Ulrik Brandes
ETH Zürich, Switzerland
Networks FC – Networks, Football & Complexity
I argue that association football (soccer) is a model system for data science research in social science domains. The game itself has been described as an open complex system by scholars from various backgrounds, and it is interfacing with other systems comprised of
organizations, markets, and audiences. The challenges encountered when studying collective behavior in a highly regulated, spatially and temporally constrained setting make for a humbling experience for anyone attempting to understand even more complex social systems. By way of example, I report on some recent work using network models to study football tactics.
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Caterina A. M. La Porta
University of Milan, Italy
Tackling complexity in cancer
The vast majority of cancer deaths are not due to the growth of the primary tumor but to the spread of metastasis. The distribution patterns of cancer metastasis depend on a sequence of steps involving on the one hand changes in gene regulation at the level of the cell and on the other hand on geometrical and mechanical systemic effects related to blood circulation. In this talk, I will disentangle the two issues by first showing simulations cancer cell trajectories in a high-resolution humanoid model of global blood circulation and comparing the results with the location of metastasis recorded in thousands of human autopsies for seven different solid tumors. I will then address gene regulation, considering the transition between epithelial and mesenchymal states, which is known to play a fundamental role in cancer progression. Using a combination of numerical simulations of a Boolean network model and the analysis of bulk and single-cell gene expression data, we constructed a topographic map underlying epithelial–mesenchymal transitions. The map reveals a multitude of metastable hybrid phenotypic states, separating stable epithelial and mesenchymal states and provides a general strategy to construct a topographic representation of phenotypic plasticity from gene expression data using statistical physics methods. The method is at the basis of ARIADNE, an algorithmic strategy to assess the risk of metastasis from transcriptomic data of patients with triple-negative breast cancer, a subtype of breast cancer with poorer prognosis with respect to the other subtypes.
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