It is well known that strange attractors are characterised
by their fractal dimensions, which quantify the mass
clustered into a small ball. Recent work, using statistical
approaches, has revealed other generic properties of
The fractal dimensions characterise the dense regions
of the attractor using a power-law, but the distribution of
density in the sparse regions is also characterised by a
power-law, which we term the 'lacunarity exponent'.
The fractal dimension describes the mass of the attractor
contained in small regions, but it is also possible to study the
shape of clusters of points which sample the attractor. The
statistics of the shape of these clusters is characterised by
power laws. The exponents of these lower-laws are found to
exhibit phase transitions.
Physical applications of these phenomena will also
be discussed, including particles advected in fluid flows
and ray trajectories in random media.
We present a method to derive analytically the growths exponents of a surface of 1 + 1 dimensions whose dynamics is ruled by cellular automata. Starting from the automata, we write down the time evolution for the height's average and height's variance (roughness). We discuss the existence of a Probability distribution for the congurations. We apply the method to the etching model[1,2] than we obtain the dynamical exponents, which perfectly match the numerical results obtained from simulations. Those exponents are exact and they are the same as those exhibited by the KPZ model for this dimension. Therefore, it shows that the etching model and KPZ belong to the same universality class. Moreover, we proof that in the continuous limit the majors terms leads to KPZ .
In aggregation, masses coalesce irreversibly to form a cluster, and in frag-
mentation, clusters break up into smaller ones. These processes occur in a
wide range of phenomena varying from tissue development in cell biology
to the formation of planetary rings. Our work is motivated by astro-
physical applications, in particular Saturn rings. A model describing such
systems considers binary collision reactions between masses, and upon
collision, the two clusters can either aggregate or fragment completely
(shatter) into the smallest constituent masses. At the mean-field level,
the time evolution of the cluster sizes is described by the deterministic
We have studied the effects of stochasticity and finite total mass in these
models. In such a setting, the Smoluchowski equation does not give the
correct answer for the mass distribution. We therefore use Monte Carlo
simulation to study the system. The key question that we address is
whether such a system shows a phase transition, and if so, what is the
nature of the phases? There is indeed an active-absorbing phase transition
in the system; in the absorbed phase, all masses aggregate into a single
cluster. The active phase, depending upon the locality of the reaction
Kernel shows temporal intermittency. Details of these aspects will be
given during the talk.
Identifying the drivers of ecosystem and population dynamical behavior is a fundamental aspect of ecology. In a spatially explicit context, the basic ingredients to consider are the spatial structure of the landscape, the local dynamics at play, and the dispersal and diffusion which mediates between the former two. Numerous studies has looked at each of these components separately, but little is known on the interplay between them. Missing has been a more integrative approach, able to map and identify the possible dynamical regimes in the system, and in particular its response to perturbations.
I will focus my talk on a simple, yet relatively general, scenario: the recovery of a homogeneous metapopulation from a single, spatially localized pulse disturbance. We find that the response can take one of three forms, each representing one of three dynamical regimes: Isolated, Interplay and Mixing. Using dimensional analysis we can predict the transition points between these regimes, and how these change with basic system properties such as its total area and the nonlinearities of local dynamics. This enables us to address pertinent issues in ecology, such as habitat fragmentation, synchrony-induced extinctions, and mechanisms of biomass productivity in metacommunities.
I will finish the talk by briefly presenting a few extensions of this work. In particular, a possible indicator of bistability based on the spatial extent of disturbances, the spatial aggregation of disturbances when their frequency is high, and the spatial patterns of ecosystem engineers along an environmental gradient.
The rapid accumulation of genome sequences from diverse organisms presents an opportunity and a challenge for theoretical research: is it possible to derive quantitative laws of genome evolution and an underlying theory? Microbes have small genomes with tightly packed protein-coding genes, and the different functional classes of genes (such as information processing, metabolism, or regulation) show distinct scaling exponents with the genome size. The compactness of microbial genomes is traditionally explained by genome streamlining under selection for high replication rate but so far, there has been no general theoretical model to account for the observed universal laws of genome content scaling. We developed a model for microbial genome evolution within the framework of population genetics and tested it against extensive data from multiple genome comparisons. The analyses indicate that the evolution of genome size is not governed by streamlining but rather, reflects the balance between the benefit of additional genes and the intrinsic preference for DNA deletion over acquisition. These results explain the observation that, contrary to the common belief, microbes with large genomes are subject to stronger selection than small genomes. Employing this model to recover the differential scaling of functional gene classes in bacterial genomes allowed us to identify the underlying factors that govern the evolution of the genome content. A key factor that we termed genome plasticity shapes genome evolution and provides a simple mathematical representation of evolvability, a central but elusive concept in evolutionary biology. These findings demonstrate that key aspects of genome evolution can be captured by general population genetics models, and pave the way for further theoretical analyses of fundamental evolutionary mechanisms.
