Active Matter Workshop 2023

Active Matter Workshop 2023

VENUE:
CMMA, Meiji Universityand via Zoom webinar

DATE:
27 - 28 January, 2023

1

Active Matter Workshop 2023

Organiser
共同利用・共同研究拠点

明治大学

先端数理科学インスティテュート（MIMS）現象数理学拠点
MEXT Joint Usage/Research Center Meiji University"Center for Mathematical Modeling and Applications" (CMMA)

Organisation committee
江端宏之（九州大学） Hiroyuki Ebata (Kyushu Univ.)

北畑裕之（千葉大学） Hiroyuki Kitahata (Chiba Univ.)

末松 J. 信彦（明治大学） Nobuhiko J. Suematsu (Meiji Univ.)
多羅間充輔（九州大学） Mitsusuke Tarama (Kyushu Univ.)

Web pagehttps://sites.google.com/view/activematter2023

Contact
email: active2023@googlegroups.com

2

Active Matter Workshop 2023

Program2023/1/27 Fri.
9:30 - 10:00

Registration

10:00 - 10:05

opening

Chair: Schnyder, Simon (University of Tokyo)
10:05 - 10:55[IL] Murakami, Hisashi (Kyoto Institute of Technology)“Mutual anticipation can facilitates self-organization in animalgroups”
10:55 - 11:20
Kaneko, Kojiro (Kyushu University)“Self-propelled rods with curved boundaries”
11:20 - 11:45
Yasuda, Kento (Kyoto University)“Most probable path of an active Brownian particle”
11:45 - 13:15
Lunch Break
Chair: Tarama, Sonja (Ritsumeikan University)
13:15 - 14:05[IL] Watanabe, Chiho (Hiroshima University)“Active droplets design and their behavior”
14:05 - 14:30
Adachi, Riku (University of Tokyo)“Life-like behavior in Autonomous Movements of Oil-droplets
and Tetrahymena”
14:30 - 15:00
Break
Chair: Yasuda, Kento (Kyoto University)
15:00 - 15:25
Feng, Chao (Kyoto University)“Direct numerical simulations of a model microswimmer near aliquid-liquid interface”
15:25 - 15:50
Tarama, Sonja (Ritsumeikan University)“Self-organization in crawling cells through mechanic feedback
interaction”
15:50 - 16:15
Schnyder, Simon (University of Tokyo)“Competition between cell types under cell cycle regulation with
apoptosis”
16:15 - 16:30
Break
Chair: Ryoichi, Yamamoto (Kyoto University)
16:30 - 16:55
Tarama, Mitsusuke (Kyushu University)“Dynamic network structure formation of mesoderm cells in earlychick embryo”
16:55 - 17:20
Ebata, Hiroyuki (Kyushu University)“Metabolism-dependent and cytoskeleton-independent rheologyof cell cytoplasm”
17:20 - 17:45
Discussion
3

Active Matter Workshop 2023

2023/1/28 Sat.
10:00 - 10:05

opening of Day 2

Chair: Tanaka, Shinpei (Hiroshima University)
10:05 - 10:55[IL] Sumino, Yutaka (Tokyo University of Science)“Dynamics and control of the injection front induced by
precipitation formation”
10:55 - 11:20
Kuroda, Yuta (Nagoya University)“Hyperuniformity and Singular Density Correlation in Chiral
Active Fluids”
11:20 - 11:45
Kato, Airi (Wenzhou Institute, UCAS)“Rough colloids at interfaces: With scopes of interfacial active
matter”
11:45 - 13:15
Lunch Break
Chair: Kato, Airi (Wenzhou Institute, University of Chinese Academy of Sciences)
13:15 - 13:40
Yamagishi, Minami (University of Tokyo)“Dynamics of a quantum active particle based on 2D nonHermitian quantum walks”
13:40 - 14:05
Kawamura, Ayase (Hiroshima University)“Self-propulsion of 3-Phenylpropionaldehyde Droplet in
Homogeneous and Heterogeneous Aqueous Solutions”
14:05 - 14:30
Tanaka, Shinpei (Hiroshima University)“Collective motion of active droplets and how to grasp it”
14:30 - 15:00
General discussion and closing===========
Presentation time
Invited lectures [IL]: 50 min each, including discussions
Contributed presentation: 25 min each including discussions

4

Active Matter Workshop 2023

Abstracts5Active Matter Workshop 2023

Day 1
27 Jan., 2023
9:30 - 10:00
Registration
10:00 - 10:05
opening
Chair: Schnyder, Simon (University of Tokyo)
10:05 - 10:55[IL] Murakami, Hisashi (Kyoto Institute of Technology)“Mutual anticipation can facilitates self-organization in animalgroups”
10:55 - 11:20
Kaneko, Kojiro (Kyushu University)“Self-propelled rods with curved boundaries”
11:20 - 11:45
Yasuda, Kento (Kyoto University)“Most probable path of an active Brownian particle”
11:45 - 13:15
Lunch Break
Chair: Tarama, Sonja (Ritsumeikan University)
13:15 - 14:05[IL] Watanabe, Chiho (Hiroshima University)“Active droplets design and their behavior”
14:05 - 14:30#Adachi, Riku (University of Tokyo)“Life-like behavior in Autonomous Movements of Oil-droplets
and Tetrahymena”
14:30 - 15:00
Break
Chair: Yasuda, Kento (Kyoto University)
15:00 - 15:25
Feng, Chao (Kyoto University)“Direct numerical simulations of a model microswimmer near aliquid-liquid interface”
15:25 - 15:50
Tarama, Sonja (Ritsumeikan University)“Self-organization in crawling cells through mechanic feedback
interaction”
15:50 - 16:15
Schnyder, Simon (University of Tokyo)“Competition between cell types under cell cycle regulation with
apoptosis”
16:15 - 16:30
Break
Chair: Ryoichi, Yamamoto (Kyoto University)
16:30 - 16:55
Tarama, Mitsusuke (Kyushu University)“Dynamic network structure formation of mesoderm cells in earlychick embryo”
16:55 - 17:20
Ebata, Hiroyuki (Kyushu University)“Metabolism-dependent and cytoskeleton-independent rheologyof cell cytoplasm”
17:20 - 17:45
Discussion
6

