Events
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Speaker
Gorka Zamora-López (Center for Brain and Cognition, UPF)Description
Discovered in the realm of social sciences the small-world phenomenon stands for the observation that any two individuals are connected by a short chain of social ties. Since then, most real networks studied have been found to be small-world as well. Despite its significance to understand empirical networks, a quantitative determination of "how short" or "how long" a network is, and how it compares to others has remained unresolved over the years. When we say that “a complex network is small-world” we mean, roughly speaking, that its average path-length is much smaller than the number of nodes, without giving further precise measurement. The usual strategy to deal with this problem has been to compare networks to well-known graph models, e.g., random graphs and regular lattices. While these represent interesting null-hypotheses, useful to answer particular questions about the data, they do not constitute absolute or universal references. Here, we establish a reference framework under which the length and efficiency of networks can be interpreted and compared. Therefore, we will evaluate how these properties deviate from the smallest and the largest values they could possibly take. We have found that these limits are given by families of singular configurations which we will refer as ultra-short and ultra-long networks. We show that typical models (random, scale-free and ring networks) undergo a transition as their density increases, all becoming ultra-short at sufficient density. The convergence rate, however, differs for each model. Then, we study a sample set of well-known empirical networks (neural, social and transportation). While most of these display path-lengths close to random graphs, when contrasted against the absolute boundaries, only the cortical connectomes reveal quasi-optimal.
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Description
Research Workshop celebrating the scientific career of Prof. José María Sancho
Organizers
UB -
Organizers
UPF -
Speaker
James Sharpe (EMBL, Barcelona)Description
Dramatic progress has been made over the last 2 decades in how we access key types of biological data - in particular sequence-based data on genomic information. However, integrating this data to produce dynamic and predictive models of higher level biological phenomena (e.g. development, regeneration, homeostasis and cancer) has been limited. Questions about tissues and organs are still most often tackled at the molecular or cellular level. We tend to ask how individual progenitor cells respond to signals from their “environment”, and thus to focus on signal transduction pathways, gene regulatory events, and epigenetic memory. But an organ is more than just an environment for cells to "act" in – it is an integrated whole, a coherent community, with cells in constant genetic, chemical and mechanical communication with each other. New technical advances such as organoid culture, 3D mesoscopic imaging, multicellular omics and computer modeling are helping us to go beyond the molecular and cellular level, to understand multicellular feedback loops, long-range signalling networks and emergent collective decisions, and thus to see tissues and organs as systems in their own right. Modelling these higher-level processes in vitro and in silico will help us understand these complex processes at a deeper level, and I will discuss our own attempts in this direction, to understand one example of complex organogenesis – namely mammalian limb development.
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Speaker
Juan MR ParrondoDescription
I will review our past work on the quantitative connection between dissipation or entropy production and irreversibility, as measured by the distinguishability between forward and backward trajectories.
In the last part of the talk, I will present some recent work on the application of those ideas to the estimation of the entropy production and ATP consumption in molecular motors, kinetic networks, and the motion of cilia in era cells when the observer has only access to a restricted number of variables.