Realizing the physics of motile cilia synchronization with driven colloids

Cilia and flagella in biological systems often show large scale cooperative behaviors such as the synchronization of their beats in "metachronal waves". These are beautiful examples of emergent dynamics in biology, and are essential for life, allowing diverse processes from the motility of eukaryotic microorganisms, to nutrient transport and clearance of pathogens from mammalian airways. How these collective states arise is not fully understood, but it is clear that individual cilia interact mechanically,and that a strong and long ranged component of the coupling is mediated by the viscous fluid. We review here the work by ourselves and others aimed at understanding the behavior of hydrodynamically coupled systems, and particularly a set of results that have been obtained both experimentally and theoretically by studying actively driven colloidal systems. In these controlled scenarios, it is possible to selectively test aspects of the living motile cilia, such as the geometrical arrangement, the effects of the driving profile and the distance to no-slip boundaries. We outline and give examples of how it is possible to link model systems to observations on living systems, which can be made on microorganisms, on cell cultures or on tissue sections. This area of research has clear clinical application in the long term, as severe pathologies are associated with compromised cilia function in humans.Comment: 31 pages, to appear in Annual Review of Condensed Matter Physic

Emergent gauge dynamics of highly frustrated magnets

Condensed matter exhibits a wide variety of exotic emergent phenomena such as the fractional quantum Hall effect and the low temperature cooperative behavior of highly frustrated magnets. I consider the classical Hamiltonian dynamics of spins of the latter phenomena using a method introduced by Dirac in the 1950s by assuming they are constrained to their lowest energy configurations as a simplifying measure. Focusing on the kagome antiferromagnet as an example, I find it is a gauge system with topological dynamics and non-locally connected edge states for certain open boundary conditions similar to doubled Chern-Simons electrodynamics expected of a $Z_2$ spin liquid. These dynamics are also similar to electrons in the fractional quantum Hall effect. The classical theory presented here is a first step towards a controlled semi-classical description of the spin liquid phases of many pyrochlore and kagome antiferromagnets and towards a description of the low energy classical dynamics of the corresponding unconstrained Heisenberg models.Comment: Updated with some appendices moved to the main body of the paper and some additional improvements. 21 pages, 5 figure

Collective motion, sensor networks, and ocean sampling

Author Posting. © IEEE, 2007. This article is posted here by permission of IEEE for personal use, not for redistribution. The definitive version was published in Proceedings of the IEEE 95 (2007): 48-74, doi:10.1109/jproc.2006.887295.This paper addresses the design of mobile sensor networks for optimal data collection. The development is strongly motivated by the application to adaptive ocean sampling for an autonomous ocean observing and prediction system. A performance metric, used to derive optimal paths for the network of mobile sensors, defines the optimal data set as one which minimizes error in a model estimate of the sampled field. Feedback control laws are presented that stably coordinate sensors on structured tracks that have been optimized over a minimal set of parameters. Optimal, closed-loop solutions are computed in a number of low-dimensional cases to illustrate the methodology. Robustness of the performance to the influence of a steady flow field on relatively slow-moving mobile sensors is also explored

Autonomous engines driven by active matter: Energetics and design principles

Because of its nonequilibrium character, active matter in a steady state can drive engines that autonomously deliver work against a constant mechanical force or torque. As a generic model for such an engine, we consider systems that contain one or several active components and a single passive one that is asymmetric in its geometrical shape or its interactions. Generally, one expects that such an asymmetry leads to a persistent, directed current in the passive component, which can be used for the extraction of work. We validate this expectation for a minimal model consisting of an active and a passive particle on a one-dimensional lattice. It leads us to identify thermodynamically consistent measures for the efficiency of the conversion of isotropic activity to directed work. For systems with continuous degrees of freedom, work cannot be extracted using a one-dimensional geometry under quite general conditions. In contrast, we put forward two-dimensional shapes of a movable passive obstacle that are best suited for the extraction of work, which we compare with analytical results for an idealised work-extraction mechanism. For a setting with many noninteracting active particles, we use a mean-field approach to calculate the power and the efficiency, which we validate by simulations. Surprisingly, this approach reveals that the interaction with the passive obstacle can mediate cooperativity between otherwise noninteracting active particles, which enhances the extracted power per active particle significantly.Comment: 21 pages, 8 figure