Invited Lecture

Active Matter Workshop 2023

10:05 - 10:55

Mutual anticipation can facilitates self-organization in animalgroupsHisashi Murakami1
1

Kyoto Institute of Technology

Collective animal groups often seem to behave as if having one body and/or one mind as a whole.
Without external controls, a globally ordered state emerges from inter-individuals interactions.
Human crowds also show various collective pattern formations similar to other animal groups, suchas bird flocks or fish schools. Anticipatory behavior has been considered to play a key role in a wide
range of self-organizing living systems. In particular, it is evident that pedestrians anticipate the future
positions of their neighbors to seek their paths in dynamical pedestrian flow, unlike distancedependent repulsive interactions in classical models of human crowds resembling physical particle
systems. This anticipatory path-finding behavior results in the deviation of pedestrians from the direct
path to their destination. However, the deviation of individual movements in human crowds is poorly
understood, even though the path-searching activities of pedestrians seem to influence collectivelevel behavior directly. Do individuals in human crowds follow a specific search strategy? If so, how
is such a strategy influenced by the ability to anticipate of motions of neighbors? Moreover, does thisindividual anticipatory behavior offer functional benefits to the group? We here show that thedeviation derived from the anticipation of motions of neighbors can be statistically described, andhow it impacts the global crowd dynamics, via two experiments with fifty-four participant
pedestrians. First, we conducted an experiment on lane formation, a striking example of collective
patterning in human crowds, where pedestrians waiting at each side of the experimental corridor
begin walking towards the opposite side and then spontaneously separate into several lanes withuniform walking directions. We found that during lane formation, the fluctuation of pedestrians fromtheir desired directions (i.e., the direct path to their destination) follows a scale-free movement
strategy called a Levy walk process. A Levy walk is a special class of random walks in which manysmall steps are interspersed with occasional long steps and is theoretically optimal when searching
unpredictably distributed resources. This result suggests that lane formation efficiently emerges
through an optimal path-seeking strategy by anticipation. To check this suggestion, we conducted thesecond experiment which was similar to the first experiment; however, we asked some participants
to perform walking while texting on the smartphone. Hence, these pedestrians were distracted to
anticipate the motions of neighbors. We found that the walking strategy of distracted pedestrians
differs from non-distracted ones, influences on motions of other pedestrians as well, and decreases
the degree of collective pattern formation (lane formation). Moreover, both the distracted pedestrians
and the non-distracted ones had difficulties navigating. These results imply mutual anticipation
facilitates the efficient transition to emergent pattern formation. Interestingly, also in other animalgroups, anticipatory behavior is known as crucial to perform robust collective behavior: individual
noisy movements derived from anticipating or negotiating neighbors, which seem to collapse theglobal order, can facilitate interactions with various neighbors and contribute to dynamic collective
behavior. Our findings suggest that mutual anticipation is an underlying mechanism across a wide
range of animal groups and is a key factor to develop the collective mind analogy.
References
1. H Murakami, C Feliciani, Y Nishiyama, K Nishinari, "Mutual anticipation can contribute to selforganization in human crowds", Science Advances 7, eabe7758 (2021)
2. H Murakami, C Feliciani, K Nishinari “Lévy walk process in self-organization of pedestrian crowds”,
Journal of the Royal Society Interface 16 (153), 20180939 (2019)
3. H Murakami, T Niizato, T Tomaru, Y Nishiyama, YP Gunji “Inherent noise appears as a Lévy walk infish schools”, Scientific Reports 5, 10605 (2015)

7

Contributed Talk

Active Matter Workshop 2023

10:55 - 11:20

Self-propelled rods with curved boundaries
Kojiro Kaneko, Jun-ichi Fukuda

Department of Physics, Faculty of Science, Kyushu Univ.
Dense bacterial suspensions show distinctive vortical motions with an intrinsic length scale. Suchemergent dynamics is now called active turbulence and attracts broad attention. When the turbulent
dynamics of such suspensions is restricted within some confinement or by some obstacles, bacteria show
different behaviors near the boundaries. If these geometrical constraints possess a length scale comparable tothe one of turbulent-like motion, they rectify the collective motion of bacteria and yield stably ordered
vortices [1,2].
This research aims to uncover the underlying mechanism of this ordered behavior and to understand theeffect of complex geometrical constraints on the dynamics of bacteria. Using an agent-based model in anenvironment with boundaries, we perform coarse-grained molecular dynamics simulations for suchsituations. We model bacteria as self-propelled rods with excluded volume so that they have anisotropic
steric interaction with each other and with boundaries. We implement both internal and external geometricalboundaries to investigate the dynamics of bacteria within a confinement or with obstacles.
In this talk, we will discuss the collective behavior of self-propelled rods when we vary (1) the properties
of interactions, (2) the shape of rods, and (3) the geometrical conditions.

Fig.!" A snapshot of simulation
in a doublet well.

Fig.#" A snapshot of simulation
with a circular obstacle.

References
1. Beppu, K., Izri, Z., Gohya, J. et al.: “Geometry-driven collective ordering of bacterial vortices”, Soft
Matter 13, 5038 (2017).
2. Nishiguchi, D., Aranson, I.S., Snezhko, A. et al.: “Engineering bacterial vortex lattice via direct laser
lithography”. Nat. Commun. 9, 4486 (2018).

8

Contributed Talk

Active Matter Workshop 2023

11:20 - 11:45

Most probable path of an active Brownian particle
Kento Yasuda, Kenta Ishimoto

Research Institute for Mathematical Sciences, Kyoto University

In this study, we investigate the transition path of a free active Brownian particle (ABP) on a twodimensional plane between two given states. The extremum conditions for the most probable pathconnecting the two states are derived using the Onsager-Machlup integral and its variational
principle. We provide explicit solutions to these extremum conditions and demonstrate their
nonuniqueness through an analogy with the pendulum equation indicating possible multiple paths.
The pendulum analogy is also employed to characterize the shape of the globally most probable
path obtained by explicitly calculating the path probability for multiple solutions. We
comprehensively examine a translation process of an ABP to the front as a prototypical example.
Interestingly, the numerical and theoretical analyses reveal that the shape of the most probable pathchanges from an I to a U shape and to the ℓ shape with an increase in the transition process time.
The Langevin simulation also confirms this shape transition. We also discuss further method
applications for evaluating a transition path in rare events in active matter.