Particles in biological and soft matter systems undergo Brownian dynamics: their deterministic motion, induced by forces, competes with random diffusion due to thermal noise. More broadly, Brownian dynamics is a generic and simple model for dynamical systems with noise. Provided only with the time-series of positions of such a system, i.e a trajectory in phase space, it is challenging to infer what force field had produced it. At the same time, this is the key information about the dynamical system, which would allow to characterize it completely. I will show that there is an information-theoretic bound on the rate at which information about the force field can be extracted from a trajectory, quantified by a channel capacity. I will discuss the relation between this capacity and the entropy production rate, as defined in stochastic thermodynamics. I will then present a practical method, Stochastic Force Inference, that uses the information contained in a trajectory to approximate force fields. This technique also permits the evaluation of out-of-equilibrium currents and entropy production. It thus makes it possible to quantify subtle time-irreversibility in biological systems at the mesoscale, and opens the door to an understanding of the importance of time- irreversibility.
We show that a non-linear measure of dependence called the codifference is a useful tool in studying ergodicity breaking and non-Gaussianity. Codifference was previously studied mainly in the context of stable and infinitely divisible processes. We extend its range of applicability to random parameter and diffusing diffusivity models which are important in contemporary physics, biology and financial engineering. We prove that the codifference detects forms of dependence and ergodicity breaking which are not visible analysing covariance and correlation. At the same time the differences between the covariance and codifference can be used to analyse non-Gaussianity.
Various animals, mammals in particular, display some form of territorial behaviour for which they make their presence conspicuous to others claiming exclusive ownership of regions of space. The signals employed to perform this form of spatial exclusion may be visual, auditory or olfactory depending on the species and the environment. When the mechanism of territorial exclusion occurs via marks deposited on the terrain (olfactory cues), one talks about stigmergy, a form of environment-mediated interaction often encountered in social insect societies.
To study the emergence of spatial segregation in stigmergic systems I have introduced a new type of collective animal movement model where alignment of the agents does not play any role. It is called the territorial random walker model as agents move freely as random walkers on a lattice, scent-marking the terrain wherever they go. As deposited marks remain active for a finite amount of time, each walker retreats upon encountering an active foreign scent. The emerging spatio-temporal dynamics of the system can be quite rich and can be studied at the meso-scale (the territories) as well as at the micro-scale (the agents).
At the meso-scale short-lived marks produce rapidly morphing and highly mobile territories, while long-lived marks yield slow territories with a narrowly defined shape distribution. More importantly the full dependence in territory mobility as a function of the time for which individual marks remain active is accompanied by a liquid-hexatic-solid transition akin to the Kosterlitz-Thouless melting scenario, apparently the first ecological model to predict such a transition.
The dynamics at the micro-scale is in general non-Markovian, but when population density is sufficiently large some mean-field analytic approaches have proved useful. By considering localized walls to mimic the sharp (retreat) interaction when an animal encounters a foreign scent, it is possible to represent via a Fokker-Planck formalism an animal roaming within neighbouring territorial boundaries. Application of this analytic model to movement data from a red fox population in Bristol, UK, is also shown.
Inspired by the findings on territorial dynamics, it is natural to ask whether it is possible to devise a swarm of independent and decentralised territorial robots. Given that building robots with actual marker reading and writing mechanisms is quite difficult in practice, inspiration comes from the behaviour of territorial birds which detect each other presence at a given location by chirping a challenge which is then countered. Rather than broadcasting a scent signal detectable by any individual passing by, the signalling occurs only between two individuals nearby. While the exclusion mechanism is not stigmergic anymore, it can still be exploited to segregate partially the robot population and limit spatial oversampling in search tasks.
 A. Heiblum-Robles and L. Giuggioli, Phase transitions in stigmergic territorial systems, accepted.
 L. Giuggioli, I. Ayre, A. Heiblum Robles and G.A. Kaminka, From ants to birds: a novel bio-inspired approach to on-line area coverage, in Groß R et al. (eds) Distributed Autonomous Robotic Systems, Springer Proceedings in Advanced Robotics, vol 6, pp. 31-43 (2018).
