Erçağ Pinçe defended his PhD thesis on 21 October 2016. Assist. Prof. Evren Doruk Engin (Ankara University), Assist. Prof. Giovanni Volpe (Bilkent University), Assist. Prof. Balázs Hétenyi (Bilkent University), Assoc. Prof. Fatih Ömer İlday (Bilkent University) and Prof. Alper Kiraz (Koç University) participated as thesis committee members.
Erçağ Pinçe investigated the role that spatial disorder can play to alter collective dynamics in a colloidal living active matter system where motile E. Coli bacteria are present. The results suggested that the level of heterogeneity present in the background changes the long-term spatial dynamics in a colloidal ensemble coupled to a bacterial bath. This work provided insights about statistical behavior and far-from-equilibrium interactions in an active matter system.
Thesis title: Manipulation and control of collective behavior in active matter systems
Thesis advisor: Giovanni Volpe
Thesis abstract: Active matter systems consist of active constituents that transform energy into directed motion in a non-equilibrium setting. The interaction of active agents with each other and with their environment results in collective motion and emergence of long-range ordering. Examples to such dynamic behaviors in living active matter systems are pattern formation in bacterial colonies, ocking of birds and clustering of pedestrian crowds. All these phenomena stem from far-from-equilibrium interactions. The governing dynamics of these phenomena are not yet fully understood and extensively studied. In this thesis, we studied the role that spatial disorder can play to alter collective dynamics in a colloidal living active matter system. We showed that the level of heterogeneity in the environment in uences the long-range order in a colloidal ensemble coupled to a bacterial bath where the non-equilibrium forces imposed by the bacteria become pivotal to control switching between gathering and dispersal of colloids. Apart from studying environmental factors in a complex active matter system, we also focused on a new class of active particles, \bionic microswimmers”, and their clustering behavior. We demonstrated that spherical bionic microswimmers which are fabricated by attaching motile E. coli bacteria on melamine particles can agglomerate in large colloidal structures. Finally, we observed the emergence of swimming clusters as a result of the collective motion of bionic microswimmers. Our results provide insights about statistical behavior and far-from-equilibrium interactions in an active matter system.
An SDE approximation for stochastic differential delay equations with colored state-dependent noise
Austin McDaniel, Ozer Duman, Giovanni Volpe & Jan Wehr
Markov Processes and Related Fields 22(3), 595-628 (2016)
We consider a general multidimensional stochastic differential delay equation (SDDE) with colored state-dependent noises. We approxi-mate it by a stochastic differential equation (SDE) system and calcu- late its limit as the time delays and the correlation times of the noises go to zero. The main result is proven using a theorem of convergence of stochastic integrals developed by Kurtz and Protter. The result formalizes and extends a method that has been used in the analysis of a noisy electrical circuit with delayed state-dependent noise, and may be further used as a working SDE approximation of an SDDE system modeling a real system, where noises are correlated in time and whose response to stimuli is delayed.
Disrupted Network Topology in Patients with Stable and Progressive Mild Cognitive Impairment and Alzheimer’s Disease
Joana B. Pereira, Mite Mijalkov, Ehsan Kakaei, Patricia Mecocci, Bruno Vellas, Magda Tsolaki, Iwona Kłoszewska, Hilka Soininen, Christian Spenger, Simmon Lovestone, Andrew Simmons, Lars-Olof Wahlund, Giovanni Volpe & Eric Westman, AddNeuroMed consortium, for the Alzheimer’s Disease Neuroimaging Initiative
Cerebral Cortex 26(8), 3476—3493 (2016)
Recent findings suggest that Alzheimer’s disease (AD) is a disconnection syndrome characterized by abnormalities in large- scale networks. However, the alterations that occur in network topology during the prodromal stages of AD, particularly in patients with stable mild cognitive impairment (MCI) and those that show a slow or faster progression to dementia, are still poorly understood. In this study, we used graph theory to assess the organization of structural MRI networks in stable MCI (sMCI) subjects, late MCI converters (lMCIc), early MCI converters (eMCIc), and AD patients from 2 large multicenter cohorts: ADNI and AddNeuroMed. Our findings showed an abnormal global network organization in all patient groups, as reflected by an increased path length, reduced transitivity, and increased modularity compared with controls. In addition, lMCIc, eMCIc, and AD patients showed a decreased path length and mean clustering compared with the sMCI group. At the local level, there were nodal clustering decreases mostly in AD patients, while the nodal closeness centrality detected abnormalities across all patient groups, showing overlapping changes in the hippocampi and amygdala and nonoverlapping changes in parietal, entorhinal, and orbitofrontal regions. These findings suggest that the prodromal and clinical stages of AD are associated with an abnormal network topology.
