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

The transformative potential of machine learning for experiments in fluid mechanics DOI
Ricardo Vinuesa, Steven L. Brunton, Beverley McKeon

et al.

Nature Reviews Physics, Journal Year: 2023, Volume and Issue: 5(9), P. 536 - 545

Published: Aug. 10, 2023

Language: Английский

Citations

63
Machine learning interpretable models of cell mechanics from protein images DOI Creative Commons
Matthew S. Schmitt, Jonathan Colen,

Stefano Sala

et al.

Cell, Journal Year: 2024, Volume and Issue: 187(2), P. 481 - 494.e24

Published: Jan. 1, 2024

Language: Английский

Citations

25
Promising directions of machine learning for partial differential equations DOI
Steven L. Brunton,

J. Nathan Kutz

Nature Computational Science, Journal Year: 2024, Volume and Issue: 4(7), P. 483 - 494

Published: June 28, 2024

Language: Английский

Citations

20
Microrobots for Biomedicine: Unsolved Challenges and Opportunities for Translation DOI
Jin Gyun Lee, Ritu R. Raj, Nicole B. Day

et al.

ACS Nano, Journal Year: 2023, Volume and Issue: 17(15), P. 14196 - 14204

Published: July 26, 2023

Microrobots are being explored for biomedical applications, such as drug delivery, biological cargo transport, and minimally invasive surgery. However, current efforts largely focus on proof-of-concept studies with nontranslatable materials through a "design-and-apply" approach, limiting the potential clinical adaptation. While these have been key to advancing microrobot technologies, we believe that distinguishing capabilities of microrobots will be most readily brought patient bedsides "design-by-problem" which involves focusing unsolved problems inform design practical capabilities. As outlined below, propose translation accelerated by judicious choice target improved delivery considerations, rational selection translation-ready biomaterials, ultimately reducing burden enhancing efficacy therapeutic drugs difficult-to-treat diseases.

Language: Английский

Citations

35
Self-enhanced mobility enables vortex pattern formation in living matter DOI
Haoran Xu, Yilin Wu

Nature, Journal Year: 2024, Volume and Issue: 627(8004), P. 553 - 558

Published: March 13, 2024

Language: Английский

Citations

14
Learning dynamical models of single and collective cell migration: a review DOI Creative Commons
David B. Brückner,

Chase P. Broedersz

Reports on Progress in Physics, Journal Year: 2024, Volume and Issue: 87(5), P. 056601 - 056601

Published: March 22, 2024

Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development immune response to wound healing cancer metastasis. To understand a physical perspective, broad variety of models the underlying mechanisms that govern motility have been developed. A key challenge in such is how connect them experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances data-driven theoretical approaches directly with data infer dynamical migration. Leveraging nanofabrication, image analysis, tracking technology, studies now provide unprecedented large datasets on cellular dynamics. parallel, efforts directed towards integrating into single tissue scale aim conceptualising emergent behaviour cells. We first review inference problem has addressed both freely migrating confined Next, why these dynamics typically take form underdamped equations motion, can be inferred data. then applications machine learning heterogeneity behaviour, subcellular degrees freedom, multicellular systems. Across applications, emphasise methods integrated active matter cells, help reveal molecular control behaviour. Together, promising avenue building data, providing conceptual links between different length-scales description.

Language: Английский

Citations

12
Machine Learning in Soft Matter: From Simulations to Experiments DOI
Kaihua Zhang,

Xiangrui Gong,

Ying Jiang

et al.

Advanced Functional Materials, Journal Year: 2024, Volume and Issue: 34(24)

Published: Jan. 31, 2024

Abstract Soft matter with diverse functionalities that are easily designable has fascinated tremendous research interests in the past several decades. Nevertheless, inherent confluence of time and length scale ubiquitous soft immensely complicates elucidation structure–property relationship thereby severely impedes function exploration materials. Recently, emergent machine learning (ML) techniques open new paradigms property prediction molecular design functional materials, due to their extraordinarily distinguished performance aspect trend identity pattern extraction from data, objective optimization by accelerating guided search high‐dimensional spaces. This review exclusively focuses on current state‐of‐the‐art progress development ML applied realms matter, ranging coarse‐grained simulations theoretical structural formation macroscopic properties, as well algorithm‐aided experiments. Finally, an outlook challenges opportunities for this rapidly evolving field is discussed.

Language: Английский

Citations

9
Inference of Onsager coefficient from microscopic simulations by machine learning DOI

Kaihua Zhang,

Shuanhu Qi, Yongzhi Ren

et al.

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(3)

Published: Jan. 15, 2025

Dynamic density functional theory (DDFT) is a fruitful approach for modeling polymer dynamics, benefiting from its multiscale and hybrid nature. However, the Onsager coefficient, only free parameter in DDFT, primarily derived empirically, limiting accuracy broad application of DDFT. Herein, we propose machine learning-based, bottom-up workflow to directly extract coefficient molecular simulations, circumventing partly heuristic assumptions traditional approaches. In this workflow, proposed DDFT-informed ordinary differential equation network, trained replicate evolution observed Brownian dynamics (BD) simulations. We validate our method by studying lamellar transition symmetric diblock copolymer melts, where DDFT model with extracted precisely reproduces both interface narrowing predicted BD thereby demonstrating reliability present scheme. Meanwhile, studies reveal strong relevance dynamic processes identify explicit connection between correlations, characterized correlation strength length, system parameters, e.g., Flory-Huggins interaction parameter. found that far point, transmits thermodynamic force into current localized strong, while close it becomes long-ranged but weak. Our aims develop more generalized framework bridge refined particle-based simulations coarse-grained field-based calculations, insights gained using could be extended other non-equilibrium systems sciences.

Language: Английский

Citations

1
Vortex reversal is a precursor of confined bacterial turbulence DOI Creative Commons
Daiki Nishiguchi,

Sora Shiratani,

Kazumasa A. Takeuchi

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2025, Volume and Issue: 122(11)

Published: March 14, 2025

Active turbulence, or chaotic self-organized collective motion, is often observed in concentrated suspensions of motile bacteria and other systems self-propelled interacting agents. To date, there no fundamental understanding how geometrical confinement orchestrates active turbulence alters its physical properties. Here, by combining large-scale experiments, computer modeling, analytical theory, we have identified a generic sequence transitions occurring bacterial confined cylindrical wells varying radii. With increasing the well’s radius, that persistent vortex motion gives way to periodic reversals, four-vortex pulsations, then well-developed turbulence. Using computational modeling shown reversal results from nonlinear interaction first three azimuthal modes become unstable with radius increase. The account for our key experimental findings. further validate approach, reconstructed equations data. Our findings shed light on universal properties matter can be applied various biological synthetic systems.

Language: Английский

Citations

1
Activity waves and freestanding vortices in populations of subcritical Quincke rollers DOI Open Access

Zeng Tao Liu,

Yan Shi, Yongfeng Zhao

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2021, Volume and Issue: 118(40)

Published: Sept. 29, 2021

Significance Active matter is made of units that move or displace others by using energy stored internally gathered from their environment. In most systems and models considered so far, these self-propelled are constantly moving. Here we study active do not when isolated, but can be set into motion close neighbors. Our subcritical consists Quincke rollers, is, colloidal spheres at the bottom a cell filled with conducting fluid submitted to vertical electric field. We find spectacular collective self-organized phenomena: activity waves propagating in quiescent population, arbitrarily large, steadily rotating vortices forming without confinement particle chirality.

Language: Английский

Citations

43