 L. Giuggioli and V.M. Kenkre, Consequences of animal interactions on their dynamics: emergence of home ranges and territoriality, Move. Ecol. 2(1), 20 (2014).
 L. Giuggioli, J.R. Potts, D.I. Rubenstein and S.A. Levin, Stigmergy, collective actions and animal social spacing, Proc. Natl. Acad. Sci. USA 110(42):16904-9 (2013).
 J.R. Potts, S. Harris and L. Giuggioli, Quantifying behavioral changes in territorial animals caused by sudden population declines, Am. Nat. 182:e73-e82 (2013).
 L. Giuggioli, J.R. Potts and S. Harris, Predicting oscillatory dynamics in the movement of territorial animals, J. Roy. Soc. Interface 9(72):1529-43 (2012).
 J.R. Potts, S. Harris and L. Giuggioli, Territorial dynamics and stable home range formation for central place foragers, PLoS ONE 7(3):e34033 (2012).
 L. Giuggioli, J.R. Potts and S. Harris, Brownian walkers within subdiffusing territorial boundaries, Phys. Rev. E 83:061138/1-11 (2011).
 L. Giuggioli, J.R. Potts and S. Harris, Animal interactions and the emergence of territoriality, PLoS Comp. Biol. 7(3):e1002008/1-9 (2011).
Macromolecular phase separation and droplet formation have long been proposed as key elements in the formation of protocells during the origin of life. A simple model of a protocell consists of a droplet, where droplet material is produced outside the droplet, and chemical reactions inside the droplet play the role of a simple metabolism. Our theoretical study shows that such chemically active droplets can have a flux-driven shape instability that leads to a symmetric droplet division. We analyze the dependence of the instability on the droplet viscosity and parameters that characterize the metabolism and material production. Our work provides a physical mechanism for the division of early protocells before the appearance of membranes.
Energy consuming processes are important in determining the large-scale organization of chromatin. Experiments indicate that chromosomes are organized in a non-random manner and occupy specific regions of a nucleus, called chromosome territories (CTs), with gene rich regions (euchromatin) more centrally positioned than gene-poor (heterochromatin) regions. Further, chromosomes are largely seen to be positioned radially by gene density, although positioning by chromosomes size is also seen. Our model for large-scale nuclear architecture incorporates the effects of non-equilibrium processes driven by the consumption of ATP, associated to cell-type specific transcriptional processes that are inhomogeneous within and across chromosomes. It yields predictions which compare favorably to experimental data including statistics of positional distributions, shapes and overlaps of each chromosome. Our simulation also reproduce common organizing principles underlying large-scale nuclear architecture across interphase human cell nucleus. These include the differential positioning of two X chromosomes in female cells, the territorial organisation of chromosomes including both gene-density-based and size-based chromosome radial positioning schemes, statistics of the shape of chromosomes, and contact probabilities of individual chromosomes. We proposed that biophysical consequences of the distribution of transcriptional activity across chromosomes should be central to any chromosome positioning code.
Entropy and free-energy estimation are key in thermodynamic characterization of simulated systems ranging from spin models through polymers, colloids, protein structure, and drug-design. Current techniques suffer from being model specific, requiring abundant computation resources and simulation at conditions far from the studied realization. In this talk, I will present a novel universal scheme to calculate entropy using lossless compression algorithms and validate it on simulated systems of increasing complexity. Our results show accurate entropy values compared to benchmark calculations while being computationally effective. In molecular-dynamics simulations of protein folding, we exhibit unmatched detection capability of the folded states by measuring previously undetectable entropy fluctuations along the simulation timeline. Such entropy evaluation opens a new window onto the dynamics of complex systems and allows efficient free-energy calculations.
Most populations are spread over spatial ranges that are far larger than individuals typically disperse. How does this affect how quickly they can adapt, and what kinds of patterns of neutral genetic diversity do we expect? We find that spatial structure creates a large gap in adaptibility between populations which are totally asexual and those that occasionally recombine. We also find that adaptation creates a kind of effective long-range dispersal, increases relatedness between spatially distant individuals.
The RT instability, in which a dense fluid invades a less dense fluid under acceleration such as gravity, is pervasive throughout nature. General RT instabilities lie at the heart of myriad applications and diverse phenomena. For example, RT instabilities occur during liquid impact and atomization, the explosion of supernovae, inertial confinement fusion, and in granular media. More prosaically, the RT instability affects resolution control of ink-jet printers and appears when a bottle of vinegar-and-oil salad dressing is turned upside down.
