Speaker
Andrea Scagliarini

Description Turbulence is the state characterizing the highly chaotic and complex motion of fluids in a huge variety of circumstances, spanning a strikingly large range of spatial and temporal scales (from arterial blood flow to hurricanes and Supernovae explosions). One of the most intriguing features is its capability to enhance the rate of dispersion of materials suspended in the fluid (e.g. pollutants in the atmosphere) and the efficiency of fluid mixing. After an introduction of the general problem, some recent theoretical and numerical results on the transport of inertial particles by turbulent flows and on turbulent mixing of miscible fluids, triggerd by a Rayleigh-Taylor instability, will be presented. I will show how particles with inertia tend to distribute inhomogeneously throughout the space and to form fractal-like aggregates. This mechanism of "preferential concentration" plays a crucial role in the way particles (emitted, for example, by a source at a point in space) separate at large times. I will then discuss Rayleigh-Taylor instability under strong stratification (a situation of great relevance in geophysical and astrophysical contexts), whose main effect is to arrest the mixing process leading to the formation of a confined convective region with a very complex boundary layer dynamics.

Speaker
Henrik Jensen

Description Understanding systems level behaviour of many complex interacting agents is very challenging for various reasons: the interacting components can lead to hierarchical structures with different causations at different levels. We use the Tangled Nature model to discuss the co-evolutionary aspects connecting the microscopic level of the individual to the macroscopic systems level. At the microscopic level the individual agent may undergo evolutionary changes due to “mutations of strategies”. The micro-dynamics always run at a constant rate. Nevertheless, the systems level dynamics exhibit a completely different type of mode characterised by intermittent abrupt dynamics where major upheavals keep throwing the system between meta-stable configurations. These dramatic transitions are described by a log-Poisson time statistics. The long time effect is a collectively adapted network. We discuss how the systems level adaptive intermittent search is related to an increase in the mutual information content describing the core of the population, while, at the same time, the adaptive search is conducted through an overall network of agents described by a decreasing degree of correlation measured in terms of mutual information. We further more relate the systems level adaptation to the functional properties of the microscopic duplication probability.

Speaker
M. Angeles Serrano

Description Network reconstructions at the cell level are a major development in Systems Biology. However, we are far from fully exploiting its potentialities. Often, the incremental complexity of the pursued systems overrides experimental capabilities, or increasingly sophisticated protocols are underutilized to merely refine confidence levels of already established interactions. For metabolic networks, the currently employed confidence scoring system rates reactions discretely according to nested categories of experimental evidence or model-based likelihood. Here, we propose a complementary network-based scoring system that exploits the statistical regularities of a metabolic network as a bipartite graph. As an illustration, we apply it to the metabolism of Escherichia coli. The model is adjusted to the observations to derive connection probabilities between individual metabolite-reaction pairs and, after validation, to assess the reliability of each reaction in probabilistic terms. This network-based scoring system uncovers very specific reactions that could be functionally or evolutionary important, identifies prominent experimental targets, and enables further confirmation of modeling results. We foresee a wide range of potential applications at different sub-cellular or supra-cellular levels of biological interactions given the natural bipartivity of many biological networks.

Speaker
Marian Boguna

Description In the age of Information Technology, the Internet has become our primary communication system. It is estimated that more than a billion users surf every day the web looking for information, sharing files, or developing new applications. One of the most surprising facts about the Internet is that, despite some preconceived ideas, its complex architecture is the result of a self-organized process where individual agents (Internet Service Providers or ISPs) interact locally without any central authority controlling its evolution. This turns the Internet into subject of truly scientific research. The Internet is now facing a serious scalability problem with its routing architecture. To route information packets to a given destination, Internet routers must communicate to maintain a coherent view of the global Internet topology. The constantly increasing size and dynamics of the Internet thus leads to immense and quickly growing communication and information processing overhead, a major bottleneck in routing scalability causing concerns among Internet experts that the existing Internet routing architecture may not sustain even another decade. In our approach, we assume that the Internet (and other complex networks) lives in a hidden metric space that shapes its topology. Discovery of this hidden metric space can then be used to greedily route information without detailed global knowledge of the network structure or organization. Following these ideas, we have introduced a network model that combines the small-world effect, scale-free degree distributions, and high clustering coefficient with metric properties. This model nicely reproduces the main topological properties of the Internet graph and other complex networks. By using it, we have shown that the metric and topologic requirements for a network to be (efficiently) navigable are met by the majority of real networks. We have also provided empirical evidence that the Internet and some social networks can be embedded in metric spaces, which justify our pursuit of metric properties in complex networks. We have also shown that if we are to associate a metric space to real networks like the Internet, this space must be negatively curved (hyperbolic) and that in this geometry, greedy routing strategies achieves the optimal performance.

Speaker
David A. Weitz

Description This talk will discuss some of the new opportunities that arise by precisely controlling droplets using microfluidic devices. I will show how the exquisite control afforded by the microfluidic devices provides the enabling technology to use droplets as nanoreactors to qualitatively increase the rate of combinatorial screening of chemical reactions, leading to important new applications in biotechnology. In addition, I will show how droplets can be used as templates to create sophisticated new structures that may find uses in applications such as drug delivery.