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Makhija E, Zheng Y, Wang J, Leong HR, Othman RB, Ng EX, Lee EH, Kellogg LT, Lee YH, Yu H, Poon Z, Van Vliet KJ. Topological defects in self-assembled patterns of mesenchymal stromal cells in vitro are predictive attributes of condensation and chondrogenesis. PLoS One 2024; 19:e0297769. [PMID: 38547243 PMCID: PMC10977694 DOI: 10.1371/journal.pone.0297769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 01/11/2024] [Indexed: 04/02/2024] Open
Abstract
Mesenchymal stromal cells (MSCs) are promising therapeutic agents for cartilage regeneration, including the potential of cells to promote chondrogenesis in vivo. However, process development and regulatory approval of MSCs as cell therapy products benefit from facile in vitro approaches that can predict potency for a given production run. Current standard in vitro approaches include a 21 day 3D differentiation assay followed by quantification of cartilage matrix proteins. We propose a novel biophysical marker that is cell population-based and can be measured from in vitro monolayer culture of MSCs. We hypothesized that the self-assembly pattern that emerges from collective-cell behavior would predict chondrogenesis motivated by our observation that certain features in this pattern, namely, topological defects, corresponded to mesenchymal condensations. Indeed, we observed a strong predictive correlation between the degree-of-order of the pattern at day 9 of the monolayer culture and chondrogenic potential later estimated from in vitro 3D chondrogenic differentiation at day 21. These findings provide the rationale and the proof-of-concept for using self-assembly patterns to monitor chondrogenic commitment of cell populations. Such correlations across multiple MSC donors and production batches suggest that self-assembly patterns can be used as a candidate biophysical attribute to predict quality and efficacy for MSCs employed therapeutically for cartilage regeneration.
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Affiliation(s)
- Ekta Makhija
- Critical Analytics for Manufacturing Personalized-medicine (CAMP) Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology (SMART), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Yang Zheng
- Critical Analytics for Manufacturing Personalized-medicine (CAMP) Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology (SMART), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- NUS Tissue Engineering Programme, Life Sciences Institute, National University of Singapore, Singapore, Singapore
- Department of Orthopaedic Surgery, National University of Singapore, Singapore, Singapore
| | - Jiahao Wang
- Mechanobiology Institute, National University of Singapore, Singapore, Singapore
| | - Han Ren Leong
- Critical Analytics for Manufacturing Personalized-medicine (CAMP) Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology (SMART), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Engineering Science Programme, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Rashidah Binte Othman
- Critical Analytics for Manufacturing Personalized-medicine (CAMP) Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology (SMART), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Ee Xien Ng
- Critical Analytics for Manufacturing Personalized-medicine (CAMP) Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology (SMART), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Eng Hin Lee
- Critical Analytics for Manufacturing Personalized-medicine (CAMP) Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology (SMART), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- NUS Tissue Engineering Programme, Life Sciences Institute, National University of Singapore, Singapore, Singapore
- Department of Orthopaedic Surgery, National University of Singapore, Singapore, Singapore
| | - Lisa Tucker Kellogg
- Critical Analytics for Manufacturing Personalized-medicine (CAMP) Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology (SMART), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
| | - Yie Hou Lee
- Critical Analytics for Manufacturing Personalized-medicine (CAMP) Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology (SMART), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Obstetrics and Gynaecology Academic Clinical Programme, SingHealth Duke-NUS, Singapore, Singapore
- SingHealth Duke-NUS Cell Therapy Centre, Singapore, Singapore
| | - Hanry Yu
- Critical Analytics for Manufacturing Personalized-medicine (CAMP) Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology (SMART), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Mechanobiology Institute, National University of Singapore, Singapore, Singapore
- Department of Physiology, National University of Singapore, Singapore, Singapore
- Institute of Bioengineering and Bioimaging, Agency for Science, Technology and Research, Singapore, Singapore
| | - Zhiyong Poon
- Critical Analytics for Manufacturing Personalized-medicine (CAMP) Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology (SMART), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- SingHealth Duke-NUS Cell Therapy Centre, Singapore, Singapore
- Department of Haematology, Singapore General Hospital, Singapore, Singapore
| | - Krystyn J. Van Vliet
- Critical Analytics for Manufacturing Personalized-medicine (CAMP) Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology (SMART), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Department of Materials Science and Engineering, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America
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2
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Killeen A, Bertrand T, Lee CF. Machine learning topological defects in confluent tissues. BIOPHYSICAL REPORTS 2024; 4:100142. [PMID: 38313863 PMCID: PMC10837480 DOI: 10.1016/j.bpr.2024.100142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 01/04/2024] [Indexed: 02/06/2024]
Abstract
Active nematics is an emerging paradigm for characterizing biological systems. One aspect of particularly intense focus is the role active nematic defects play in these systems, as they have been found to mediate a growing number of biological processes. Accurately detecting and classifying these defects in biological systems is, therefore, of vital importance to improving our understanding of such processes. While robust methods for defect detection exist for systems of elongated constituents, other systems, such as epithelial layers, are not well suited to such methods. Here, we address this problem by developing a convolutional neural network to detect and classify nematic defects in confluent cell layers. Crucially, our method is readily implementable on experimental images of cell layers and is specifically designed to be suitable for cells that are not rod shaped, which we demonstrate by detecting defects on experimental data using the trained model. We show that our machine learning model outperforms current defect detection techniques and that this manifests itself in our method as requiring less data to accurately capture defect properties. This could drastically improve the accuracy of experimental data interpretation while also reducing costs, advancing the study of nematic defects in biological systems.