Experimental work on the RT instability in fluids has, until now, been plagued by jitter during acceleration of the tank that contains the fluids. Here I will discuss our development of an alternate method that obviates this problem, viz., magnetic levitation of the dense fluid above the less dense fluid. Using this approach, we have been able to obtain a dispersion relationship for the instability for not only a two fluid / one-interface system, but multiple layers as well. In the latter case, the multiple interfaces are found to couple and modify the dispersion relationship when the intervening fluid layer is sufficiently thin. I will compare our experimental results with long-standing, but until now never tested, theoretical predictions.
Earths jet streams, Jupiters Great Red Spot and its zonal winds are all examples of persistent
large scale ows, whose dynamics is to a good approximation two-dimensional. These ows are
also highly turbulent, and the interaction between the turbulence and these coherent structures
remains poorly understood. Apart from its geophysical relevance, 2D turbulence is a rich and
beautiful fundamental system|where turbulence takes a counter-intuitive role. Indeed, in 2D,
energy is transferred to progressively larger scales, which can terminate in the self organization of
the turbulence into a large scale coherent structure, a so called condensate, on top of small scale
I will describe a recent theoretical framework in which the prole of this coherent mean
can be obtained, along with the mean momentum ux of the uctuations. I will explain how
and when the relation between the two can be deduced from dimensional analysis and symmetry
considerations, and how it can be derived. Finally, I will show that, to leading order, the velocity
two-point correlation function solves a scale invariant advection equation. The solution determines
the average energy of the uctuations, but does not contribute at this order to the momentum
due to parity + time reversal symmetry. Using analytic expressions for the solutions, matched to
data from extensive numerical simulations, it is then possible to determine the main characteristics
of the average energy. This is the rst-ever self-consistent theory of turbulence-ow interaction.
Theoretical models are central to how we think of ecosystems, and yet in many aspects remain poorly understood. We identify a small number of parameters that are sufficient to predict the large-scale properties of a wide variety ecological-community models. These parameters thus play a role similar to temperature and pressure in thermodynamics. We go on to study the generic model that emerges, and describe its phases, including a critical phase where all states are marginally stable.
Different laser cooling mechanisms have been rising important questions from thermodynamics and statistical physics point of view ever since the beginning of this research field (over 40 years ago). Sisyphus cooling is especially well known in this respect providing experimentally accessible regime to study deviations from thermal equilibrium. Here we discuss the Doppler cooling mechanism (the most basic laser cooling mechanism which works even for a simple two-level atom) and show that it also supports deviations from Gaussian statistics for a certain parameters range. We study experimentally the Doppler cooling in Lithium and point out an interesting deviation from the simple, two-level theory, namely cooling at resonance. We develop a realistic theory which accounts for all energy levels of lithium atoms and all laser fields and show its successes and failures.
In the usual setting non-demographic noise, emanating, e.g., from environmental variability, is manifested by time-varying reaction rates. In this work we investigate a different type of non-demographic noise in the form of uncertainty in the reaction step-size, and demonstrate that this type of noise can have a dramatic effect on the stability of self-regulating populations. By employing the usual reaction scheme mA->kA, but allowing, e.g., the product number k to be a-priori unknown and sampled from a given distribution, we show that such non-demographic noise can greatly increase the population's stability compared to the case of fixed k. Our analysis is tested against numerical simulations, and by using empirical data of different species, we argue that certain distributions may be more evolutionary beneficial than others.
A number of complex physical systems will be presented in a unified way and the main idea of the SCE of mimicking the complex system by a simple but arbitrary simple system will be outlined. Two very simple problems will be presented as models for the application of the SCE, showing its obvious superiority over conventional treatments. Results for some of the complex systems including KPZ and noise driven Navier-Stokes will be discussed.
Inter-particle forces in amorphous solids such as glasses, colloids and granular material can be used to study phenomena such as jamming and force-chains. So far, no generally applicable methods exist for measuring the forces between each and every particle in the system. Our recently developed methods aim to x this unfortunate situation in both a-thermal and thermal systems, and produce some interesting insights as to the nature of these forces. In the a-thermal case all that is required for nding the force-law are the xed particle positions and the pressure. The method is shown to accurately recover the force-law in simulation. In the thermal case, we are developing a method to extract an eective potential, using the mean positions. This will allow for analysis of thermal systems using tools hitherto reserved for a-thermal ones, and thereby prediction of thermodynamic properties, study of stability, etc. Quite remarkably we observe the emergence of eective many-body interactions, even when the bare interactions are purely 2-body. This resolves the puzzle posed by recent studies that showed a quantitative match between 2D/3D measurements and the innite dimension mean-eld prediction.
