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  1. Article ; Online: Assembling Microtubule-Based Active Matter.

    Tayar, Alexandra M / Lemma, Linnea M / Dogic, Zvonimir

    Methods in molecular biology (Clifton, N.J.)

    2022  Volume 2430, Page(s) 151–183

    Abstract: Studied for more than a century, equilibrium liquid crystals provided insight into the properties of ordered materials, and led to commonplace applications such as display technology. Active nematics are a new class of liquid crystal materials that are ... ...

    Abstract Studied for more than a century, equilibrium liquid crystals provided insight into the properties of ordered materials, and led to commonplace applications such as display technology. Active nematics are a new class of liquid crystal materials that are driven out of equilibrium by continuous motion of the constituent anisotropic units. A versatile experimental realization of active nematic liquid crystals is based on rod-like cytoskeletal filaments that are driven out of equilibrium by molecular motors. We describe protocols for assembling microtubule-kinesin based active nematic liquid crystals and associated isotropic fluids. We describe the purification of each protein and the assembly process of a two-dimensional active nematic on a water-oil interface. Finally, we show examples of nematic formation and describe methods for quantifying their non-equilibrium dynamics.
    MeSH term(s) Anisotropy ; Cytoskeleton ; Kinesins ; Liquid Crystals/chemistry ; Microtubules/chemistry
    Chemical Substances Kinesins (EC 3.6.4.4)
    Language English
    Publishing date 2022-04-27
    Publishing country United States
    Document type Journal Article
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-0716-1983-4_10
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Spatio-temporal patterning of extensile active stresses in microtubule-based active fluids.

    Lemma, Linnea M / Varghese, Minu / Ross, Tyler D / Thomson, Matt / Baskaran, Aparna / Dogic, Zvonimir

    PNAS nexus

    2023  Volume 2, Issue 5, Page(s) pgad130

    Abstract: Microtubule-based active fluids exhibit turbulent-like autonomous flows, which are driven by the molecular motor powered motion of filamentous constituents. Controlling active stresses in space and time is an essential prerequisite for controlling the ... ...

    Abstract Microtubule-based active fluids exhibit turbulent-like autonomous flows, which are driven by the molecular motor powered motion of filamentous constituents. Controlling active stresses in space and time is an essential prerequisite for controlling the intrinsically chaotic dynamics of extensile active fluids. We design single-headed kinesin molecular motors that exhibit optically enhanced clustering and thus enable precise and repeatable spatial and temporal control of extensile active stresses. Such motors enable rapid, reversible switching between flowing and quiescent states. In turn, spatio-temporal patterning of the active stress controls the evolution of the ubiquitous bend instability of extensile active fluids and determines its critical length dependence. Combining optically controlled clusters with conventional kinesin motors enables one-time switching from contractile to extensile active stresses. These results open a path towards real-time control of the autonomous flows generated by active fluids.
    Language English
    Publishing date 2023-04-12
    Publishing country England
    Document type Journal Article
    ISSN 2752-6542
    ISSN (online) 2752-6542
    DOI 10.1093/pnasnexus/pgad130
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  3. Article ; Online: Multiscale Microtubule Dynamics in Active Nematics.

    Lemma, Linnea M / Norton, Michael M / Tayar, Alexandra M / DeCamp, Stephen J / Aghvami, S Ali / Fraden, Seth / Hagan, Michael F / Dogic, Zvonimir

    Physical review letters

    2021  Volume 127, Issue 14, Page(s) 148001

    Abstract: In microtubule-based active nematics, motor-driven extensile motion of microtubule bundles powers chaotic large-scale dynamics. We quantify the interfilament sliding motion both in isolated bundles and in a dense active nematic. The extension speed of an ...

    Abstract In microtubule-based active nematics, motor-driven extensile motion of microtubule bundles powers chaotic large-scale dynamics. We quantify the interfilament sliding motion both in isolated bundles and in a dense active nematic. The extension speed of an isolated microtubule pair is comparable to the molecular motor stepping speed. In contrast, the net extension in dense 2D active nematics is significantly slower; the interfilament sliding speeds are widely distributed about the average and the filaments exhibit both contractile and extensile relative motion. These measurements highlight the challenge of connecting the extension rate of isolated bundles to the multimotor and multifilament interactions present in a dense 2D active nematic. They also provide quantitative data that is essential for building multiscale models.
    Language English
    Publishing date 2021-10-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 208853-8
    ISSN 1079-7114 ; 0031-9007
    ISSN (online) 1079-7114
    ISSN 0031-9007
    DOI 10.1103/PhysRevLett.127.148001
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    In stock of ZB MED Bonn / Germany

    Z 4' 58/153: Show issues

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  4. Article ; Online: Statistical properties of autonomous flows in 2D active nematics.

    Lemma, Linnea M / DeCamp, Stephen J / You, Zhihong / Giomi, Luca / Dogic, Zvonimir

    Soft matter

    2019  Volume 15, Issue 15, Page(s) 3264–3272

    Abstract: We study the dynamics of a tunable 2D active nematic liquid crystal composed of microtubules and kinesin motors confined to an oil-water interface. Kinesin motors continuously inject mechanical energy into the system through ATP hydrolysis, powering the ... ...