Often it is desirable to stabilize a system around an optimal state. This can be effectively accomplished using feedback control, where the system deviation from the desired state is measured in order to determine the magnitude of the restoring force to be applied. Contrary to conventional wisdom, i.e. that a more precise measurement is expected to improve the system stability, here we demonstrate that a certain degree of measurement error can improve the system stability. We exemplify the implications of this finding with numerical examples drawn from various fields, such as the operation of a temperature controller, the confinement of a microscopic particle, the localization of a target by a microswimmer, and the control of a population.
Jalpa Soni from the bioNaP lab at the Indian Institute of Science Education and Research Kolkata, India, joined the Soft Matter Lab on 1 June 2016 as a postdoctoral researcher.
Her PhD thesis, “Quantitative Mueller matrix polarimetry in biophotonics and nanoplasmonics”, deals with understanding the interaction of polarized
light in various biophotonic and plasmonic systems. She studied both fundamental effects such as understanding spin-orbit interaction (SOI) of light and polarization dependent beam shifts as well as various practical applications involving biological systems.
At the Soft Matter Lab, she will work on a project related to the realisation of a microscopic heat engine using optical tweezers and noisy electric fields.
Nonadditivity of critical Casimir forces
Paladugu Sathyanarayana, Agnese Callegari, Yazgan Tuna, Lukas Barth, Siegfried Dietrich, Andrea Gambassi & Giovanni Volpe
Nature Communications 7, 11403 (2016)
In soft condensed matter physics, effective interactions often emerge due to the spatial confinement of fluctuating fields. For instance, microscopic particles dissolved in a binary liquid mixture are subject to critical Casimir forces whenever their surfaces confine the thermal fluctuations of the order parameter of the solvent close to its critical demixing point. These forces are theoretically predicted to be nonadditive on the scale set by the bulk correlation length of the fluctuations. Here we provide direct experimental evidence of this fact by reporting the measurement of the associated many-body forces. We consider three colloidal particles in optical traps and observe that the critical Casimir force exerted on one of them by the other two differs from the sum of the forces they exert separately. This three-body effect depends sensitively on the distance from the critical point and on the chemical functionalisation of the colloid surfaces.
Effective drifts in dynamical systems with multiplicative noise: A review of recent progress (Invited review)
Giovanni Volpe & Jan Wehr
Reports on Progress in Physics 79(5), 053901 (2016)
Noisy dynamical models are employed to describe a wide range of phenomena. Since exact modeling of these phenomena requires access to their microscopic dynamics, whose time scales are typically much shorter than the observable time scales, there is often need to resort to effective mathematical models such as stochastic differential equations (SDEs). In particular, here we consider effective SDEs describing the behavior of systems in the limits when natural time scales become very small. In the presence of multiplicative noise (i.e. noise whose intensity depends upon the system’s state), an additional drift term, called noise-induced drift or effective drift, appears. The nature of this noise-induced drift has been recently the subject of a growing number of theoretical and experimental
studies. Here, we provide an extensive review of the state of the art in this eld. After an introduction, we discuss a minimal model of how multiplicative noise affects the evolution of a system. Next, we consider several case studies with a focus on recent experiments: the Brownian motion of a microscopic particle in thermal equilibrium with a heat bath in the presence of a diffusion gradient; the limiting behavior of a system driven by a colored noise modulated by a multiplicative feedback; and the behavior of an autonomous agent subject to sensorial delay in a noisy environment. This allows us to present the experimental results, as well as mathematical methods and numerical techniques, that can be employed to study a wide range of systems. At the end we give an application-oriented overview of future projects involving noise-induced drifts, including both theory and experiment.