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Affiliation(s)
- Andrew Killeen
- Department of Bioengineering, Imperial College London, South Kensington Campus, London, United Kingdom
| | - Thibault Bertrand
- Department of Mathematics, Imperial College London, South Kensington Campus, London, United Kingdom
| | - Chiu Fan Lee
- Department of Bioengineering, Imperial College London, South Kensington Campus, London, United Kingdom
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3
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Li Y, Zarei Z, Tran PN, Wang Y, Baskaran A, Fraden S, Hagan MF, Hong P. A machine learning approach to robustly determine director fields and analyze defects in active nematics. SOFT MATTER 2024; 20:1869-1883. [PMID: 38318759 DOI: 10.1039/d3sm01253k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Active nematics are dense systems of rodlike particles that consume energy to drive motion at the level of the individual particles. They exist in natural systems like biological tissues and artificial materials such as suspensions of self-propelled colloidal particles or synthetic microswimmers. Active nematics have attracted significant attention in recent years due to their spectacular nonequilibrium collective spatiotemporal dynamics, which may enable applications in fields such as robotics, drug delivery, and materials science. The director field, which measures the direction and degree of alignment of the local nematic orientation, is a crucial characteristic of active nematics and is essential for studying topological defects. However, determining the director field is a significant challenge in many experimental systems. Although director fields can be derived from images of active nematics using traditional imaging processing methods, the accuracy of such methods is highly sensitive to the settings of the algorithms. These settings must be tuned from image to image due to experimental noise, intrinsic noise of the imaging technology, and perturbations caused by changes in experimental conditions. This sensitivity currently limits automatic analysis of active nematics. To address this, we developed a machine learning model for extracting reliable director fields from raw experimental images, which enables accurate analysis of topological defects. Application of the algorithm to experimental data demonstrates that the approach is robust and highly generalizable to experimental settings that are different from those in the training data. It could be a promising tool for investigating active nematics and may be generalized to other active matter systems.
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Affiliation(s)
- Yunrui Li
- Computer Science Department, Brandeis University, USA.
| | - Zahra Zarei
- Physics Department, Brandeis University, USA
| | - Phu N Tran
- Physics Department, Brandeis University, USA
| | - Yifei Wang
- Computer Science Department, Brandeis University, USA.
| | | | - Seth Fraden
- Physics Department, Brandeis University, USA
| | | | - Pengyu Hong
- Computer Science Department, Brandeis University, USA.
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4
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Piven A, Darmoroz D, Skorb E, Orlova T. Machine learning methods for liquid crystal research: phases, textures, defects and physical properties. SOFT MATTER 2024; 20:1380-1391. [PMID: 38288719 DOI: 10.1039/d3sm01634j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Liquid crystal materials, with their unique properties and diverse applications, have long captured the attention of researchers and industries alike. From liquid crystal displays and electro-optical devices to advanced sensors and emerging technologies, the study and application of liquid crystals continue to be of paramount importance in the fields of materials science, chemistry and physics. With the ever-increasing complexity and diversity of liquid crystal materials, researchers face new challenges in understanding their behaviors, properties, and potential applications. On the other hand, machine learning, a rapidly evolving interdisciplinary field at the intersection of computer science and data analysis, has already become a powerful tool for unraveling implicit correlations and predicting new properties of a wide variety of physical and chemical systems and structures. Here we aim to consider how machine learning methods are suitable for solving fundamental problems in the field of liquid crystals and what are the advantages of this artificial intelligence based approach.