As temperature is lowered, motion becomes more sluggish. Below the glass transition temperature, the dynamics of super-cooled liquids becomes so slow that the system falls out of equilibrium. One hypothesis for this dynamic arrest is that it is due to a thermodynamic phase transition with a diverging length scale. However, there is scant evidence of such a length scale appearing in the structure. Motivated by this, we study another amorphous system that undergoes a phase transition: jammed soft repulsive spheres at zero temperature. We have discovered a subtle correlation length, associated with the local coordination of particles, that is not seen in the two-point correlation function, g(r). We argue that this scale plays an important role in determining the local rigidity of the system, and diverges with an exponent 2/(d+1) as the jamming transition is approached.
Positive feedback in biochemical networks can lead to a bifurcation in state space. Universality implies that if molecules are well mixed, this bifurcation should exhibit the critical scaling behavior of the Ising universality class in the mean-field limit. Making this statement quantitative requires the appropriate mapping between the biochemical parameters and the Ising parameters. Here we derive this mapping rigorously and uniquely for a broad class of stochastic birth-death models with feedback, and show that the expected static and dynamic critical exponents emerge. The generality of the mapping allows us to extract the order parameter, effective temperature, magnetic field, and heat capacity from T cell flow cytometry data without needing to know the underlying molecular details. We find that T cells obey critical scaling relations and exhibit critical slowing down, and that the heat capacity determines molecule number from fluorescence data. We demonstrate that critical scaling holds even as our system is driven out of its steady state, via the Kibble-Zurek mechanism for driven critical systems. Our approach places a ubiquitous biological mechanism into a known class of physical systems and is immediately applicable to other biological data.
In this talk I will present an experimental study of the anomalous dynamics of ultra-cold Rb atoms propagating in a 1D, dissipative, Sisyphus-type optical lattice. We find that the width of the cloud exhibits a power-law time dependence with an exponent that depends on the lattice depth. Moreover, the distribution exhibits fractional self-similarity with the same characteristic exponent. The self-similar shape of the distribution is found to be well fitted by a Lévy distribution. I will further present a measurement of the phase-space density distribution (PSDD) of the cloud of atoms. The PSDD is imaged using a direct tomographic method comprised of velocity selection and spatial imaging. We show that the position-velocity correlation function, obtained from the PSDD, decays asymptotically as a function of time with a power-law that we relate to a simple scaling theory involving the power-law asymptotic dynamics of the position and velocity. The generality of this scaling theory is confirmed using Monte-Carlo simulations of two distinct models of anomalous diffusion dynamics.
Phase transitions are of unfading interest. While classical systems in equilibrium present no phase transitions in 1 dimension, they can be manifested in systems driven out of equilibrium. In this talk we will explore the current fluctuations in boundary driven systems. Current fluctuations are explored by the probability to observe an atypical current over a long period of time. We will show a few examples of phase transitions and classify them. For a special kind of phase transitions, we will show a mapping to a single particle evolving under classical Lagrangian mechanics. This mapping provides us with a simple picture of where one could expect such transitions.
What is the fate of a forager that depletes its environment as it wanders? We investigate this question within the "starving" random walk model, in which the forager starves when it travels S steps without eating. The forager consumes food whenever it is found and becomes fully sated. However, when the forager lands on an empty site, it moves one time unit closer to starvation. We determine the forager lifetime, analytically in one dimension and numerically in higher dimensions. In two dimensions, long-lived walks explore a highly ramified region so as to remain close to food.
We also investigate the role of greed, in which the forager preferentially moves towards food when faced with a choice of hopping to food or to an empty site. Paradoxically, the forager lifetime can have a non-monotonic dependence on greed, with a different sense to the non-monotonicity in one and in two dimensions.