Fig.１： Examples of the most probable path of an active Brownian particle.

References
1. K. Yasuda and K. Ishimoto, Phys. Rev. E 106, 064120 (2022).

9

Invited Lecture

Active Matter Workshop 2023

13:15 - 14:05

Active droplets design and their behaviors
Chiho Watanabe1, 3, Shinpei Tanaka2, 3
1

Graduate School of Integrated Sciences for Life,
Graduate School of Advanced Science and Engineering,3
School of Integrated Arts and Sciences, Hiroshima University, Hiroshima, Japan
2

Self-propelled active objects or droplets floating on the water surface, including a camphor boat andethyl salicylate droplets, have been studied and attracted attention [1,2]. Such active matters are often called
Marangoni surfers and their motion is driven by inhomogeneous surface tension around the object of interest.
Therefore, how to impose the inhomogeneous surface tension around the object is the key to the design. In
this study, we investigated droplets’ motion floating on the water where two types of droplets, a surfactant
source, and a surfactant sink, created inhomogeneous surface tension around them. Specifically, we used adroplet of 1-decanol which has weak surface activity as a surfactant source, and fluid paraffin as a sink
droplet that dissolves 1-decanol. This two-droplet source-sink system can make a minimal “droplet
chemotactic” system that can show characteristic co-responsive behavior. A dye, Sudan black B (SB), wasadded to the 1-decanol droplet for visualization, though we found that the active droplets’ trajectory patterns
depended on the concentration of SB. At low SB concentration (0.005 wt%), the droplets show binary-starlike motion while at high SB concentration (0.20 wt%), the 1-decanol droplet chases the paraffin droplet. On
the other hand, at an intermediate SB concentration (0.05 wt%), the 1-decanol droplet pokes at the paraffin
droplet periodically, indicating an oscillatory motion pattern (Figure 1a) [3]. In this talk, we will introduce
the behavior of this two-droplet source-sink system as well as other experimental results of different initialconfigurations of source-sink droplets (Figure 1b).
(a)

(b)

1-decanol
sourceSource 1, Sink 1

Paraffinsink

Source 1, Sink 2

U

water

binary starLowoscillation
SB Concentration

Source 2, Sink 1

chasingHigh

Fig.１： (a) An illustration of our experimental set up and the droplets’ motion with different dye concentration[3], (b) Examples of source-sink active droplets system.

Acknowledgments
This work was done in collaboration with Dr. Löffler, R.J.G., Prof. Hanczyc, M.M., and Prof. Gorecki, J.
References
1. Fujita, R.; Matsuo, M.; Nakata, S. “Multidimensional Self-Propelled Motion Based on Nonlinear Science”
Front. Phys. 10, 854892 (2022).
2. Tanaka, S.; Nakata, S.; Nagayama, M. “A Surfactant Reaction Model for the Reciprocating Motion of a
Self-Propelled Droplet” Soft Matter 17 (2), 388–396 (2021).
3. Watanabe, C.; Tanaka, S.; Löffler, R.J.G.; Hanczyc, M.M.; Gorecki, J., “Dynamic ordering caused by asource-sink relation between two droplets”, Soft Matter 18, 6465 (2022)

10

Contributed Talk

Active Matter Workshop 2023

14:05 - 14:30

Life-like behavior in Autonomous Movements of Oil-droplets andTetrahymena
Riku Adachi1, Hiroki Kojima1, Takashi Ikegami1
1

Department of General Systems Studies, Graduate School of Arts and Sciences, The University of Tokyo

In considering the transition from non-living to living organisms, it is essential to understand themechanisms of autonomous movement observed in common between the two. Therefore, in order to discuss
the requirements for being "life itself" , it is imperative to compare the motions of the two systems andexamine their similarities and differences. To the best of our knowledge, studying "artificial life systems"
have been conducted independently in the context of active matter research. Therefore, we scrutinize thespontaneous motion of oil droplets as an example of non-living matter and the spontaneous motion of
Tetrahymena as a living organism, and provide an integrated theoretical description of what constitutes lifelike behavior.
The experiment setup of the oil droplet consisted of a mixture of ethyl
salicylate (ES) and paraffin in a petri dish with an anionic surfactant, Sodium
Dodecyl Sulfate (SDS) solution1. A droplet whose parameters such as volume
and composition ratio (ES: Paraffin) were changed accordingly, and its emerging
motion was recorded (Fig.1). While Tetrahymena, a eukaryotic unicellularorganism, was confined in a two-dimensional microculture apparatus andobserved under a stereomicroscope with sufficient nutrition. Henceforth, thedifferences in motion were quantitatively evaluated by analyzing the trajectorydata obtained from the recordings. As a result, an oil droplet showedcharacteristic motion patterns (e.g., circular, reciprocal, straight, and chaotic)
Fig1. Experimental setup
depending on its volume and composition ratio (Fig.2). In comparison,
Tetrahymena was observed moving randomly in the culture medium. According to the energy distribution
calculated from the time series of motion, the motion of the oil droplets seems to oscillate around a constantkinetic energy, but Tetrahymena exhibits a characteristic kinetic energy distribution that deviates from theexponential distribution. As the volume of the oil droplet was increased or the ratio of ES was decreased, thedistribution function of kinetic energy was found to be closer to that of Tetrahymena.
! Moreover, we estimated the Lyapunov exponent as a nonlinear analysis
and distinguished whether the governing dynamics behind the movement
follow a stochastic process or a nonlinear dynamical process. Those
results imply that the origin of fluctuations in the motion of non-living(oil droplets) and living (Tetrahymena) systems were different. Oil
droplets, which are susceptive to the external surface tension field
generated by chemical gradient, exhibited partially chaotic motion; still,
the influence of noise (or, say, high dimensional chaos) was dominant for
Tetrahymena. This may be because of the increments of the contributionof the degree of freedom for the deformation of the droplet itself as theFig2. Phase diagram
volume of an oil droplet increases or the ratio of ES decreases. On the other
hand, does Tetrahymena own "autonomy" that intrinsically determines lifeand non-life system in the form of noise-like complex behavior? These issues will be discussed on the day.
Acknowledgements
This work was financially supported by the World-leading Innovative Study for Frontiers of Mathematical
Sciences and Physics (WINGS-FMSP), Graduate School of Mathematical Sciences, The University ofTokyo.
References
1. Tanaka, Shinpei, Yoshimi Sogabe, and Satoshi Nakata. "Spontaneous change in trajectory patterns of aself-propelled oil droplet at the air-surfactant solution interface." Physical Review E 91.3 (2015): 032406.