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Affiliation(s)
- Anastasiia Piven
- Infochemistry Scientific Center, ITMO University, Saint-Petersburg, Russia.
| | - Darina Darmoroz
- Infochemistry Scientific Center, ITMO University, Saint-Petersburg, Russia.
| | - Ekaterina Skorb
- Infochemistry Scientific Center, ITMO University, Saint-Petersburg, Russia.
| | - Tetiana Orlova
- Infochemistry Scientific Center, ITMO University, Saint-Petersburg, Russia.
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5
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McGorty RJ, Currie CJ, Michel J, Sasanpour M, Gunter C, Lindsay KA, Rust MJ, Katira P, Das M, Ross JL, Robertson-Anderson RM. Kinesin and myosin motors compete to drive rich multiphase dynamics in programmable cytoskeletal composites. PNAS NEXUS 2023; 2:pgad245. [PMID: 37575673 PMCID: PMC10416814 DOI: 10.1093/pnasnexus/pgad245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 07/07/2023] [Accepted: 07/17/2023] [Indexed: 08/15/2023]
Abstract
The cellular cytoskeleton relies on diverse populations of motors, filaments, and binding proteins acting in concert to enable nonequilibrium processes ranging from mitosis to chemotaxis. The cytoskeleton's versatile reconfigurability, programmed by interactions between its constituents, makes it a foundational active matter platform. However, current active matter endeavors are limited largely to single force-generating components acting on a single substrate-far from the composite cytoskeleton in cells. Here, we engineer actin-microtubule (MT) composites, driven by kinesin and myosin motors and tuned by crosslinkers, to ballistically restructure and flow with speeds that span three orders of magnitude depending on the composite formulation and time relative to the onset of motor activity. Differential dynamic microscopy analyses reveal that kinesin and myosin compete to delay the onset of acceleration and suppress discrete restructuring events, while passive crosslinking of either actin or MTs has an opposite effect. Our minimal advection-diffusion model and spatial correlation analyses correlate these dynamics to structure, with motor antagonism suppressing reconfiguration and demixing, while crosslinking enhances clustering. Despite the rich formulation space and emergent formulation-dependent structures, the nonequilibrium dynamics across all composites and timescales can be organized into three classes-slow isotropic reorientation, fast directional flow, and multimode restructuring. Moreover, our mathematical model demonstrates that diverse structural motifs can arise simply from the interplay between motor-driven advection and frictional drag. These general features of our platform facilitate applicability to other active matter systems and shed light on diverse ways that cytoskeletal components can cooperate or compete to enable wide-ranging cellular processes.
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Affiliation(s)
- Ryan J McGorty
- Department of Physics and Biophysics, University of San Diego, San Diego, CA 92110, USA
| | - Christopher J Currie
- Department of Physics and Biophysics, University of San Diego, San Diego, CA 92110, USA
| | - Jonathan Michel
- School of Physics and Astronomy, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Mehrzad Sasanpour
- Department of Physics and Biophysics, University of San Diego, San Diego, CA 92110, USA
| | - Christopher Gunter
- Department of Mechanical Engineering, San Diego State University, San Diego, CA 92182, USA
| | - K Alice Lindsay
- Department of Physics, Syracuse University, Syracuse, NY 13244, USA
| | - Michael J Rust
- Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL 60637, USA
| | - Parag Katira
- Department of Mechanical Engineering, San Diego State University, San Diego, CA 92182, USA
| | - Moumita Das
- School of Physics and Astronomy, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Jennifer L Ross
- Department of Physics, Syracuse University, Syracuse, NY 13244, USA
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6
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Golden M, Grigoriev RO, Nambisan J, Fernandez-Nieves A. Physically informed data-driven modeling of active nematics. SCIENCE ADVANCES 2023; 9:eabq6120. [PMID: 37406118 PMCID: PMC10321743 DOI: 10.1126/sciadv.abq6120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 05/31/2023] [Indexed: 07/07/2023]
Abstract
A continuum description is essential for understanding a variety of collective phenomena in active matter. However, building quantitative continuum models of active matter from first principles can be extremely challenging due to both the gaps in our knowledge and the complicated structure of nonlinear interactions. Here, we use a physically informed data-driven approach to construct a complete mathematical model of an active nematic from experimental data describing kinesin-driven microtubule bundles confined to an oil-water interface. We find that the structure of the model is similar to the Leslie-Ericksen and Beris-Edwards models, but there are appreciable and important differences. Rather unexpectedly, elastic effects are found to play no role in the experiments considered, with the dynamics controlled entirely by the balance between active stresses and friction stresses.