Materia resonante: patrones que correlacionan con modelos-in-formativos

Through the aid of ever advancing technology, the analysis of complex phenomena offers us more comprehensive insights regarding the intricate inner workings of Nature’s dynamic processes. Through such digital simulations (i.e., of fluid, aero, neuro and vibratory dynamics), the operations and flow of energy are revealed as highly patterned process-structures of activity. These vivid configurations often resemble and correlate with the patterns and motifs found at different scales throughout Nature and in a myriad of cultural artifacts. As intricately braided cellular relationships, these fertile processes evolve into highly integrative systems with re-generative, shape-shifting and re-structuring capabilities. Moreover, they are robust coalitions of event-filled-processes, highly responsive and fluently encoded with information. This embodied potential of generative kinetic in-formation and related patterns have been explored and offer more comprehensive insights regarding the resonances between self-organization, pattern generation and emergent complex morphology. At the heart of this lies the nature of process-structures and their elaborations into multi-dimensionally entrained kinetic patterns of patterns-in-formation. We are experientially embodied with and inextricably embedded within this interplay of ubiquitous metapatterns with reciprocally related cultural artifacts and motifs offering insightful resonances as analytical tools advance and probe further into the inner workings of the human mind and the nature of embodied consciousness.Con la ayuda de una tecnología en constante desarrollo, el análisis de los fenómenos complejos nos ofrece una visión más completa sobre el funcionamiento interno de los procesos dinámicos que suceden en la Naturaleza. A través de simulaciones digitales (por ejemplo, la neurodinámica o la de fluidos, gases y vibratorias), las operaciones y el flujo de energía se revelan como procesos-estructurales de actividad que se ajustan a un modelo. Estas intensas configuraciones a menudo se asemejan y se correlacionan con los patrones y motivos encontrados en distintas escalas a través de la Naturaleza y en numerosos artefactos culturales. De forma similar a relaciones celulares enmarañadamente trenzadas, estos procesos fértiles evolucionan en sistemas altamente integradores con capacidades regenerativas, cambiantes de forma y restructuradoras. Más aún, son robustas coaliciones procesos-eventos-completos, altamente sensibles y con gran información codificada. Esta encarnación del potencial de in-formación cinética generativa y los patrones relacionados, al explorarse, ofrecen amplios puntos de vista con respecto a las interrelaciones entre la autogestión, la generación de patrones y el surgimiento de una morfología compleja. En esto radica la naturaleza de los procesos-estructurales y sus elaboraciones en patrones de los modelos-in-formativos, entrelazados cinéticamente y multidimensionalmente. Estamos experimentalmente encarnados y complejamente inmersos en este juego de metapatrones ubicuos, recíprocamente relacionados con artefactos y motivos culturales, que ofrecen unas resonancias profundas como herramientas analíticas de avance y sondeo que van más allá del funcionamiento interno de la mente humana y de la naturaleza de su conciencia