Disorder-mediated crowd control in an active matter system
Erçağ Pinçe, Sabareesh K. P. Velu, Agnese Callegari, Parviz Elahi, Sylvain Gigan, Giovanni Volpe & Giorgio Volpe
Nature Communications 7, 10907 (2016)
Living active matter systems such as bacterial colonies, schools of fish and human crowds, display a wealth of emerging collective and dynamic behaviours as a result of far-from- equilibrium interactions. The dynamics of these systems are better understood and controlled considering their interaction with the environment, which for realistic systems is often highly heterogeneous and disordered. Here, we demonstrate that the presence of spatial disorder can alter the long-term dynamics in a colloidal active matter system, making it switch between gathering and dispersal of individuals. At equilibrium, colloidal particles always gather at the bottom of any attractive potential; however, under non-equilibrium driving forces in a bacterial bath, the colloids disperse if disorder is added to the potential. The depth of the local roughness in the environment regulates the transition between gathering and dispersal of individuals in the active matter system, thus inspiring novel routes for controlling emerging behaviours far from equilibrium.
The small-mass limit for Langevin dynamics with unbounded coefficients and positive friction
David P. Herzog, Scott Hottovy & Giovanni Volpe
Journal of Statistical Physics 163(3), 659—673 (2016)
A class of Langevin stochastic differential equations is shown to converge in the small-mass limit under very weak assumptions on the coefficients defining the equation. The convergence result is applied to three physically realizable examples where the coefficients defining the Langevin equation for these examples grow unboundedly either at a boundary, such as a wall, and/or at the point at infinity. This unboundedness violates the assumptions of previous limit theorems in the literature. The main result of this paper proves convergence for such examples.
Engineering sensorial delay to control phototaxis and emergent collective behaviors
Mite Mijalkov, Austin McDaniel, Jan Wehr & Giovanni Volpe
Physical Review X 6(1), 011008 (2016)
Collective motions emerging from the interaction of autonomous mobile individuals play a key role in many phenomena, from the growth of bacterial colonies to the coordination of robotic swarms. For these collective behaviors to take hold, the individuals must be able to emit, sense, and react to signals. When dealing with simple organisms and robots, these signals are necessarily very elementary; e.g., a cell might signal its presence by releasing chemicals and a robot by shining light. An additional challenge arises because the motion of the individuals is often noisy; e.g., the orientation of cells can be altered by Brownian motion and that of robots by an uneven terrain. Therefore, the emphasis is on achieving complex and tunable behaviors from simple autonomous agents communicating with each other in robust ways. Here, we show that the delay between sensing and reacting to a signal can determine the individual and collective long-term behavior of autonomous agents whose motion is intrinsically noisy. We experimentally demonstrate that the collective behavior of a group of phototactic robots capable of emitting a radially decaying light field can be tuned from segregation to aggregation and clustering by controlling the delay with which they change their propulsion speed in response to the light intensity they measure. We track this transition to the underlying dynamics of this system, in particular, to the ratio between the robots’ sensorial delay time and the characteristic time of the robots’ random reorientation. Supported by numerics, we discuss how the same mechanism can be applied to control active agents, e.g., airborne drones, moving in a three-dimensional space. Given the simplicity of this mechanism, the engineering of sensorial delay provides a potentially powerful tool to engineer and dynamically tune the behavior of large ensembles of autonomous mobile agents; furthermore, this mechanism might already be at work within living organisms such as chemotactic cells.