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Affiliation(s)
- Matthew Golden
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Roman O. Grigoriev
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Jyothishraj Nambisan
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Department of Condensed Matter Physics, University of Barcelona, Barcelona 08028, Spain
- Institute of Complex Systems (UBICS), University of Barcelona, Barcelona 08028, Spain
| | - Alberto Fernandez-Nieves
- Department of Condensed Matter Physics, University of Barcelona, Barcelona 08028, Spain
- Institute of Complex Systems (UBICS), University of Barcelona, Barcelona 08028, Spain
- ICREA-Institució Catalanade Recerca i Estudis Avançats, Barcelona 08010, Spain
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7
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Ray S, Zhang J, Dogic Z. Rectified Rotational Dynamics of Mobile Inclusions in Two-Dimensional Active Nematics. PHYSICAL REVIEW LETTERS 2023; 130:238301. [PMID: 37354394 DOI: 10.1103/physrevlett.130.238301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 04/14/2023] [Indexed: 06/26/2023]
Abstract
We investigate the dynamics of mobile inclusions embedded in 2D active nematics. The interplay between the inclusion shape, boundary-induced nematic order, and autonomous flows powers the inclusion motion. Disks and achiral gears exhibit unbiased rotational motion, but with distinct dynamics. In comparison, chiral gear-shaped inclusions exhibit long-term rectified rotation, which is correlated with dynamics and polarization of nearby +1/2 topological defects. The chirality of defect polarities and the active nematic texture around the inclusion correlate with the inclusion's instantaneous rotation rate. Inclusions provide a promising tool for probing the rheological properties of active nematics and extracting ordered motion from their inherently chaotic motion.
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Affiliation(s)
- Sattvic Ray
- Department of Physics, University of California at Santa Barbara, Santa Barbara, California 93106, USA
| | - Jie Zhang
- Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China (USTC), 230026 Hefei, China
- Department of Polymer Science and Engineering, CAS Key Laboratory of Soft Matter Chemistry, University of Science and Technology of China (USTC), 230026 Hefei, China
| | - Zvonimir Dogic
- Department of Physics, University of California at Santa Barbara, Santa Barbara, California 93106, USA
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8
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Zaplotnik J, Pišljar J, Škarabot M, Ravnik M. Neural networks determination of material elastic constants and structures in nematic complex fluids. Sci Rep 2023; 13:6028. [PMID: 37055564 PMCID: PMC10102156 DOI: 10.1038/s41598-023-33134-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/07/2023] [Indexed: 04/15/2023] Open
Abstract
Supervised machine learning and artificial neural network approaches can allow for the determination of selected material parameters or structures from a measurable signal without knowing the exact mathematical relationship between them. Here, we demonstrate that material nematic elastic constants and the initial structural material configuration can be found using sequential neural networks applied to the transmmited time-dependent light intensity through the nematic liquid crystal (NLC) sample under crossed polarizers. Specifically, we simulate multiple times the relaxation of the NLC from a random (qeunched) initial state to the equilibirum for random values of elastic constants and, simultaneously, the transmittance of the sample for monochromatic polarized light. The obtained time-dependent light transmittances and the corresponding elastic constants form a training data set on which the neural network is trained, which allows for the determination of the elastic constants, as well as the initial state of the director. Finally, we demonstrate that the neural network trained on numerically generated examples can also be used to determine elastic constants from experimentally measured data, finding good agreement between experiments and neural network predictions.
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Affiliation(s)
- Jaka Zaplotnik
- Faculty of Mathematics and Physics, University of Ljubljana, 1000, Ljubljana, Slovenia.
- Jožef Stefan Institute, 1000, Ljubljana, Slovenia.
| | - Jaka Pišljar
- Jožef Stefan Institute, 1000, Ljubljana, Slovenia
| | | | - Miha Ravnik
- Faculty of Mathematics and Physics, University of Ljubljana, 1000, Ljubljana, Slovenia
- Jožef Stefan Institute, 1000, Ljubljana, Slovenia
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9
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Abstract
Matter self-assembling into layers generates unique properties, including structures of stacked surfaces, directed transport, and compact area maximization that can be highly functionalized in biology and technology. Smectics represent the paradigm of such lamellar materials - they are a state between fluids and solids, characterized by both orientational and partial positional ordering in one layering direction, making them notoriously difficult to model, particularly in confining geometries. We propose a complex tensor order parameter to describe the local degree of lamellar ordering, layer displacement and orientation of the layers for simple, lamellar smectics. The theory accounts for both dislocations and disclinations, by regularizing singularities within defect cores and so remaining continuous everywhere. The ability to describe disclinations and dislocation allows this theory to simulate arrested configurations and inclusion-induced local ordering. This tensorial theory for simple smectics considerably simplifies numerics, facilitating studies on the mesoscopic structure of topologically complex systems.