11

Contributed Talk

Active Matter Workshop 2023

15:00 - 15:25

Direct numerical simulations of a model microswimmer
near a liquid-liquid interface
Chao Feng1, John J. Molina1, Matthew S. Turner2, and Ryoichi Yamamoto1
1

Department of Chemical Engineering, Kyoto University, Kyoto 615-8510, Japan2

Department of Physics, University of Warwick, Coventry CV4 7AL, UK

Transport of material across liquid interfaces is ubiquitous for living cells and is also a crucial step in drugdelivery and in many industrial processes. Here, we present numerical simulations of the hydrodynamic flows
associated with a single microswimmer embedded in a binary fluid mixture. The two fluids demix, separated
by a penetrable and deformable interface that we assume to be initially prepared in its planar ground state. We
find that the microswimmer can either penetrate the interface, move parallel to it, or bounce back off it. We
analyze how the trajectory depends on the swimmer type (pusher/puller) and the angle of incidence withrespect to the interface. Our simulations are performed in a system with periodic boundary conditions,corresponding to an infinite array of fluid interfaces. A puller reaches a steady state in which it either swims
parallel to the interface or selects a perpendicular orientation, repeatedly penetrating through the interface. In
contrast, a pusher follows a bouncing trajectory between two interfaces. We discuss several examples inbiology in which swimmers penetrate soft interfaces. Our paper can be seen as a highly simplified model ofsuch processes.
We also investigated the effect of the fluid viscosity ratio on the movement patterns of microswimmers.
We find that swimmers systematically reorientate towards the region containing the lower viscosity fluid.
Ultimately this is expected to drive the swimmers to behave as if they are more inclined to swim in low
viscosity fluids. Furthermore, in addition to the types of swimming already reported in the isoviscous system,
i.e. bouncing, sliding and penetrating, we observed a hovering motion, in which strong pullers swim parallelto the interface with a certain distance, which is consistent with the dynamics of such swimmers near the solid
wall.

12

Contributed Talk

Active Matter Workshop 2023

15:25 - 15:50

Self-organization in crawling cells
through mechanic feedback interaction
S. Tarama1, M. Tarama2
1

College of Life Sciences, Department of Bioinformatics, Ritsumeikan University
2
Department of Physics, Kyushu University

Communication between cells is essential for correct tissue formation and morphogenesis. Here, wetheoretically investigate the ability of actively crawling cells to coordinate their motion throughmechano-sensing. The basic model considers the cell crawling motion as a cycle of cell elongationand contraction, accompanied by a modification of the friction with the underlying substrate
through adhesion to and de-adhesion from it. Due to different surface frictions in the adhered andde-adhered states, the cell is able to effectively push itself forward during its motile cycle. A
feedback is then introduced between the experienced force resulting from neighbouring cells
pushing against the cell, and its internal dynamics. As a result, the speed at which the motile cycle
advances as well as the magnitude of the cell elongation during the cycle changes depending on theexperienced force. For positive feedback, cells tend to follow the external force, resulting in asmaller elongation length and higher (lower) progression rates in the shrinking (elongating) stage ofthe cycle. Contrarily, for negative feedback, cells try to counteract the external force, resulting in alonger elongation length and higher (lower) progression rates in the elongating (shrinking) stage ofthe cycle. The resulting multi-cell dynamics for the one-dimensional case reveals a multitude of
dynamical modes, including synchronization and travelling wave formation, depending on the
feedback strength. These results are a first step to understanding how self-organization of cells may
occur in biological systems based on mechanical sensing, with the larger goal of understanding cell
dynamics up to the tissue scale, including morphogenesis and organ formation.

!

!

References
1. M. Tarama, R. Yamamoto: Mechanics of Cell Crawling by Means of Force-free Cyclic Motion, JPSJ 87,
044803 (2018)13

Contributed Talk

15:50 - 16:15

Active Matter Workshop 2023

Competition between cell types under cell cycle regulation with
apoptosis
Jintao Li1, Simon K. Schnyder1,2, Matthew S. Turner3,1, Ryoichi Yamamoto1

1

Department of Chemical Engineering, Kyoto University, Kyoto 615-8510, Japan
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku,
Tokyo 153-8505, Japan3
Department of Physics, University of Warwick, Coventry CV4 7AL, United Kingdom
2

Mechanics-based models for cellular tissues often assume that cell growth and division directly dependon physical variables such as pressure. However, biologists understand these processes as fundamentally
being controlled by the cell cycle. While both approaches have had successes, they have so far not been
properly unified. In a first step, we developed a hybrid model for tissue growth that combines a regulatory
cell cycle with a physical model [1]. We study this model using particle-based simulations and within acontinuum analysis in order to investigate the expansion of non-motile colonies in quasi-1D channels and on
2D substrates. The model reproduces a range of features that we relate to experimental observations. We also
study the competition of two colonies with different rates of programmed cell death (apoptosis) andcharacteristic cell-cycle control pressures [2]. Synchronisation of cell division/apoptosis events can emerge,causing oscillations in cell number, pressure and cell-cycle activity over timescales corresponding to several
division times.
(a)3τ div(b) 75020τ div
NA(e) 300
100

N

a2A

500

30
NB

ωA

0 .0
(g)

0 .4

ωA

101

102

(0)

kB
0.01

0.1

1

0 .2

10−2 10−1

100
(0)
kA

0 .0
1.
0
1. 0
05

40

58

!p/prB "20Time t/τ div

100

0 .2

prA /prB

1 .0

10−1

1.

!r "

!r B "

0 .4

τ r, τ v

43!r A "

0 .3
0 .2
0 .1
0 .0
(d) 1.5

10−21.

(c) 0.4

10−3(f) 0.6

29

0

1.

250

0 .5
0

50τ div

17

t=0Type B

1.

