Exact coherent structures and transition to turbulence in a confined active nematic

Active matter describes a class of systems that are maintained far from equilibrium by driving forces acting on the constituent particles. Here I will focus on confined active nematics, which exhibit especially rich flow behavior, ranging from structured patterns in space and time to disordered turbulent flows. To understand this behavior, I will take a deterministic dynamical systems approach, beginning with the hydrodynamic equations for the active nematic. This approach reveals that the infinite-dimensional phase space of all possible flow configurations is populated by Exact Coherent Structures (ECS), which are exact solutions of the hydrodynamic equations with distinct and regular spatiotemporal structure; examples include unstable equilibria, periodic orbits, and traveling waves. The ECS are connected by dynamical pathways called invariant manifolds. The main hypothesis in this approach is that turbulence corresponds to a trajectory meandering in the phase space, transitioning between ECS by traveling on the invariant manifolds. Similar approaches have been successful in characterizing high Reynolds number turbulence of passive fluids. Here, I will present the first systematic study of active nematic ECS and their invariant manifolds and discuss their role in characterizing the phenomenon of active turbulence.

Theory of activity-powered interface

Interfaces and membranes are ubiquitous in cellular systems across various scales. From lipid membranes to the interfaces of biomolecular condensates inside the cell, these borders not only protect and segregate the inner components from the outside world, but also are actively participating in mechanical regulation and biochemical reaction of the cell. Being part of a living system, these interfaces (membranes) are usually active and away from equilibrium. Yet, it's still not clear how activity can tweak their equilibrium dynamics. Here, I will introduce a model system to tackle this problem. We put together a passive fluid and an active nematics, and study the behavior of this liquid-liquid interface. Whereas thermal fluctuation of such an interface is too weak to be observed, active stress can easily force the interface to fluctuate, overhang, and even break up. In the presence of a wall, the active phase exhibits superfluid-like behavior: it can climb up walls -- a phenomenon we call activity-induced wetting. I will show how to formulate theories to capture these phenomena, highlighting the nontrivial effects of active stress. Our work not only demonstrates that activity can introduce interesting features to an interface, but also sheds light on controlling interfacial properties using activity.

Do leader cells drive collective behavior in Dictyostelium Discoideum amoeba colonies?

Dictyostelium Discoideum (DD) are a fascinating single-cellular organism. When nutrients are plentiful, the DD cells act as autonomous individuals foraging their local vicinity. At the onset of starvation, a few (<0.1%) cells begin communicating with others by emitting a spike in the chemoattractant protein cyclic-AMP. Nearby cells sense the chemical gradient and respond by moving toward it and emitting a cyclic-AMP spike of their own. Cyclic-AMP activity increases over time, and eventually a spiral wave emerges, attracting hundreds of thousands of cells to an aggregation center. How DD cells go from autonomous individuals to a collective entity remains an open question for more than 60 years--a question whose answer would shed light on the emergence of multi-cellular life. Recently, trans-scale imaging has allowed the ability to sense the cyclic-AMP activity at both cell and colony levels. Using both the images as well as toy simulation models, this research aims to clarify whether the activity at the colony level is in fact initiated by a few cells, which may be deemed "leader" or "pacemaker" cells. In this talk, I will demonstrate the use of information-theoretic techniques to classify leaders and followers based on trajectory data, as well as to infer the domain of interaction of leader cells. We validate the techniques on toy models where leaders and followers are known, and then try to answer the question in real data--do leader cells drive collective behavior in DD colonies?

3D Printing Cellular Communities: Mammalian Cells, Bacteria, And Beyond

While the motion and collective behavior of cells are well-studied on flat surfaces or in unconfined liquid media, in most natural settings, cells thrive in complex 3D environments. Bioprinting processes are capable of structuring cells in 3D and conventional bioprinting approaches address this challenge by embedding cells in bio-degradable polymer networks. However, heterogeneity in network structure and biodegradation often preclude quantitative studies of cell behavior in specified 3D architectures. Here, I will present a new approach to 3D bioprinting of cellular communities that utilizes jammed, granular polyelectrolyte microgels as a support medium. The self-healing nature of this medium allows the creation of highly precise cellular communities and tissue-like structures by direct injection of cells inside the 3D medium. Further, the transparent nature of this medium enables precise characterization of cellular behavior. I will describe two examples of my work using this platform to study the behavior of two different classes of cells in 3D. First, I will describe how we interrogate the growth, viability, and migration of mammalian cells—ranging from epithelial cells, cancer cells, and T cells—in the 3D pore space. Second, I will describe how we interrogate the migration of E. coli bacteria through the 3D pore space. Direct visualization enables us to reveal a new mode of motility exhibited by individual cells, in stark contrast to the paradigm of run-and-tumble motility, in which cells are intermittently and transiently trapped as they navigate the pore space; further, analysis of these dynamics enables prediction of single-cell transport over large length and time scales. Moreover, we show that concentrated populations of E. coli can collectively migrate through a porous medium—despite being strongly confined—by chemotactically “surfing” a self-generated nutrient gradient. Together, these studies highlight how the jammed microgel medium provides a powerful platform to design and interrogate complex cellular communities in 3D—with implications for tissue engineering, microtissue mechanics, studies of cellular interactions, and biophysical studies of active matter.