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10
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Joshi C, Ray S, Lemma LM, Varghese M, Sharp G, Dogic Z, Baskaran A, Hagan MF. Data-Driven Discovery of Active Nematic Hydrodynamics. PHYSICAL REVIEW LETTERS 2022; 129:258001. [PMID: 36608242 DOI: 10.1103/physrevlett.129.258001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 11/14/2022] [Indexed: 06/17/2023]
Abstract
Active nematics can be modeled using phenomenological continuum theories that account for the dynamics of the nematic director and fluid velocity through partial differential equations (PDEs). While these models provide a statistical description of the experiments, the relevant terms in the PDEs and their parameters are usually identified indirectly. We adapt a recently developed method to automatically identify optimal continuum models for active nematics directly from spatiotemporal data, via sparse regression of the coarse-grained fields onto generic low order PDEs. After extensive benchmarking, we apply the method to experiments with microtubule-based active nematics, finding a surprisingly minimal description of the system. Our approach can be generalized to gain insights into active gels, microswimmers, and diverse other experimental active matter systems.
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Affiliation(s)
- Chaitanya Joshi
- Department of Physics, Brandeis University, Waltham, Massachusetts 02453, USA
- Department of Physics and Astronomy, Tufts University, Medford, Massachusetts 02155, USA
| | - Sattvic Ray
- Department of Physics, University of California at Santa Barbara, Santa Barbara, California 93106, USA
| | - Linnea M Lemma
- Department of Physics, Brandeis University, Waltham, Massachusetts 02453, USA
- Department of Physics, University of California at Santa Barbara, Santa Barbara, California 93106, USA
| | - Minu Varghese
- Department of Physics, Brandeis University, Waltham, Massachusetts 02453, USA
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109 USA
| | - Graham Sharp
- Department of Physics, University of California at Santa Barbara, Santa Barbara, California 93106, USA
| | - Zvonimir Dogic
- Department of Physics, University of California at Santa Barbara, Santa Barbara, California 93106, USA
| | - Aparna Baskaran
- Department of Physics, Brandeis University, Waltham, Massachusetts 02453, USA
| | - Michael F Hagan
- Department of Physics, Brandeis University, Waltham, Massachusetts 02453, USA
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11
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Najma B, Varghese M, Tsidilkovski L, Lemma L, Baskaran A, Duclos G. Competing instabilities reveal how to rationally design and control active crosslinked gels. Nat Commun 2022; 13:6465. [PMID: 36309493 PMCID: PMC9617906 DOI: 10.1038/s41467-022-34089-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 10/13/2022] [Indexed: 12/25/2022] Open
Abstract
How active stresses generated by molecular motors set the large-scale mechanics of the cell cytoskeleton remains poorly understood. Here, we combine experiments and theory to demonstrate how the emergent properties of a biomimetic active crosslinked gel depend on the properties of its microscopic constituents. We show that an extensile nematic elastomer exhibits two distinct activity-driven instabilities, spontaneously bending in-plane or buckling out-of-plane depending on its composition. Molecular motors play a dual antagonistic role, fluidizing or stiffening the gel depending on the ATP concentration. We demonstrate how active and elastic stresses are set by each component, providing estimates for the active gel theory parameters. Finally, activity and elasticity were manipulated in situ with light-activable motor proteins, controlling the direction of the instability optically. These results highlight how cytoskeletal stresses regulate the self-organization of living matter and set the foundations for the rational design and optogenetic control of active materials.