Small-amplitude fast vibrations and small surface micropatterns affect properties of various systems involving wetting, such as superhydrophobic surfaces and membranes. The mathematical method of averaging the effect of small fast vibrations is known as the method of separation of motions. The vibrations are substituted by effective force or energy terms, leading to vibration-induced phase control. The best known example of that is the stabilizationb of an inverted pendulum on a vibrating foundation (the Kapitza pendulum); however, the method can be applied to a number of various situations including wetting. A similar averaging method can be applied to surface micropatterns leading to surface texture-induced phase control. We argue that the method provides a framework that allows studying such effects typical to biomimetic surfaces, such as superhydrophobicity, membrane penetration and others. Patterns and vibration can effectively jam holes and pores in vessels with liquid, separate multi-phase flow, change membrane properties, result in propulsion, and lead to many other multiscale, non-linear effects. These effects can be used to develop novel materials.
In a liquid all the particles are mobile, while in a glass only some of them are mobile at any given time. Although overall the structure is amorphous in both cases, the difference is that in glasses there are local structures that inhibit the movement of particles inside them. We investigate these structures by considering the minimum number of particles that need to move before a specific particle can move. By mapping the dynamics of the particles to diffusion of mobile vacancies, we find a general algebraic relation between the mean size of the structures and the mean persistence time, which is the time until a particle moves for the first time. The exponent relating these two quantities depends on the system's properties.
We investigated this relation analytically and numerically in several kinetically-constrained models: the Fredrickson-Andersen, Kob-Andersen and Spiral models. These models are either lattice gas models or Ising-like models, in which a particle can move or a spin can flip only if the local environment satisfies some model-dependent rule. Due the discrete nature of these models and relative simplicity, we were able to analytically find the relation between the structure and the dynamics and found an excellent agreement between our analytical results, our numerical simulations, and the heuristic arguments presented above. In these simple models, the minimum number of particles that need to move before a specific particle can move is easily found by using a culling algorithm, also called bootstrap or threshold percolation.
The recently formulated macroscopic fluctuation theory is a successful description of out of equilibrium diffusive systems. I will focus on current fluctuations of boundary driven systems within the macroscopic theory description, and discuss the relevance of the additivity principle to derive the large deviation function associated with the current fluctuations. Three results will be shown
1) Current fluctuations in boundary driven systems are universal
2) A criterion for the validity of the additivity principle and application to dynamical phase transitions.
3) Relevance of the macroscopic fluctuation theory to transport of disordered quantum systems.
Collective movement patterns appear at all scales from microorganisms to invertebrate and vertebrate animals. In certain cases the individual entities communicate indirectly modifying the environment in which they roam by leaving a trace of their action or passage. This form of interaction has a long tradition in the ecological literature and is called stigmergy. In the context of territorial mammals modification of the environment occurs because of scent deposition and is being exploited to maintain exclusive ownership of certain region of space. By introducing the so-called territorial random walkers, it is possible to study the formation of territorial patterns by modelling the movement and interaction of scent-depositing animals. Territorial random walkers consist of agents that move at random and deposit scent, that is mark the locations they visit using temporal flags that decay over a finite amount of time, and retreat upon encountering a foreign scent. Depending solely on the ratio between the time for which the mark is active and the time it takes for the walker to cover its own territory, the system displays different patterns. Short lived marks produce rapidly morphing, fast traveling territories. A broad range of shapes and territory sizes are observed, and these territories may display ergodic trajectories. Marks that remain active for long times yield slowly moving territories that resemble glassy systems. In such state territories are effectively confined in space and have a more homogeneous shape distribution. I will show how these different regimes emerge based on the population density and the length of time for which marks remain active. I will also present an adiabatic mean-field approximation that allows to describe at short times the dynamics of the walker and that of the territory boundaries through a Fokker-Planck formalism.
Regime shifts in ecosystems are typically understood to be abrupt global transitions from one stable state to an alternative stable state, induced by slow environmental changes or global disturbances. However, spatially extended ecosystems often exhibit patterned states, which allows for more complex dynamics to take place. A bistability of a patterned state and a uniform state can lead to a multitude of stable hybrid states, with small domains of one state embedded in the other state. The response of the system to local disturbances or change in global parameters in these systems can lead to gradual regime shifts, involving the expansion of alternative-state domains by front propagation, rather than a global collapse. Moreover, a regime of periodic perturbations can give rise to step-like gradual shifts with extended pauses at these states. The implications of these scenarios to regime shifts in dryland vegetation will be discussed, focusing on the case of fairy circles in Namibia as a concrete example.