    Abstract We study the dynamics of a tunable 2D active nematic liquid crystal composed of microtubules and kinesin motors confined to an oil-water interface. Kinesin motors continuously inject mechanical energy into the system through ATP hydrolysis, powering the relative microscopic sliding of adjacent microtubules, which in turn generates macroscale autonomous flows and chaotic dynamics. We use particle image velocimetry to quantify two-dimensional flows of active nematics and extract their statistical properties. In agreement with the hydrodynamic theory, we find that the vortex areas comprising the chaotic flows are exponentially distributed, which allows us to extract the characteristic system length scale. We probe the dependence of this length scale on the ATP concentration, which is the experimental knob that tunes the magnitude of the active stress. Our data suggest a possible mapping between the ATP concentration and the active stress that is based on the Michaelis-Menten kinetics that governs the motion of individual kinesin motors.
    Language English
    Publishing date 2019-03-25
    Publishing country England
    Document type Journal Article
    ZDB-ID 2191476-X
    ISSN 1744-6848 ; 1744-683X
    ISSN (online) 1744-6848
    ISSN 1744-683X
    DOI 10.1039/c8sm01877d
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  5. Article ; Online: Machine learning active-nematic hydrodynamics.

    Colen, Jonathan / Han, Ming / Zhang, Rui / Redford, Steven A / Lemma, Linnea M / Morgan, Link / Ruijgrok, Paul V / Adkins, Raymond / Bryant, Zev / Dogic, Zvonimir / Gardel, Margaret L / de Pablo, Juan J / Vitelli, Vincenzo

    Proceedings of the National Academy of Sciences of the United States of America

    2021  Volume 118, Issue 10

    Abstract: Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in ... ...

    Abstract Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields that encode the distribution of energy-injecting microscopic components. Here, we use active nematics to demonstrate that neural networks can map out the spatiotemporal variation of multiple hydrodynamic parameters and forecast the chaotic dynamics of these systems. We analyze biofilament/molecular-motor experiments with microtubule/kinesin and actin/myosin complexes as computer vision problems. Our algorithms can determine how activity and elastic moduli change as a function of space and time, as well as adenosine triphosphate (ATP) or motor concentration. The only input needed is the orientation of the biofilaments and not the coupled velocity field which is harder to access in experiments. We can also forecast the evolution of these chaotic many-body systems solely from image sequences of their past using a combination of autoencoders and recurrent neural networks with residual architecture. In realistic experimental setups for which the initial conditions are not perfectly known, our physics-inspired machine-learning algorithms can surpass deterministic simulations. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems, even in the absence of knowledge of the underlying dynamics.
    MeSH term(s) Hydrodynamics ; Machine Learning
    Language English
    Publishing date 2021-03-02
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2016708118
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1148: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (1.OG)
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  6. Book ; Online: Statistical Properties of Autonomous Flows in 2D Active Nematics

    Lemma, Linnea M / Decamp, Stephen J / You, Zhihong / Giomi, Luca / Dogic, Zvonimir

    2018  

    Abstract: We study the dynamics of a tunable 2D active nematic liquid crystal composed of microtubules and kinesin motors confined to an oil-water interface. Kinesin motors continuously inject mechanical energy into the system through ATP hydrolysis, powering the ... ...

    Abstract We study the dynamics of a tunable 2D active nematic liquid crystal composed of microtubules and kinesin motors confined to an oil-water interface. Kinesin motors continuously inject mechanical energy into the system through ATP hydrolysis, powering the relative microscopic sliding of adjacent microtubules, which in turn generates macroscale autonomous flows and chaotic dynamics. We use particle image velocimetry to quantify two-dimensional flows of active nematics and extract their statistical properties. In agreement with the hydrodynamic theory, we find that the vortex areas comprising the chaotic flows are exponentially distributed, which allows us to extract the characteristic system length scale. We probe the dependence of this length scale on the ATP concentration, which is the experimental knob that tunes the magnitude of the active stress. Our data suggest a possible mapping between the ATP concentration and the active stress that is based on the Michaelis-Menten kinetics that governs motion of individual kinesin motors.

    Comment: 17 pages, 7 figures
    Keywords Condensed Matter - Soft Condensed Matter ; Physics - Biological Physics
    Subject code 612
    Publishing date 2018-09-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Machine learning active-nematic hydrodynamics

    Colen, Jonathan / Han, Ming / Zhang, Rui / Redford, Steven A. / Lemma, Linnea M. / Morgan, Link / Ruijgrok, Paul V. / Adkins, Raymond / Bryant, Zev / Dogic, Zvonimir / Gardel, Margaret L. / De Pablo, Juan J. / Vitelli, Vincenzo

    2020  

    Abstract: Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such hydrodynamic parameters are difficult to derive from microscopics. Seldom is this challenge more apparent than in ... ...

    Abstract Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such hydrodynamic parameters are difficult to derive from microscopics. Seldom is this challenge more apparent than in active matter where the energy cascade mechanisms responsible for autonomous large-scale dynamics are poorly understood. Here, we use active nematics to demonstrate that neural networks can extract the spatio-temporal variation of hydrodynamic parameters directly from experiments. Our algorithms analyze microtubule-kinesin and actin-myosin experiments as computer vision problems. Unlike existing methods, neural networks can determine how multiple parameters such as activity and elastic constants vary with ATP and motor concentration. In addition, we can forecast the evolution of these chaotic many-body systems solely from image-sequences of their past by combining autoencoder and recurrent networks with residual architecture. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems even when no knowledge of the underlying dynamics exists.

    Comment: SI Movie 1: https://www.youtube.com/watch?v=9WzIT7OG9pY SI Movie 2: https://youtu.be/Trc4RyU7-dw SI Movie 3: https://youtu.be/Epm_P_EakH8
    Keywords Condensed Matter - Soft Condensed Matter ; Condensed Matter - Materials Science ; Nonlinear Sciences - Chaotic Dynamics ; Physics - Fluid Dynamics
    Subject code 612
    Publishing date 2020-06-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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