Type AprA /prB

Fig. 1: (a) Simulation snapshots showing the invasion of A cells (red) at the expense of B (blue). Evolution of (b)cell number N, (c) cell cycle activity r and (d) the cell-type-averaged pressure p. Note the emergence of synchronisation in birth/death, manifested in oscillations in these mechanochemical variables. (e) peak value of thesquared mode amplitude in the power spectrum as a function of the cell cycle times τ! = τ" . (f,g) Shows thecorresponding peak (power) frequency.

References
1. Li, J., Schnyder, S. K., Turner, M. S. & Yamamoto, R.: “Role of the Cell Cycle in Collective Cell
Dynamics”, Phys. Rev. X 11, 031025 (2021).
2. Li, J., Schnyder, S. K., Turner, M. S. & Yamamoto, R.: “Competition between cell types under cell cycle
regulation with apoptosis”, Phys. Rev. R 4, 033156 (2022).

14

Contributed Talk

Active Matter Workshop 2023

16:30 - 16:55

Dynamic network structure formation of mesoderm cells
in early chick embryo
Mitsusuke Tarama
Department of Physics, Kyushu University
Self-organisation of various cellular structures is of fundamental importance in morphogenesis during
development of biological organisms. In this talk, I introduce the network structure of mesoderm cells thatwas found in the early chick embryo. During the development of the chick embryo, the mesoderm cells thatappear through invagination at the primitive streak migrate collectively in the lateral direction between theepiblast (outer layer) and the endoderm (inner layer). While moving collectively, the mesoderm cells selforganise into a network structure, where spaces void of cells filled with extracellular liquid are surroundedby cells that are connected to each other. The network of mesoderm cells undergoes reconnection
dynamically.
I developed a theoretical model of self-propelling cells of elongated shape to understand the origin of the
dynamic network structure formation. By introducing a short-range attractive interaction with a repulsive
core between cells, the model cells reproduced the network structure for intermediate strengths of theattractive interaction. The results were compared with the experiment quantitatively by using persistenthomology. In addition, we studied the effect of the elongated shape and the self-propulsion on the structureformation. Moreover, we also investigated how the self-organised structure changes as the development
proceeds.

References
1. Y. Nakaya, M. Tarama, et al., “Migrating mesoderm cells self-organize into a dynamic meshwork
structure during chick gastrulation”, bioRxiv 2022.09.08.507227 (2022).

15

Contributed Talk

Active Matter Workshop 2023

16:55 - 17:20

Metabolism-dependent and cytoskeleton-independentrheology of cell cytoplasm
Hiroyuki Ebata1, Daisuke Mizuno1
1

Department of physics, faculty of science, Kyushu University

Intracellular space among cell cytoskeleton is crowded with bio-macromolecules, which can lead toglassy or jamming behavior. When metabolic activity was suppressed, the bacterial cytoplasm showed theglassy dynamics, such as nonergodicity, caging, and dynamic heterogeneity [1]. Viscosity of cell extracts
increased with super-exponential function and practically diverged as the concentration of solid contents
approached physiological value [2]. On the other hand, cytoplasm of living cell maintained fluidity withsimilar concentrations [2]. These results suggest that the cell cytoplasm is fluidized by the forces generated
by motor-proteins and mechano-enzymes. Previous studies have measured the rheology of cell cytoplasm
that contained not only interstitial fluid with dense bio-macromolecules but also polymer networks, that is
cytoskeleton. Therefore, in order to elucidate the mechanism of fluidization of the glassy cytoplasm, it isnecessary to clarify the rheological properties of the cytoplasm in cells with impaired cytoskeletal
development.
We conducted active microrheology in HeLa cells to measure the complex shear viscoelastic modulus!(#) = ! ! (#) − '!"(#) . To figure out the effect of the cytoskeletal development, we used cells on soft
gels, cells with cytoskeletal inhibitors, and genetically deficient cells. As a result, the complex shear
viscoelastic modulus of spherical cells on soft gels, cells with inhibited actin skeleton, microtubule inhibited
cells, and vimentin deficient cells showed no significant difference with that of untreated HeLa cells.! In allconditions, same frequency dependence of the storage and loss moduli !(#) ∝ (−'#)".$ was obtained (Fig.
1), which is characteristic for dense colloidal systems near the jamming transition point. On the other hand,when ATP was depleted by the drug and the metabolic activity was severely suppressed, storage modulus
significantly increased at low frequency, which caused a constant plateau as !(#) = !" + ,(−'#)".$ . Thus,
the linear viscoelasticity of the cytoplasm of HeLa cells is almost independent of the cytoskeletal
development, while it is strongly influenced by metabolic activity.

Figure 1：Frequency dependence of storage modulus G’ and loss modulus G” for the untreated cell,
cells with different cytoskeletal structures, and cells with low metabolic activity. Untreated (bluecircle): untreated HeLa cells. VMKO (green triangle): vimentin deficient HeLacells. Gel (red squre):cells on soft gels. Noco (purple diamond): microtubule inhibited cells. ATP dep (black circle): ATPdepleted cells.
References
1. B. R. Parry, et al., “The bacterial cytoplasm has glass-like properties and is fluidized by metabolic
activity”, Cell 156, 183-94 (2014).
2. K. Nishizawa et al., “Universal glass-forming behavior of in vitro and living cytoplasm”, Sci. Rep. 7, 1
(2017).

16

Active Matter Workshop 2023

Day 2
28 Jan., 2023
10:00 - 10:05
opening of Day 2
Chair: Tanaka, Shinpei (Hiroshima University)
10:05 - 10:55[IL] Sumino, Yutaka (Tokyo University of Science)“Dynamics and control of the injection front induced by
precipitation formation”
10:55 - 11:20
Kuroda, Yuta (Nagoya University)“Hyperuniformity and Singular Density Correlation in Chiral
Active Fluids”
11:20 - 11:45
Kato, Airi (Wenzhou Institute, UCAS)“Rough colloids at interfaces: With scopes of interfacial active
matter”
11:45 - 13:15
Lunch Break
Chair: Kato, Airi (Wenzhou Institute, University of Chinese Academy of Sciences)
13:15 - 13:40
Yamagishi, Minami (University of Tokyo)“Dynamics of a quantum active particle based on 2D nonHermitian quantum walks”
13:40 - 14:05
Kawamura, Ayase (Hiroshima University)“Self-propulsion of 3-Phenylpropionaldehyde Droplet in
Homogeneous and Heterogeneous Aqueous Solutions”
14:05 - 14:30
Tanaka, Shinpei (Hiroshima University)“Collective motion of active droplets and how to grasp it”
14:30 - 15:00
General discussion and closing