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Affiliation(s)
- Bibi Najma
- grid.253264.40000 0004 1936 9473Department of Physics, Brandeis University, Waltham, MA 02453 USA
| | - Minu Varghese
- grid.253264.40000 0004 1936 9473Department of Physics, Brandeis University, Waltham, MA 02453 USA ,grid.214458.e0000000086837370Department of Physics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Lev Tsidilkovski
- grid.253264.40000 0004 1936 9473Department of Physics, Brandeis University, Waltham, MA 02453 USA
| | - Linnea Lemma
- grid.253264.40000 0004 1936 9473Department of Physics, Brandeis University, Waltham, MA 02453 USA ,grid.133342.40000 0004 1936 9676Department of Physics, University of California at Santa Barbara, Santa Barbara, CA 93106 USA ,grid.16750.350000 0001 2097 5006Present Address: Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544 USA
| | - Aparna Baskaran
- grid.253264.40000 0004 1936 9473Department of Physics, Brandeis University, Waltham, MA 02453 USA
| | - Guillaume Duclos
- grid.253264.40000 0004 1936 9473Department of Physics, Brandeis University, Waltham, MA 02453 USA
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12
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Wang H, Zou B, Su J, Wang D, Xu X. Variational methods and deep Ritz method for active elastic solids. SOFT MATTER 2022; 18:6015-6031. [PMID: 35920447 DOI: 10.1039/d2sm00404f] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Variational methods have been widely used in soft matter physics for both static and dynamic problems. These methods are mostly based on two variational principles: the variational principle of minimum free energy (MFEVP) and Onsager's variational principle (OVP). Our interests lie in the applications of these variational methods to active matter physics. In our former work [H. Wang, T. Qian and X. Xu, Soft Matter, 2021, 17, 3634-3653], we have explored the applications of OVP-based variational methods for the modeling of active matter dynamics. In the present work, we explore variational (or energy) methods that are based on MFEVP for static problems in active elastic solids. We show that MFEVP can be used not only to derive equilibrium equations, but also to develop approximate solution methods, such as the Ritz method, for active solid statics. Moreover, the power of the Ritz-type method can be further enhanced using deep learning methods if we use deep neural networks to construct the trial functions of the variational problems. We then apply these variational methods and the deep Ritz method to study the spontaneous bending and contraction of a thin active circular plate that is induced by internal asymmetric active contraction. The circular plate is found to be bent towards its contracting side. The study of such a simple toy system gives implications for understanding the morphogenesis of solid-like confluent cell monolayers. In addition, we introduce a so-called activogravity length to characterize the importance of gravitational forces relative to internal active contraction in driving the bending of the active plate. When the lateral plate dimension is larger than the activogravity length (about 100 micron), gravitational forces become important. Such gravitaxis behaviors at multicellular scales may play significant roles in the morphogenesis and in the up-down symmetry broken during tissue development.
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Affiliation(s)
- Haiqin Wang
- Physics Program, Guangdong Technion - Israel Institute of Technology, 241 Daxue Road, Shantou, Guangdong, 515063, China.
- Technion - Israel Institute of Technology, Haifa, 32000, Israel
| | - Boyi Zou
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China
| | - Jian Su
- Physics Program, Guangdong Technion - Israel Institute of Technology, 241 Daxue Road, Shantou, Guangdong, 515063, China.
| | - Dong Wang
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China
- Shenzhen International Center for Industrial and Applied Mathematics, Shenzhen Research Institute of Big Data, Shenzhen, Guangdong, 518172, China
| | - Xinpeng Xu
- Physics Program, Guangdong Technion - Israel Institute of Technology, 241 Daxue Road, Shantou, Guangdong, 515063, China.
- Technion - Israel Institute of Technology, Haifa, 32000, Israel
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13
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Submersed micropatterned structures control active nematic flow, topology, and concentration. Proc Natl Acad Sci U S A 2021; 118:2106038118. [PMID: 34535551 DOI: 10.1073/pnas.2106038118] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/24/2021] [Indexed: 01/10/2023] Open
Abstract
Coupling between flows and material properties imbues rheological matter with its wide-ranging applicability, hence the excitement for harnessing the rheology of active fluids for which internal structure and continuous energy injection lead to spontaneous flows and complex, out-of-equilibrium dynamics. We propose and demonstrate a convenient, highly tunable method for controlling flow, topology, and composition within active films. Our approach establishes rheological coupling via the indirect presence of fully submersed micropatterned structures within a thin, underlying oil layer. Simulations reveal that micropatterned structures produce effective virtual boundaries within the superjacent active nematic film due to differences in viscous dissipation as a function of depth. This accessible method of applying position-dependent, effective dissipation to the active films presents a nonintrusive pathway for engineering active microfluidic systems.
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Learning active nematics one step at a time. Proc Natl Acad Sci U S A 2021; 118:2102169118. [PMID: 33707217 DOI: 10.1073/pnas.2102169118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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