Speaker
Carlos Lugo

Description In this talk, a number of phenomena observed and measured in cardiac cells with and without the presence of an external stimulus are introduced along with the common ideas employed to model and study the genesis and possible consequences of a number these behaviours at the cardiomiocyte and tissue levels. Events such as spontaneous release, spark and wave formation observed at the single cell level, require an approach which considers the cell as a system composed by a large number of more fundamental sub-units often called Calcium Release Units (CRU's), which populate the cell's cytoplasm and are spatially distibuted in a non-homogeneous fashion along the cell, and are charactherised by a discrete set of possible “states” which govern the occurence of Calcium-release events and are in general of a stochastic nature. I will also discuss a model over a larger scale, which intend to give account of the genesis and formation of cardiac arrhythmias, spiral waves and other instabilities. This requires a modelling strategy which considers the miocytes as the fundamental building units. Thus taking the cardiac tissue as a system composed of many cells coupled by “junctions”. Both approaches and results are briefly presented along with the current state of the collaboration with the findings of the experimental team at Sant Pau.

Speaker
Alex Arenas

Description Modular structure is ubiquitous in real-world complex networks, and its detection is important because it gives insights into the structure–functionality relationship. The standard approach is based on the optimization of a quality function, modularity, which is a relative quality measure for the partition of a network into modules. Recently, some authors have pointed out that the optimization of modularity has a fundamental drawback: the existence of a resolution limit beyond which no modular structure can be detected even though these modules might have their own entity. The reason is that several topological descriptions of the network coexist at different scales, which is, in general, a fingerprint of complex systems. Here, we propose a method that allows for multiple resolution screening of the modular structure. The method has been validated using synthetic networks, discovering the predefined structures at all scales. Its application to two real social networks allows us to find the exact splits reported in the literature, as well as the substructure beyond the actual split. We also investigate the adaptation and performance of modularity-based algorithms, designed in the scope of complex networks, to analyze the mesoscopic structure of correlation matrices. Using a multi-resolution analysis we are able to describe the structure of the data in terms of clusters at different topological levels. We demonstrate the applicability of our findings in two different scenarios: to analyze the neural connectivity of the nematode Caenorhabditis elegans , and to automatically classify a typical benchmark of unsupervised clustering, the Iris data set, with considerable success.

Speaker
Tsvi Tlusty

Description All organisms rely on noisy molecular recognition to convey, process and store information. This stochastic biophysical setting poses a tough challenge: how to construct information processing systems that are efficient and economical yet error-resilient? I will review recent results that reveal generic design principles of molecular information systems. This biological design problem turns out to be equivalent to the statistical physics of stochastic maps and optimization processes. The examples considered range from molecular codes through molecular recognition and homologous recombination (a crucial mechanism of sexual reproduction that yields genetic diversity) to the spatial organization of chromosomes in the cell nucleus.

Speaker
J. Doyne Farmer

Description Phenomena as diverse as breeding bird populations, the size of U.S. firms, money invested in mutual funds, and the scientific output of universities all show unusual but remarkably similar growth fluctuations. The fluctuations display characteristic features, including heavy tails and anomalous power law scaling of the standard deviation as a function of size. Many theories have now been put forward to explain this, all of them based on modifications and extensions of proportional growth of subunits. We analyze data from bird populations, firms, and mutual funds and show that the growth fluctuations match a Levy distribution very well. This was previously suggested by Wyart and Bouchaud and Gabaix, but until now never tested. However, we show that their theory (and indeed all previous theories) are ruled out, at least for these three data sets, because they require size distributions that are too heavy tailed. We introduce a simple additive replication model, in which groups (such as firms) grow by replacing each of their members by a random number of new members. To demonstrate how the individual growth fluctuations can be heavy-tailed even though the sizes are not, we propose a model based on stochastic influence dynamics over a scale-free contact network, and show that it produces the correct behavior. We generalize the model to the case where some groups are preferred over others, and show that this can lead to a breakdown of the anomalous scaling, which appears to be observed for some other data sets.

Speaker
J. Doyne Farmer

Description There are a large number of theories for what caused the economic crisis. How is it that we can have such wide disagreement on something with such enormous economic consequences for the world? I will argue that this has to do with fundamental problems in the discipline of economics, dating back to fundamental decisions that were made by the leaders of the economics establishment in the late 1970's. I will lay out an alternative vision for economics, arguing that we need to model the economy as a complex system in order to take proper advantage of modern capabilities for data collection and computer simulation.