17

Invited Lecture

Active Matter Workshop 2023

10:05 - 10:55

Dynamics and control of the injection front induced by
precipitation formation
Yutaka Sumino, Shunsuke Tanaka, Kojiro Otoguro
1

Department of Applied Physics, Tokyo University of Science

Many engineering processes, such as chemical grouting and enhanced oil recovery, involve an injectionof fluid into soil that has a confined geometry. Similar dynamics of fluid in soil play relevant roles in thegeological process, including earthquakes. Varieties of study on such dynamics of the fluid is conductedusing a simple experimental model. A famous example is viscous fingering produced through SaffmanTaylor instability. Special attention should be paid to precipitation formation accompanied by the above
engineering and geological processes. The generated precipitation hinders fluid motion and creates acomplex pattern. The observation of the dynamics of the injection front in situ, as well as its control, would
be relevant to physical problems.
For this reason, we built a binary fluid system that produces a precipitate confined in a thin cell. In both
our previous study with water glass and cobalt chrolide1 and CTAB and NaSal2, the precipitating systemshows circular and filament patterns. Figure 1 shows the typical injection dynamics. Here, the top view oftransparent parallel cells is shown. The cell was filled with aqueous solution of CTAB whose concentration
was kept at 50 mM. The injected solution was an aqueous solution of NaSal whose concentration c wasvaried. We also varied the injection rate of the inner fluid J. Special attention should be given for the absenceof Saffman-Taylor instability due to small viscosity contrast (inner fluid (! = 50 mM, 1.03 m Pa s, Outer
fluid: 1.07 mPa s). Upon mixing the inner and outer fluid, worm-like micelles are formed to create a gel-like
precipitate. Notably, the number of actively extending filaments, N, saturates around the fixed number
proportional to J. Furthermore, we show that the motion of the advancing tip of the filament can becontrolled to have a straight motion by the appropriately spaced obstacle in the flow cells. A mathematicalmodel based on a moving boundary adopting the effect of precipitation formation reproduced the straightmotion of filaments in structured cells.

Fig.１： The time development of (top) circular pattern, (bottom) filament pattern. Here, injection rate J was
fixed as 200 mL/h, and c= (top)50 and (bottom) 200 mM. Scale bar: 50 mm.

References
1. Shu Wagatsuma, Takuro Higashi, Yutaka Sumino, and Ayumi Achiwa: “Pattern of a confined chemical
garden controlled by injection speed”, Phys. Rev. E 95, 052220 (2017).
2. Shunsuke Tanaka, Kojiro Otoguro, Miyuki Kunihiro, Hiroki Ishikawa, Yutaka Sumino: “Gelation induced
filamentous pattern of injected fluid controlled by structured cell”, in prep.

18

Contributed Talk

Active Matter Workshop 2023

10:55 - 11:20

Hyperuniformity and Singular Density Correlationin Chiral Active Fluids
Yuta Kuroda1, Takeshi Kawasaki1, Kunimasa Miyazaki1
1

Department of Physics, Nagoya University

Active matter systems can be classified by the symmetry of the systems. Chiral active matter, where thecomponents perform chiral symmetry-broken motions, is one of the most discussed classes [1]. Here wefocus on the density fluctuations in the fluidic state of chiral active matter (hereafter referred to as chiralactive fluids). One of the typical hallmarks of active matter is anomalous enhanced density fluctuations atlarge scales, which are called giant number fluctuations (GNF) [2]. GNF has been observed in various active
matter systems, including bacterial suspension, Janus particles, and shaken rods. However, the twodimensional chiral active fluids exhibit an exactly opposite behavior to GNF, in that the density fluctuations
are anomalously suppressed at large scale [3]. This phenomenon is termed hyperuniformity.
Hyperuniformity has been reported in several non-equilibrium systems, such as the jamming system andperiodically driven colloidal suspension [4]. The hyperuniformity is characterized by the static structure
factor 𝑆(𝑞) , where 𝑞 is the wavenumber. If this quantity behaves as 𝑆(𝑞) ∝ 𝑞 𝛼 (𝛼 > 0) for small
wavenumbers, then the system is said to be hyperuniform.
The hyperuniformity in two-dimensional chiral active fluids was first reported in the numerical study[3]. In Ref. [3], a simple particle model, called the chiral active Brownian particles (cABP), was employed,
and it was reported that this system shows hyperuniformity with 𝛼 = 2 . Very recently, experimental
realizations of this phenomenon were achieved by exploiting the marine algae system [5] and anisotropicshaped Quincke rollers [6]. Despite intensive studies, however, a microscopic theory that explains
hyperuniformity is still lacking. Also, all studies in the past are limited to the two-dimensional system andthe three-dimensional case has not been investigated yet.
Here we attempt to understand the density fluctuations in both two- and three-dimensional chiral active
fluids theoretically. First, we put forward a microscopic theory for hyperuniformity in the two-dimensional
system. An effective hydrodynamic description is derived for cABP. We demonstrate that the obtainedhydrodynamic equations yield hyperuniformity with 𝛼 = 2. We also compare our theory with numericalresults. Subsequently, we apply the hydrodynamic theory to the three-dimensional system. We theoreticallyfind the density fluctuations show a singularity that reflects the anisotropy of the system. This theoreticalprediction is corroborated by a numerical simulation.

References
1. Benno Liebchen and Demian Levis: “Chiral active matter”, EPL 139, 67001 (2022)
2. S. Ramaswamy, R. A. Simha, and J. Toner: “Active nematics on a substrate: Giant number fluctuations
and long-time tails”, EPL 62, 196 (2003)
3. Q.-L. Lei, M. P. Ciamarra, and R. Ni: “Nonequilibrium strongly hyperuniform fluids of circle
active particles with large local density fluctuations”, Science Advances 5, eaau7423 (2019)4. S. Torquato, “Hyperuniform states of matter”: Physics Reports 745, 1 (2018)
5. M. Huang, W. Hu, S. Yang, Q.-X. Liu, and H. P. Zhang: “Circular swimming motility and disordered
hyperuniform state in an algae system”, PNAS 118, e2100493118 (2021).
6. B. Zhang and A. Snezhko, “Hyperuniform active chiral fluids with tunable internal structure”: Phys. Rev.
Lett. 128, 218002 (2022).

