hence uncontrolled system. Our results provide roadmap leverage optical methods rationally design structure, dynamics, function wide

Language: Английский

Citations

0
On the Numerical Integration of the Multidimensional Kuramoto Model DOI
Marcus A. M. de Aguiar

Brazilian Journal of Physics, Journal Year: 2024, Volume and Issue: 54(4)

Published: May 29, 2024

Language: Английский

Citations

3
Interpreting Neural Operators: How Nonlinear Waves Propagate in Nonreciprocal Solids DOI
Jonathan Colen, Alexis Poncet, Denis Bartolo

et al.

Physical Review Letters, Journal Year: 2024, Volume and Issue: 133(10)

Published: Sept. 3, 2024

We present a data-driven pipeline for model building that combines interpretable machine learning, hydrodynamic theories, and microscopic models. The goal is to uncover the underlying processes governing nonlinear dynamics experiments. exemplify our method with data from microfluidic experiments where crystals of streaming droplets support propagation waves absent in passive crystals. By combining physics-inspired neural networks, known as operators, symbolic regression tools, we infer solution, well mathematical form, dynamical system accurately models experimental data. Finally, interpret this continuum fundamental physics principles. Informed by coarse grain interacting discover nonreciprocal interactions stabilize promote wave propagation.

Language: Английский

Citations

3
Discovering dynamics and parameters of nonlinear oscillatory and chaotic systems from partial observations DOI Creative Commons
George Stepaniants, Alasdair Hastewell, Dominic J. Skinner

et al.

Physical Review Research, Journal Year: 2024, Volume and Issue: 6(4)

Published: Oct. 23, 2024

Despite rapid progress in data acquisition techniques, many complex physical, chemical, and biological systems remain only partially observable, thus posing the challenge to identify valid theoretical models estimate their parameters from an incomplete set of experimentally accessible time series. Here, we combine sensitivity methods ranked-choice model selection construct automated hidden dynamics inference framework that can discover predictive nonlinear dynamical for both observable latent variables noise-corrupted oscillatory chaotic systems. After validating prototypical FitzHugh-Nagumo oscillations, demonstrate its applicability experimental squid neuron activity measurements Belousov-Zhabotinsky reactions, as well Lorenz system regime. Published by American Physical Society 2024

Language: Английский

Citations

3
Learning developmental mode dynamics from single-cell trajectories DOI Creative Commons
Nicolas Romeo, Alasdair Hastewell, Alexander Mietke

et al.

eLife, Journal Year: 2021, Volume and Issue: 10

Published: Dec. 29, 2021

Embryogenesis is a multiscale process during which developmental symmetry breaking transitions give rise to complex multicellular organisms. Recent advances in high-resolution live-cell microscopy provide unprecedented insights into the collective cell dynamics at various stages of embryonic development. This rapid experimental progress poses theoretical challenge translating high-dimensional imaging data predictive low-dimensional models that capture essential ordering principles governing migration geometries. Here, we combine mode decomposition ideas have proved successful condensed matter physics and turbulence theory with recent sparse dynamical systems inference realize computational framework for learning quantitative continuum from single-cell data. Considering pan-embryo early gastrulation zebrafish as widely studied example, show how trajectory on curved surface can be coarse-grained compressed suitable harmonic basis functions. The resulting representation enables compact characterization direct an interpretable hydrodynamic model, reveals similarities between active Brownian particle surfaces. Due its generic conceptual foundation, expect mode-based model help advance biophysical understanding wide range structure formation processes.

Language: Английский

Citations

19