Atomic scale matter, like a particle with spin, can respond to external perturbations in a chiral way: the spin rotates in response to a magnetic field. That is, a vector perturbation gives rise to an angular velocity. The technology of pulsed nuclear magnetic resonance exploits this response to organize and manipulate a sample of initially disordered spins. In this talk we explore the analogs of this principle in the world of colloidal matter—micron-scale solid bodies of irregular shape. Such bodies can respond chirally to external forcing via their hydrodynamic coupling. This chiral response is richer than that of a nuclear spin. As with nuclear spin, this response gives a handle that can bring a randomly-oriented dispersion of colloidal objects into a common orientation. The alignment can be created by phase locking, analogous to pulsed nuclear magnetic resonance. It can also be created by random external perturbations. Here the alignment principle is the phenomenon of “noise-induced synchronization” known in dynamical systems.
Both thermal fluctuations and material inhomogeneity/disorder play a major role in many branches of science. This talk will focus on various aspects of the interplay between the two. First, we consider the spatial distribution of thermal fluctuational energy and derive universal bounds for internal-stress-free systems. In addition, we show that in 1D systems the thermal energy is equally partitioned even among coupled degrees of freedom. Applications to severing of actin filaments and protein unfolding are discussed. Then, we consider fluctuations in residually-stressed systems and their coupling to anharmonicity. In the context of glassy systems, we show that thermal energy van be spatially localized and suggest that it might serve as a useful structural diagnostic tool, e.g. for identifying glassy lengthscales and precursors to plastic events under driving forces. Lastly, we consider the continuum approach (Statistical Field Theory) to analyzing fluctuations in inhomogeneous systems, and demonstrate fundamental discrepancies between the continuum and the discrete theories in explicit calculations of some, but not all, fluctuation-induced (Casimir-like) forces.
In 1913 Michaelis & Menten published a seminal paper in which they presented a mathematical model of an enzymatic reaction and demonstrated how it can be utilized for the analysis and interpretation of kinetic data. More than a century later, the work of Michaelis & Menten is considered classic textbook material, and their reaction scheme is widely applied both in and out of its original context. At its very core, the scheme can be seen as one which describes a generic first passage time process that has further become subject to stochastic restart. This context free standpoint is not the standard one but I will explain how it has recently allowed us to treat a wide array of seemingly unrelated processes on equal footing, and how this treatment has unified, altered, and deepened our view on single-molecule enzymology, kinetic proof-reading and complex search processes. Newly opened opportunities for theoretical and experimental research will also be discussed.
Our body is colonized by trillions of microbes, known as the human microbiome,
living with us in a complex ecological system. Those micro-organisms play a crucial
role in determining our health and well-being, and there are ongoing efforts to
develop tools and strategies to control these ecosystems.
In this talk I address a simple but fundamental question: are the microbial ecosystems
in different people governed by the same host-independent ecological
principles, represented by a characteristic (i.e. “universal”) mathematical model?
Answering this question determines the feasibility of general therapies and
control strategies for the human microbiome.
I will introduce our novel methodology that distinguishes between two scenarios: host-independent
and host-specific underlying dynamics. This methodology has been applied to study
different body sites across healthy subjects. The results can
fundamentally improve our understanding of forces and processes shaping human microbial
ecosystems, paving the way to design general microbiome-based therapies.
Brownian motion with time-dependent diffusion coefficient is ubiquitous in nature. It has been observed for the mobility of proteins in cell membranes, motion of molecules in porous environment, water diffusion in brain measured in terms of magnetic resonance imaging and also in media with time-dependent temperature such as free cooling granular materials or melting snow.
We investigate a new type of anomalous diffusion processes governed by an underdamped Langevin equation with time-dependent diffusion and friction coefficients and discuss possible applications to real physical systems such as free cooling granular materials. We show that for certain range of parameter values the overdamped limit for the Langevin equation does not exist.
We study the random bond lattice model in which the strength
of the bond between any two neighboring particles is randomly chosen such
that each of N particles is characterized by N
We investigate the Brownian motion of boomerang colloidal particles confined between two glass plates. Our experimental observations show that the mean displacements are biased towards the center of hydrodynamic stress (CoH), and that the mean-square displacements exhibit a crossover from short-time faster to long-time slower diffusion with the short-time diffusion coefficients dependent on the points used for tracking. A model based on Langevin theory elucidates that these behaviors are ascribed to the superposition of two diffusive modes: the ellipsoidal motion of the CoH and the rotational motion of the tracking point with respect to the CoH.