19

Contributed Talk

Active Matter Workshop 2023

11:20 - 11:45

Rough colloids at interfaces: With scopes of interfacial active matter
Airi N. Kato1,2, Yujie Jiang,1,2, Wei Chen1, Ryohei Seto1,2,3, Tao Li1,2
1
Wenzhou institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China.
(WIUCAS)
2
Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou,
Zhejiang 325001, China.
3
Graduate School of Information Science, University of Hyogo, Kobe, Hyogo 650-0047, Japan.

Colloidal monolayers—consisting of particles trapped at liquid interfaces energetically—have been
known to show various self-assemblies and undergo structural transitions upon compression, depending on
particle morphology [1]. This shape dependency is believed to be due to the quadrupole capillary interactions[2]. Smooth rigid spherical colloids have no capillary interaction ideally, which consequences a directtransition from a gas-like state to a solid-like state, where particles are close-packed jamming; they finallycollapse abruptly under further compression [1].
Similar to anisotropic colloids, particles with surface roughness feature not only capillary attractions [2]but also frictional contact which leads to the shift of jamming point and the strength of shear thickening [3]in 3D dense suspension. Here we study how roughness affects the interactions and thereby phase behaviors
in 2D experimentally and numerically. Experimentally, four surface roughnesses were specifically designed
and compared through isotherm measurements upon compression and microscopic observations. We findthat, for sufficiently rough systems, a nontrivial intermediate state appears in surface pressure–area isotherms
before the close-packed jamming. The intermediate states are attributed to a percolated network (Fig.1, left),caused by roughness-induced capillary attraction. Also, the surface roughness lowers the close-packed
jamming points ϕJ, caused by roughness-induced frictions (Fig.1, center). These behaviors are verified by our
Brownian dynamics simulations; we modeled rough particles at the interface as attractive and frictional
particles. By constraining the tangential motion of particles in contact, we further simulate interlocking
cases. Moreover, the gradual off-plane collapse was observed for very rough particles experimentally,
probably owing to the tangential contact force due to the asperities; buckling and collapse can be simulated
in future works. The details of this work can access via our preprint [4].
Though the systems of this work are NOT active but quasi-equilibrium, some future directions to active
systems will be presented in the talk.

!
Figure 1 SEM micrographs and schematics of the percolation, jamming, and collapsed monolayer
Acknowledgments
We acknowledge the staffs of the common facilities in WIUCAS. The speaker thanks the organizers who
welcome the speaker to talk about our work on non-active systems.
References
1. T. Li et al.: “Langmuir–Blodgett technique for anisotropic colloids: Young investigator perspective”,
Journal of Colloid and Interface Science 540, 420 (2019).
2. L. Botto et al., “Capillary interactions between anisotropic particles”, Soft Matter, 8, 9957 (2012).
3. A. Singh et al.: “Shear Thickening and Jamming of Dense Suspensions: The “Roll” of Friction”,
Physical Review Letters 124, 248005 (2020).
4. A. N. Kato, Y. Jiang, W. Chen, R. Seto, and T. Li, “How Surface Roughness Affects the Interparticle
Interactions at a Liquid Interface”, arXiv: 2209.02922.

20

Contributed Talk

Active Matter Workshop 2023

13:15 - 13:40

Dynamics of a quantum active particle based on 2D non-Hermitian quantum walks
Manami Yamagishi1, Naomichi Hatano2, Hideaki Obuse2, 3
1

Department of Physics and 2Institute of Industrial Science, the University of Tokyo
3
Department of Applied Physics, Hokkaido University

We present a model of quantum active particles using non-Hermitian quantum walks in two dimensions(2D). This talk will be a continuation of the last talk that I gave on the one-dimensional (1D) quantum active
particle in Active Matter Workshop 2022 [1].
Although there are a number of works on active matter, most of them are conducted in classical systems.
Since neither the energy nor the momentum is conserved, it is challenging to express active-matter dynamics
in terms of a quantum Hamiltonian. Adachi et al. [2] used a non-Hermitian quantum spin system to simulate
a “stoquastic” active matter. In contrast to their work with many-body systems, we start with simpler, oneparticle systems to allow systems real-time evolution in a fully quantum range. We aim to reproduce similar
phenomena that Schweitzer et al. [3] numerically found, that is, the dynamics of their (active) Brownian
particle changes depending on energy-take-up term.
We believe that the following two points are essential properties for a system to be an active matter: (i)energy nor momentum are not conserved and (ii) kinetic motion depends on particles’ internal states. A
system without energy conservation is realized with our non-Hermitian Hamiltonian−(−)* $%!!" = # # ⨂% # ⨂ &
,,
&%
−)*
+(
#
#
where # and % are 2 × 2 identity matrices for the space spanned by the leftward and rightward states (|L⟩and |R⟩) and the space spanned by the downward and upward state (|D⟩ and |U⟩), respectively. We introduce
new internal states, the ground state |G⟩ and the excited state |E⟩. The non-Hermiticity parameter 8 promotes
transition from |G⟩ to |E⟩, and hence the particle takes up energy from the environment. We use different
parameter values for |G⟩ and |E⟩, which means that the kinetic motion depends on particle’s internal state, torealize a system without momentum conservation.
To simulate “Brownian motion” under a harmonic potential in a 2D quantum system, we use quantum
walks. The quantum walk (QW) is a quantum analogue of random walk. Instead of stochastic fluctuations ofa classical random walker, a quantum walker moves under interference of quantum fluctuations at each site,which deterministically governs the walker’s dynamics. We start with proposing a 2D Dirac Hamiltonian [4]())!' ≔ (;# + <, + =, (>)# - )⨂% # ⨂@ # + # , ⨂A;% + <- + =- (B)% - C⨂@ # ,which can be mapped to a 2D QW as well as to a Schrödinger
Hamiltonian as we will show in the talk. Here, {# , , # - , # + } and{% , , % - , % + } are the Pauli matrices for the spaces spanned by{|L⟩, |R⟩} and {|D⟩, |U⟩} , respectively, and @ # is a 2 × 2 identity
matrix for the space spanned by |G⟩ and |E⟩. We let =, (>) and=- (B) denote the mass terms, which are proportional to theparameters F, (>) and F- (B) for the coin operators for QW [5]. The(a)momenta <, and <- can be rewritten in the forms of −i H⁄H> and−i H⁄HB , respectively. Figure 1 shows probability distributions
after 500 time steps with different values of 8. We observe that lessamount of wave functions are bound around the center by thepotential in the 8 = 2 case than in the 8 = 0 case. This means that
more wave functions have gone out, climbing up the potential wallin the 8 = 2 case.
[1] ◯M. Yamagishi, N. Hatano, and H. Obuse: “Defining a Quantum Active
Particle Using Non-Hermitian Quantum Walk”, Active Matter Workshop 2022,(b)
Day 2, 8th talk, online, Jan. 2022. [2] K. Adachi et al.: “Activity-induced phase Fig. 1: Probability distribution after
transition in a quantum many-body system”, Phys. Rev. Research 4, 013194(2022). [3] F. Schweitzer et al.: “Complex Motion of Brownian Particles with 500 time steps of evolution. (a) 8 = 0
Energy Depots”, Phys. Rev. Lett. 80, 23 (1998). [4] M. Yamagishi, N. Hatano, and (b) 8 = 2. ( = ) = L = 0.2.
K-I. Imura, and H. Obuse: “Multi-Dimensional Quantum Walks: a Playground of Dirac and Schrödinger Particles”,
arXiv:2211.09336 (2022). [5] F. W. Strauch: “Relativistic quantum walks” Phys. Rev. A 73, 054302 (2006).