Stochastic dynamics of migrating cells

Cell migration is critical in many physiological phenomena, including embryogenesis, immune response, and cancer. In all these processes, cells face a common physical challenge: they navigate confining extra-cellular environments, in which they squeeze through thin constrictions. The motion of cells is powered by a complex machinery whose molecular basis is increasingly well understood. However, a quantitative understanding of the functional cell behaviours that emerge at the cellular scale remains elusive. This raises a central question, which acts as a common thread throughout the projects in this thesis: do migrating cells exhibit emergent dynamical 'laws' that describe their behavioural dynamics in confining environments? To address this question, we develop data-driven approaches to infer the dynamics of migrating cells directly from experimental data. We study the migration of cells in artificial confinements featuring a thin constriction across which cells repeatedly squeeze. From the experimental cell trajectories, we infer an equation of cell motion, which decomposes the dynamics into deterministic and stochastic contributions. This approach reveals that cells deterministically drive themselves into the thin constriction, which is in contrast to the intuition that constrictions act as effective barriers. This active driving leads to intricate non-linear dynamics that are poised close to a bifurcation between a bistable system and a limit cycle oscillator. We further generalize this data-driven framework to detect and characterize the variance of migration behaviour within a cell population and to investigate how cells respond to varying confinement size, shape, and orientation. We next investigate the mechanistic basis of these dynamics. Cell migration relies on the concerted dynamics of several cellular components, including cell protrusions and adhesive connections to the environment. Based on the experimental data, we systematically constrain a mechanistic model for confined cell migration. This model indicates that the observed deterministic driving is a consequence of the combined effects of the variable adhesiveness of the environment and a self-reinforcement of cell polarity in response to thin constrictions. These results suggest polarity feedback adaptation as a key mechanism in confined cell migration. Finally, we investigate the dynamics of interacting cells. To enable inference of cell-cell interactions, we develop Underdamped Langevin Inference, an inference method for stochastic high-dimensional and interacting systems. We apply this method to experiments of confined pairs of cells, which repeatedly collide with one another. This reveals that non-cancerous (MCF10A) and cancerous (MDA-MB-231) cells exhibit distinct interactions: while the non-cancerous cells exhibit repulsion and effective friction, the cancerous cells exhibit attraction and a surprising 'anti-friction' interaction. These interactions lead to non-cancerous cells predominantly reversing upon collision, while the cancer cells are able to efficiently move past one another by relative sliding. Furthermore, we investigate the effects of cadherin-mediated molecular contacts on cell-cell interactions in collective migration. Taken together, the data-driven approaches presented in this thesis may help to provide a new avenue to uncover the emergent laws governing the stochastic dynamics of migrating cells. We demonstrate how these approaches can provide key insights both into underlying mechanisms as well as emergent cell behaviours at larger scales.Zellmigration ist ein Kernelement vieler physiologischer Phänomene wie der Embryogenese, dem Immunsystem und der Krebsmetastase. In all diesen Prozessen stehen Zellen vor einer physikalischen Herausforderung: Sie bewegen sich in beengten Umgebungen, in denen sie Engstellen passieren müssen. Die Zellbewegung wird von einer komplexen Maschinerie an- getrieben, deren molekulare Komponenten immer besser verstanden werden. Demgegenüber fehlt ein quantitatives Verständnis des funktionalen Migrationsverhaltens der Zelle als Ganzes. Die verbindende Fragestellung der Projekte in dieser Arbeit lautet daher: gibt es emergente dynamische 'Gesetze', die die Verhaltensdynamik migrierender Zellen in beengten Umgebungen beschreiben? Um dieser Frage nachzugehen, entwickeln wir datengetriebene Ansätze, die es uns erlauben, die Dynamik migrierender Zellen direkt aus experimentellen Daten zu inferieren. Wir untersuchen Zellmigration in künstlichen Systemen, in denen Zellen Engstellen wiederholt passieren müssen. Aus den experimentellen Zelltrajektorien inferieren wir eine Bewegungsgleichung, die die Dynamik in deterministische und stochastische Komponenten trennt. Diese Methode zeigt, dass sich Zellen deterministisch 'aktiv' in die Engstellen hineinbewegen, ganz entgegen der intuitiven Erwartung, dass Engstellen als Hindernis fungieren könnten. Dieser aktive Antrieb führt zu einer komplexen nichtlinearen Dynamik im Übergangsbereich zwischen einem bistabilen System und einem Grenzzyklus-Oszillator. Wir verallgemeinern diesen datenbasierten Ansatz, um die Varianz des Migrationsverhaltens innerhalb einer Zellpopulation zu quantifizieren, und analysieren, wie Zellen auf die Größe, Form und Orientierung ihrer Umgebung reagieren. Darauf aufbauend untersuchen wir die zugrundeliegenden Mechanismen dieser Dynamik. Zellmigration basiert auf verschiedenen zellulären Komponenten, wie unter Anderem den Zellprotrusionen und der Adhäsion mit der Umgebung. Auf Basis der experimentellen Daten entwickeln wir ein mechanistisches Modell für Zellmigration in beengten Systemen, welches zeigt, dass der beobachtete aktive Antrieb eine Konsequenz zweier Effekte ist: Einer variierenden Adhäsion mit der Umgebung und einer Zellpolarität, die sich in Engstellen selbst verstärkt. Diese Ergebnisse deuten darauf hin, dass die Anpassung der Zellpolarität an die lokale Geometrie ein Schlüsselmechanismus in beengter Zellmigration ist. Schließlich analysieren wir die Dynamik interagierender Zellen. Um Zell-Zell Interaktionen zu inferieren, entwickeln wir die Underdamped Langevin Inference, eine Inferenzmethode für stochastische hochdimensionale und interagierende Systeme. Wir wenden diese Methode auf Daten von eingeschlossenen Zellpaaren an, welche wiederholt miteinander kollidieren. Dies zeigt, dass gesunde (MCF10A) und krebsartige (MDA-MB-231) Zellen unterschiedliche Interaktionen aufweisen: Während gesunde Zellen mit Abstoßung und effektiver Reibung interagieren, zeigen Krebszellen Anziehung und eine überraschende 'Anti-Reibung'. Diese Interaktionen führen dazu, dass gesunde Zellen nach Kollisionen primär umkehren, während Krebszellen effizient aneinander vorbeigleiten. Darüberhinaus analysieren wir die Effekte von Cadherin-basierten Molekularkontakten auf Zell-Zell Interaktionen in kollektiver Migration. Zusammenfassend könnten die in dieser Arbeit präsentierten datengetriebenen Ans ̈atze dabei helfen, ein besseres Verständnis der emergenten stochastischen Dynamik migrierender Zellen zu erlangen. Wir zeigen, wie diese Methoden wichtige Erkenntnisse sowohl über die zugrundeliegenden Mechanismen als auch über das emergente Zellverhalten liefern können