Speaker
Daniel Rodríguez Amor (MIT)

Description

Microbiomes are complex, dynamic communities intimately related to their environments. Despite the increasing efforts at developing a comprehensive ecological understanding of these microbial ecosystems, we are still far from a predictive theory to routinely engineer their community dynamics in order to promote health or fight environmental deterioration. Here, we study, both theoretically and experimentally, the dynamics between steady states of two different microbial ecosystems. The first model system permitted us to analyze how environmental resources shape the spatial dynamics of bacterial population fronts. Interestingly, we show that improved environments can slow down range expansions of mutualistic bacterial strains. In the second model, we explore transitions between population stable states induced by short-term perturbations. On the one hand, we show that environmental perturbations (such as antibiotic shocks) can induce shifts between stable states that can be predicted by analyzing the interactions between bacteria and their environment. On the other hand, we propose that transient invaders (alien species that interact with the community for shorts periods of time) can be a common cause of shifts between microbial stable states.

Speaker
Andreas Meyerhans (Universitat Pompeu Fabra)

Description

The dynamic interplay between an expanding virus and the concomitantly activated host immune response during the primary infection phase is critical for establishing a chronic infection. However the key sensors and regulatory mechanisms that ultimately move the virus-host dynamics towards virus persistence and immune system exhaustion are still poorly understood.  Our objective is the identification of key elements during the establishment of viral persistence using the Lymphocytic Choriomeningitis Virus (LCMV) mouse model system.

Our experiments revealed modules of highly connected genes (hub genes) that represent the main biological pathways involved in acute versus persistent infection outcomes. Module comparisons from both infection outcomes showed a positive correlation between module size and preservation, indicating that few genes are involved in outcome-specific pathways. A chronic infection-specific module suggested an important biological role for the chemokine Xcl1 and responsive Xcr1+DC. Depletion of this cell population resulted in reduction of LCMV-specific CD8 T cells, increased virus loads and death. Virus-specific CD8 T cell exhaustion during persistent LCMV infection is followed by an increase of cross-presenting Xcr1+DC in spleen that maintain a low level of cytotoxic effector cells and control virus loads to non-pathogenic levels. Immunotherapeutic strategies to boost Xcr1+DC-dependent T cell responses may present a mean to better control virus loads in persistent virus infections.

Speaker
Helmut G. Katzgraber (Texas A&M University , 1QBit Information Technologies, Santa Fe Institute)

Description

Can quantum computers meet the tantalizing promise of solving complex calculations---such as optimization problems or database queries---faster than classical computers based on transistor technologies? Although IBM recently opened up their five-qubit programmable quantum computer to the public to tinker with, the holy grail of a useful large-scale programmable universal quantum computer is decades away. While working mid-scale programmable special-purpose quantum optimization machines exist, a conclusive detection of quantum speedup remains controversial despite the recent promising results. In this talk a head-to-head comparison between quantum and classical optimization approaches is given.  Current quantum annealing technologies must outperform classical devices to claim the crown in the race for quantum speedup.

Speaker
Rodrigo Ledesma-Aguilar (Smart Materials and Surfaces Laboratory, Northumbria University)

Description

Moving or deforming a liquid droplet in contact with a solid surface often involves an energy change. This is because a change in configuration is normally linked to a displacement in the local capillary energy landscape. The displacement can be static or dynamic, and will often be opposed by dissipative forces, such as contact angle hysteresis or viscous friction.

In this talk we explain recent theoretical and experimental results that go beyond this conception and explore the concept energy invariance in capillary systems. By identifying the underlying symmetries of the equilibrium configuration of a liquid droplet in contact with solid boundaries, we construct energy-invariant paths upon a reconfiguration of the solid boundaries. Such paths can lead to deformations and translations of the droplet whilst keeping a net zero change in the free energy of the system. Therefore, any energy losses upon actuation stem from out of equilibrium processes. Experimentally, we illustrate our ideas by manipulating a liquid barrel (a droplet of positive mean curvature) in a low-pinning, low-friction wedge geometry formed by lubricant-impregnated surfaces, where we quantify the relatively small dissipation caused by a departure from the energy-invariant equilibria.

Speaker
Daniele Avitabile (School of Mathematical Sciences of the University of Nottingham)

Description

I will discuss level-set based approaches to study the existence and bifurcation structure of spatio-temporal patterns in biological neural networks. Using this framework, which extends previous ideas in the study of neural field models, we study the first example of canards in an infinite-dimensional dynamical system, and perform a computational reduction of dimensionality in certain neural network models.

Phenomenological neural field models have been intensively studied in the past and are known to support a variety of coherent structures observed experimentally (localised bumps of activity, travelling fronts, travelling bumps, lurching waves, rotating waves). These models are typically written as integro-differential equations, where the integral term is a Hammerstein nonlinear operator, featuring a sigmoidal firing rate and a synaptic kernel. Successful strategies for the analysis of these models include special choices of the synaptic kernels (leading to equivalent PDE formulations) and interface methods.

The main message of the talk is that the latter can be used effectively to construct or compute coherent structures in multiple-scale, heterogeneous, and possibly stochastic systems. I will initially consider a spatially-extended network with heterogeneous synaptic kernel. Interfacial methods allow for the explicit construction of a bifurcation equation for localised steady states, so that analytical, closed-form expressions for a classical "snakes and ladders” bifurcation scenario can be derived.

When the model is subject to slow variations in the control parameters, a new type of coherent structure emerges: the structure displays a spatially-localised pattern, undergoing a slow-fast modulation at the core. Using interfacial dynamics and geometric singular perturbation theory, we show that these patterns follow an invariant repelling slow manifold, hence we name them "spatio-temporal canards". We classify spatio-temporal canards and give conditions for the existence of folded-saddle and folded-node canards. We also find that these structures are robust to changes in the synaptic connectivity and firing rate. The theory correctly predicts the existence of spatio-temporal canards with octahedral symmetries in a neural field model posed on a spherical domain.

I will then discuss how the insight gained with interfacial dynamics may be used to perform coarse-grained bifurcation analysis on neural networks, even in models where the network does not evolve according to an integro-differential equation. Time permitting, two illustrative examples will be discussed.

The first example is a well-known event-driven network of spiking neurons, proposed by Laing and Chow. In this setting, we construct numerically travelling waves whose profiles possess an arbitrary number of spikes. An open question is the origin of the travelling waves, which have been conjectured to form via a destabilisation of a bump solution. We provide numerical evidence that this mechanism is not in place, by showing that disconnected branches of travelling waves with countably many spikes exist, and terminate at grazing points; the grazing points correspond to travelling waves with an increasing number of spikes, a well-defined width, and decreasing propagation speed.

The second example is a heterogeneous neural network written as a discrete Markov chain with discrete ternary state space, posed on a lattice. The model supports coarse bumps, multi-bumps and travelling waves, but the derivation of a coarse evolution equation is nontrivial. I will show that, by choosing the interfaces as coarse variables, it is possible to perform an efficient numerical coarse-graining, following the pattern in parameter space and analysing their stability.