Trapping active particles up to the limiting case: bacteria enclosed in a biofilm

Active matter systems are composed of constituents, each one in nonequilibrium, that consume energy in order to move [1]. A characteristic feature of active matter is collective motion leading to nonequilibrium phase transitions or large scale directed motion [2]. A number of recent works have featured active particles interacting with obstacles, either moving or fixed [3,4,5]. When an active particle encounters an asymmetric obstacle, different behaviours are detected depending on the nature of its active motion. On the one side, rectification effects arise in a suspension of run-and-tumble particles interacting with a wall of funnelled-shaped openings, caused by particles persistence length [6]. The same trapping mechanism could be responsible for the intake of microorganisms in the underground leaves [7] of Carnivorous plants [8]. On the other side, for aligning particles [9] interacting with a wall of funnelled-shaped openings, trapping happens on the (opposite) wider opening side of the funnels [10,11]. Interestingly, when funnels are located on a circular array, trapping is more localised and depends on the nature of the Vicsek model. Active particles can be synthetic (such as synthetic active colloids) or alive (such as living bacteria). A prototypical model to study living microswimmers is P. fluorescens, a rod shaped and biofilm forming bacterium. Biofilms are microbial communities self-assembled onto external interfaces. Biofilms can be described within the Soft Matter physics framework [12] as a viscoelastic material consisting of colloids (bacterial cells) embedded in a cross-linked polymer gel (polysaccharides cross-linked via proteins/multivalent cations), whose water content vary depending on the environmental conditions. Bacteria embedded in the polymeric matrix control biofilm structure and mechanical properties by regulating its matrix composition. We have recently monitored structural features of Pseudomonas fluorescens biofilms grown with and without hydrodynamic stress [13,14]. We have demonstrated that bacteria are capable of self-adapting to hostile hydrodynamic stress by tailoring the biofilm chemical composition, thus affecting both the mesoscale structure of the matrix and its viscoelastic properties that ultimately regulate the bacteria-polymer interactions. REFERENCES [1] C. Bechinger et al. Rev. Mod. Phys. 88, 045006 (2016); [2] T. Vicsek, A. Zafeiris Phys. Rep. 517, 71 (2012); [3] C. Bechinger, R. Di Leonardo, H. Lowen, C. Reichhardt, G. Volpe, and G. Volpe, Reviews of Modern Physics 88, 045006 (2016); [4] R Martinez, F Alarcon, DR Rodriguez, JL Aragones, C Valeriani The European Physical Journal E 41, 1 (2018); [5] DR Rodriguez, F Alarcon, R Martinez, J Ramírez, C Valeriani, Soft matter 16 (5), 1162 (2020); [6] C. O. Reichhardt and C. Reichhardt, Annual Review of Condensed Matter
Physics 8, 51 (2017); [7] W Barthlott, S Porembski, E Fischer, B Gemmel Nature 392, 447 (1998); [8] C B. Giuliano, R Zhang, R.Martinez Fernandez, C.Valeriani and L.Wilson (in preparation, 2021); [9] R Martinez, F Alarcon, JL Aragones, C Valeriani Soft matter 16 (20), 4739 (2020); [10] P. Galajada, J. Keymer, P. Chaikin and R.Austin, Journal of bacteriology, 189, 8704 (2007); [11] M. Wan, C.O. Reichhardt, Z. Nussinov, and C. Reichhardt, Physical Review Letters 101, 018102 (2008); [12] J N. Wilking , T E. Angelini , A Seminara , M P. Brenner , and D A. Weitz MRS Bulletin 36, 385 (2011); [13]J Jara, F Alarcón, A K Monnappa, J Ignacio Santos, V Bianco, P Nie, M Pica Ciamarra, A Canales, L Dinis, I López-Montero, C Valeriani, B Orgaz, Frontiers in microbiology 11, 3460 (2021); [14] P Nie, F Alarcon, I López-Montero, B Orgaz, C Valeriani, M Pica Ciamarra

