Speaker
Jesús Gómez-Gardeñes

Description Recent studies on critical phenomena in complex networks have proved the emergence of dynamical abrupt transitions in the macroscopic state of the system. These explosive transitions have been shown in the context of network percolation and the emergence of congestion and jamming. Here we demonstrate how an explosive transition shows up in the synchronization of Kuramoto phase oscillators in scale-free networks by incorporating a microscopic correlation between the structural and the dynamical properties of the elements of the system. The characteristics of the explosive synchronization transition are analytically studied in a star graph reproducing the results obtained in synthetic networks. Inspired in these results, we extend the validity of explosive synchronization in networks of chaotic units. Namely, we use both extensive simulations of networks made up of chaotic Rössler units to demonstrate the existence of a first order transition towards synchronization of their phases. In addition, we reproduce experimentally these results by means of electronic circuits arranged in a star configuration. These findings constitute the first prove of this kind of synchronization in practice, thus opening the path to its use in real-world applications.

Speaker
James A. Glazier

Description While modern biology faces a data deluge of molecular and genetic information, our ability to integrate this information to make biomedically meaningful predictions at the organ or organism level is still limited, because of the difficulty of predicting the emergent properties of large ensembles of cells from the cells' molecular signatures. For the past 15 years, we have been developing computational tools and approaches to bridge the gap between molecule and physiological outcome. Our open-source CompuCell3D modeling environment enables rapid Python-scripted specification and refinement of complex biomedical simulations that combine subcellular molecular reaction kinetics models, the physical and mechanical behaviors of cells and the longer range effects of the extracellular environment. Such simulations are much easier to disseminate, support, share, test and reuse than classical low-level-code. I will illustrate two projects using CompuCell3D, one on Choroidal neovascularization (CNV) in Age-Related Macular Degeneration (the most common cause of blindness among the elderly), the second on the factors influencing the rate of progression of solid tumors to metastatic cancers due to evolutionary selection.

Speaker
Shlomo Havlin

Description Network research and percolation theory have been focused on the properties of a single isolated network that does not interact or depends on other networks. In reality, many real-networks interact with other networks. We present a framework for studying percolation of interacting networks. In interdependent networks, when nodes in one network fail, they cause dependent nodes in other networks to also fail. This may happen recursively and can lead to a cascade of failures. In fact, a failure of a very small fraction of nodes in one network may lead to the complete fragmentation of a system of many interdependent networks. I will present exact analytical solutions for the critical threshold and giant component of a network of n interdependent networks. For n=1 we obtain the classical known result for a single isolated network of second order percolation transition. For n>1 cascading failures occur and the transition becomes a first order. Our results for a network of n interdependent networks suggest that the classical percolation theory extensively studied in physics and mathematics in the past 60 years is only a limiting case of n=1 of a more general case of network of networks. As I will show, this general theory has many novel features that are not present in classical percolation theory. For example, analyzing complex systems as a set of interdependent networks may alter a basic assumption that network theory has relied on: while for a single network a broader degree distribution of the network nodes results in the network being more robust to random failures, for interdependent networks, the broader the distribution is, the more vulnerable the networks become to random failure. We also show that reducing the coupling between the networks leads to a change from a first order percolation phase transition to a second order percolation transition at a critical point. References: [1] S. Buldyrev, R. Parshani, G. Paul, H.E. Stanley, S. Havlin, Nature, 465, 0893 (2010) [2] R. Parshani, S. Buldyrev, S. Havlin, PRL, 105, 048701 (2010) [3] R. Parshani, S.V. Buldyrev, S. Havlin, PNAS 108, 1007 (2011) [4] J. Gao, S. Buldyrev, S. Havlin, H. E. Stanley, PRL, 107, 195701 (2011) [5] J. Gao, S. Buldyrev, H. E. Stanley, S. Havlin, Nature Physics, 8, 40 (2012).

Speaker
Georg Basler

Description Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein-protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, including small average path length, large clustering coefficient, heavy-tail degree distribution, and hierarchical organization, viewed as requirements for efficient and robust system architectures. Existing studies estimate the significance of network properties using a generic randomization scheme-a Markov-chain switching algorithm-which generates unrealistic reactions in metabolic networks, as it does not account for the physical principles underlying metabolism. Therefore, it is unclear whether the properties identified with this generic approach are related to the functions of metabolic networks. Within my doctoral thesis, I have developed an algorithm for mass-balanced randomization of metabolic networks, which runs in polynomial time and samples networks almost uniformly at random. The properties of biological systems result from two fundamental origins: ubiquitous physical principles and a complex history of evolutionary pressure. The latter determines the cellular functions and abilities required for an organism's survival. Consequently, the functionally important properties of biological systems result from evolutionary pressure. By employing randomization under physical constraints, the salient structural properties, i.e., the small-world property, degree distributions, and biosynthetic capabilities of six metabolic networks from all kingdoms of life are shown to be independent of physical constraints, and thus likely to be related to evolution and functional organization of metabolism. This stands in stark contrast to the results obtained from the commonly applied switching algorithm. In addition, a novel network property is devised to quantify the importance of reactions by simulating the impact of their knockout. The relevance of the identified reactions is verified by the findings of existing experimental studies demonstrating the severity of the respective knockouts. The results suggest that the novel property may be used to determine the reactions important for viability of organisms. The method is further extended to optimizing metabolic pathways by introducing novel chemically feasibly reactions. The results suggest that, in three organisms of biotechnological importance, introduction of the identified reactions may allow for optimizing their growth. The approach is general and allows identifying chemical reactions which modulate the performance with respect to any given objective function, such as the production of valuable compounds or the targeted suppression of pathway activity. These theoretical developments can find applications in metabolic engineering or disease treatment.

Speaker
Anna Deluca

Description Despite the complexity of the processes relevant for atmospheric convection and precipitation, these have been hypothesized to be a real-world instance of Self-Organized Criticality. Different statistical measures, as a universal power-law in the probability density of rain event sizes or the critical relation found between water vapor and precipitation (tuning and order parameters respectively), have supported this idea. It remains still unclear the possible consequences of this paradigm for the prediction of atmospheric phenomena. By using 1 minute resolution local rain intensities across different climates we investigate which degree of predictability can be attained from the series of rain event sizes. The predictability of extreme events is studied by means of a decision variable sensitive to tendency to cluster between them. The opposite behavior was observed to arise in simple SOC model due to finite-size effects. We will discuss how the time scale separation, between energy input and energy release, can be crucial for resolving these discrepancies. References [1] O. Peters and D. Neelin, Nature Phys. 2, 393-396 (2006) [2] O. Peters, A. Deluca, A. Corral, J. D. Neelin and C. E. Holloway, J. Stat. Mech. P11030 (2010) [3] A. Garber, S. Halleberg and H. Kantz, PRE 80, 026124 (2009)