21

Contributed Talk

Active Matter Workshop 2023

13:40 - 14:05

Self-propulsion of 3-Phenylpropionaldehyde Droplet in
Homogeneous and Heterogeneous Aqueous Solutions
Ayase Kawamura and Shinpei Tanaka
Graduate School of Advanced Science and Engineering, Hiroshima University
In the presence of local interfacial tension gradient at the oil-water or air-water interface,oil droplets can be driven in the water or on the water surface by the flow generated at the interface.
We have recently found a new self-propulsive system of 3-Phenylpropionaldehyde (3PPA)
droplets, capable of self-propelled both on the surface of Sodium Decyl Sulfate (SDS) aqueous
solution and on the glass surface in the solution.
Furthermore, we have also found that the system has the ability to synthesize surfactants
when amino acid is added to the solution. The amino acid, histidine (HIS), reacts with 3PPA, analdehyde, to produce an amphiphilic compound through spontaneous condensation reaction.
Although it was expected that the spontaneously generated surfactant propelled droplets, weobserved that the produced surfactant could not cause the droplet’s motion, even though it
decreased the interfacial tension by the same degree as that of the SDS solution. On the other hand,
we have noticed that the motion of a 3PPA droplet is significantly changed by the existence of HIS
in the SDS solution. In a SDS solution containing HIS, longer duration of propulsion andreciprocation with higher frequency were found to occur compared to a SDS solution without HIS.
In addition to this homogeneous system, we investigated the effect of inhomogeneous
distribution of HIS in the SDS solution on the 3PPA droplet. When 3PPA droplets were in aperiodic motion on the surface of aqueous SDS solution, HIS powder was added locally to thesolution. Figure 1 shows how the motion was altered before and after the addition of HIS. As
Figure 1(a) shows, the 3PPA droplet was oscillated periodically before the addition of HIS. Afterthe addition of HIS powder, 3PPA was once attracted to the HIS-rich region immediately(Figure(b)) and exhibited a motion with rapid oscillation thereafter as shown in Figure 1(c).
In the presentation, these self-propulsion phenomena will be discussed in more detail.

Fig.1 The motion of 3PPA droplet. (a) Before HIS powder was placed on the solution, (b) The
3PPA showed sudden directional change in the middle of the motion as a result of attraction to
the inserted HIS powder; HIS powder is represented as orange area. (c) The rapid reciprocationsof the 3PPA during the motion after the attraction.

22

Contributed Talk

14:05 - 14:30

Active Matter Workshop 2023

Collective motion of active droplets and how to grasp it
Shinpei Tanaka
Graduate School of Advanced Science and Engineering, Hiroshima University
Alkylsalicylate droplets have been known [1] to exhibit self-propulsion when they are placed on the surfaceof surfactant solution. Their collective motion is distinctively complex. For example, droplets repeat forming andreforming a circular cluster, collapsing immediately after its formation. Figure 1 shows such a behavior seen in asample of 10 droplets. The physical mechanisms behind their collective motion are unknown.
We have proposed [1] that a distribution of droplets relative to a droplet and its nearest neighbor could be used
to contract motion behavior into a static 2-dimensional map. Figure 2a shows an example of the distribution. A
dark region around the origin is where droplets are excluded because a droplet and its nearest neighbor are locatedat the two centers of the region. Whereas the information about time is lost, the distribution clearly shows where
droplets tend to locate around the pair. Moreover, it shows a certain symmetry indicating a dynamic order in the
motion behavior.
In this study, we used the distribution as a target to be realized by a simulation model with adjustable parameters.
We tested the method with a non-physical toy model having a three-body interaction term introduced as a driving
force of particles. The parameters in the model were adjusted so that the distance between the distribution ofdroplets (Fig. 2a) and that of the particles in simulation (Fig. 2b) became minimum. The resulting motion patternseen in the simulation (Fig. 2c) was considered to best simulate the motion pattern of droplets. I will discuss in thepresentation how much motion patterns are reproduced by a simple toy model adjusted using this method.

a

c

b

d

Fig. 1. Collective motion of 10 ethylsalicylate droplets on the surface of sodium dodecyl sulfate solution.
Images are shown every 17 s. The Petri dish was 88 mm in diameter.

a

c

b

Fig. 2. (a) A distribution of droplets relative to a droplet at the origin and its nearest neighbor pair on the x-axis.
(b) The same distribution obtained by a simulation. (c) A snapshot of a simulation.
Reference
1. Shinpei Tanaka, Satoshi Nakata and Takeshi Kano, “Dynamic ordering in a swarm of floating droplets driven
by solutal Marangoni effect”, J. Phys. Soc. Jpn., 86, 101004 (2017).

23