Liquid-liquid phase separation out of equilibrium

Living cells contain millions of enzymes and proteins, which carry out multiple reactions simultaneously. To optimize these processes, cells compartmentalize reactions in membraneless liquid condensates. Certain features of cellular condensates can be explained by principles of liquid-liquid phase separation studied in material science. However, biological condensates exist in the inherently out of equilibrium environment of a living cell, being driven by force-generating microscopic processes. These cellular conditions are fundamentally different than the equilibrium conditions of liquid-liquid phase separation studied in materials science and physics. How condensates function in the active riotous environment of a cell is essential for understanding of cellular functions, as well as to the onset of neurodegenerative diseases. Currently, we lack model systems that enable rigorous studies of these processes. Living cells are too complex for quantitative analysis, while reconstituted equilibrium condensates fail to capture the non-equilibrium environment of biological cells. To bridge this gap, we reconstituted a DNA based membraneless condensates in an active environment that mimics the conditions of a living cell. We combine condensates with a reconstituted network of cytoskeletal filaments and molecular motors, and study how the mechanical interactions change the phase behavior and dynamics of membraneless structures. Studying these composite materials elucidates the fundamental physics rules that govern the behavior of liquid-liquid phase separation away from equilibrium while providing insight into the mechanism of condensate phase separation in cellular environments.

Soft Capricious Matter: The collective behavior of particles with “noisy” interactions

Diversity in the natural world emerges from the collective behavior of large numbers of interacting objects. Statistical physics provides the framework relating microscopic to macroscopic properties. A fundamental assumption underlying this approach is that we have complete knowledge of the interactions between the microscopic entities. But what if that, even though possible in principle becomes impossible in practice ? Can we still construct a framework for describing their collective behavior ? Dense suspensions and granular materials are two often quoted examples where we face this challenge. These are systems where because of the complicated surface properties of particles there is extreme sensitivity of the interactions to particle positions. In this talk, I will present a perspective based on notions of constraint satisfaction that provides a way forward. I will focus on our recent work on the emergence of elasticity in the absence of any broken symmetry, and sketch out other problems that can be addressed using this perspective.

Length Scales and Dynamics in Contractile Active Gels

Most materials deform when external stresses are applied. This paradigm is familiar to sculptors who deform clay to produce structures. However, living materials such as cells and embryos are capable of deforming on their own. Contractile active gels of the proteins actin and myosin are one of the main drivers of force generation in biology. Here I will present experiments that characterize the length-scale behavior of active gel contraction, which find evidence for critical behavior. I will then present experiments that characterize the dynamics of active gel contraction, which identify dynamic precursors to contraction.

“Super Spreaders in the Corona Epidemics”

Recently a powerful example of a replicating nano-machine entered our society. In principle, it’s just a normal disease, that one attempts to model with 3 or 4 simple coupled equations with 2 important parameters: a timescale, and a replication factor (the famous R0). Then one tries to guess how changes in society change R0 and perhaps adopt some more or less strong lock-down measures. However, this virus has more “personality” than that. It behaves differently in different persons, and persons behave differently. Presumably, only a few of us infect a lot, while most do not infect so much. This assumption is supported by the observation that couples living together only infect each other with about 15 percent probability, indicating that most infected people are not really infectious. I will discuss this and other aspects of Covid-19 in the perspective of models that describe heterogeneous individuals in a society. In particular, we suggest that limiting superspreading opportunities is a cost-effective strategy to mitigate Covid-19.

Untitled Seminar

Can machine learning learn new physics, or do we need to put it in by hand?"\

There has been a surge of publications on using machine learning (ML) on experimental data from physical systems: social, biological, statistical, and quantum. However, can these methods discover fundamentally new physics? It can be that their biggest impact is in better data preprocessing, while inferring new physics is unrealistic without specifically adapting the learning machine to find what we are looking for — that is, without the “intuition” — and hence without having a good a priori guess about what we will find. Is machine learning a useful tool for physics discovery? Which minimal knowledge should we endow the machines with to make them useful in such tasks? How do we do this? Eight speakers below will anchor the workshop, exploring these questions in contexts of diverse systems (from quantum to biological), and from general theoretical advances to specific applications. Each speaker will deliver a 10 min talk with another 10 minutes set aside for moderated questions/discussion. We expect the talks to be broad, bold, and provocative, discussing where the field is heading, and what is needed to get us there.

Physics of Behavior: Now that we can track (most) everything, what can we do with the data?

We will organize the workshop around one question: “Now that we can track (most) everything, what can we do with the data?” Given the recent dramatic advances in technology, we now have behavioral data sets with orders of magnitude more accuracy, dimensionality, diversity, and size than we had even a few years ago. That being said, there is still little agreement as to what theoretical frameworks can inform our understanding of these data sets and suggest new experiments we can perform. We hope that after this workshop we’ll see a variety of new ideas and perhaps gain some inspiration. We have invited eight speakers, each studying different systems, scales, and topics, to provide 10 minute presentations focused on the above question, with another 10 minutes set aside for questions/discussions (moderated by the two of us). Although we naturally expect speakers to include aspects of their own work, we have encouraged all of them to think broadly and provocatively. We are also hoping to organize some breakout sessions after the talks so that we can have some more expanded discussions about topics arising during the meeting.