For over 90 years there has been an unexplained puzzle associated with the viscosity of dilute aqueous salt solutions. More specifically, there is a contribution to the viscosity which is linear in the salt concentration and very ion specific for monovalent salts. This is usually discussed in terms of the Jones-Dole coefficient (B) which is the amplitude of the linear term in the concentration. The A coefficient was derived by Falkenhagen and Onsager many years ago in terms of the Debye-Hückel theory for electrolytes. We shall discuss our current understanding of the Jones-Dole coefficient in the context of ionic hydration.
Mechanics of cancer cells are directly linked to their metastatic potential (MP), or ability to produce a secondary tumor at a distant site. Metastatic cells can squeeze through blood vessel walls and tissue. Such considerable structural changes rely on rapid remodeling of internal cell structure and mechanics. We perform a comparative study, using particle-tracking to evaluate the intracellular mechanics of living epithelial breast cells with varying invasiveness. Probe-particle transport differs between the cell types, likely relating to their cytoskeleton network-structure and underlying transport. The basic analysis included evaluation of the time-dependent mean square displacement (MSD), the second power of the displacement. Particles in all the evaluated cell lines exhibit anomalous super-diffusion with an MSD scaling exponent of 1.4, at short lag times below 1 second. While indicating active transport within the cells, the MSD alone cannot reveal the underlying mechanisms. Hence, we analyze particle motion through a combination the MSD, other powers of the displacement, and various trajectory and displacement analysis procedures to identify structural and dynamic changes associated with metastatic capabilities of cells.
The dynamic cytoskeleton and especially the molecular motors acting on it provide the cell with its remodeling capabilities and allow active transport within the cell. While active transport in living cells has been well-documented, the underlying mechanisms have not been determined. Here, we systematically target the cytoskeleton, molecular motors, and ATP energy related processes to determine their roles in particle transport. Our results show that particle motion is likely driven by different processes in each cell type. Intracellular transport in high MP cells is suggested to originate from fluctuations of microtubule filaments as well as from direct and indirect interactions between particles and microtubule-associated molecular motors. In the low MP cells we suggest that motion results from direct and indirect interactions between particles and microtubule-associated molecular motors, being transported by them or nudged by passing motors, respectively. The benign cells, however, reveal significant involvement of the acto-myosin network, where particle motion was related to network contractions. Thus, we are able to provide insight into dynamic intracellular structure and mechanics that can support the unique function and invasive capabilities of highly metastatic cells.
We discuss models of anomalous diffusion (mostly subdiffusion) in complex systems on the different levels of description and try to classify different types of the behavior. We moreover introduce statistical tests which allow to distinguish between different classes of such models and between different models within the class on the level of ensembles of the trajectories and of the single trajectories of the corresponding processes.
Inspired by biological systems in which thousands of different types of proteins interact within a cell, we use molecular dynamics simulations in 2d to study multi-component systems in the large number of species limit, i.e., all particles differ from each other (APD systems). All the particles are assumed to be of the same size and interact via the Lennard-Jones (LJ) potential, but their pair interaction parameters are generated at random from a uniform or a peaked distribution. We analyze both the global and the local properties of these systems at temperatures above the freezing transition and find that APD fluids relax into a self-organized state characterized by clustering of particles according to the values of their pair interaction parameters.
I discuss the occupation time statistics in thermal, ergodic continuous-time random walks. While the average occupation time is given by the canonical Boltzmann-Gibbs law from Statistical Physics, the finite-time fluctuations around this mean turn out to be large and nontrivial. They exhibit dual time scaling and distribution laws: the infinite density of large fluctuations complements the Lévy-stable density of bulk fluctuations.
Neither of the two should be interpreted as a stand-alone limiting law, as each has its own deficiency: the infinite density has an infinite norm (despite particle conservation), while the stable distribution has an infinite variance (although occupation times are bounded). These unphysical divergences are remedied by consistent use and interpretation of both formulas.
Interestingly, while the system's canonical equilibrium laws naturally determine the mean occupation time of the ergodic motion, they also control the infinite and stable densities of fluctuations.
We study the sequence of particles' collisions in a small system of hard balls. We demonstrate that ergodicity implies quite unusual phenomenon. The particles have preferences over long time intervals during which the particle consistently collides more with certain particles and less with others. Things look like there is effective interaction between the particles. Though the preferences change sooner or later the average waiting time to the change is infinite. The results hold for dilute gas with arbitrary short-range interactions and dense fluids of hard balls.