Speaker
Javier Macía ( Dept. Experimental and Health Sciences, UPF)

Description

Synthetic biology seeks to envision living cells as a matter of engineering. However, increasing evidence suggests that the genetic load imposed by the incorporation of synthetic devices in a living organism introduces a sort of unpredictability in the design process. As a result, individual part characterization is not enough to predict the behavior of designed circuits and thus, a costly trial-error process is eventually required. A new theoretical framework is necessary for the predictive treatment of the genetic load and their relationship with the observed reduction of the expression of a given synthetic gene when an extra genetic load is introduced in the circuit. The theory also explains that such dependence qualitatively differs when the extra load is added either by transcriptional or translational modifications. Interestingly, considering the limitation of the cellular resources as a limiting factor for gene expression, the mathematical formulation converges to an expression analogous to the Ohm's law for electric circuits.

 

Speaker
Eugenio Valdano (Universitat Rovira i Virgili)

Description

A wide range of physical, social and biological phenomena can be expressed in terms of spreading processes on networked systems. Notable examples include the spread of infectious diseases through direct contacts, the spatial propagation of epidemics driven by mobility networks, the spread of cyber worms along computer connections, or the diffusion of ideas mediated by social interactions. All these phenomena arise from a complex interplay between the spreading process and the network’s underlying topology and dynamics, making a full theoretical understanding difficult. In particular, a fundamental property of such phenomena is the presence of a critical transmission probability above which large-scale propagation occurs, as opposed to quick extinction of the epidemic-like process. Computing this threshold is of utmost importance for epidemic containment and control of information diffusion. Previous studies have extensively characterized the epidemic threshold in the regime of timescale separation, i.e. when the spreading process evolves much slower, or much faster, than the timescale characterizing the evolution of the underlying network. In the case of comparable timescales, however, extensive empirical studies in social settings show that the detailed temporal structure of the network often determines how epidemics spread, and cannot be neglected. I will present a new analytical framework for the computation of the epidemic threshold for an arbitrary time-varying network. By reinterpreting the tensor formalism of multi-layer networks, this framework allows the analytical calculation of the epidemic threshold for the Susceptible-Infectious-Susceptible (SIS) model, without making any assumption on contact structure and evolution. Many contagion processes, however, are characterized by a period of latency, i.e. a time lag between being infected and becoming infectious (SEIS model). I will show how the additional timescale induced by latency period has a non-negligible impact on the epidemic threshold, and derive an analytical formula for its computation in this scenario. In order to do that I will introduce a novel mapping of the SEIS model around critical point into an SIS model on a two-layer structure.

Speaker
Abigail Jiménez Lloret (Universidad de Almería)

Description

The L’Aquila event in 2009 highlighted the important problem in earthquake science of providing authoritative information about seismic forecasting to civil protection and to the general public. It is in Italy that the term Operational Earthquake Forecasting (OEF) was born. So far OEF systems are focused on the scientific part of the decision-making process, separating the information given by seismologists from how this information is managed. Two different tasks lie before seismologists right now: first, to produce models that reproduce seismicity, at least in a weather-like fashion, and second, to create unbiased ways of testing our models.

Speaker
Valerio Sbragaglia, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany

Description

New technologies (e.g. video tracking and telemetry) are shaping the way in which ethologists are approaching to the study of animal behavior. In particular social or collective behaviors are demonstrating how social networks are important in the understanding of animal ecology. I will present examples of animal behaviors where the understanding of the mechanisms of synchronization of the agents in the social networks could provide interesting insight in the ecology of the species.

1. Daily rhythms of physiology and behavior are regulated by endogenous timekeeping mechanisms, the circadian oscillators (or clocks). Light-dark cycles synchronize circadian clocks, but there are also other environmental cues that can synchronize them such as social interactions. I will present data regarding the locomotor activity rhythms of lobsters organized in a dominance hierarchy (lobsters form and maintain linear dominance hierarchies using specific chemical signal to communicate the rank status).

2. Moving in a school is a prerogative of many fish species. Staying together enhances the chances to escape from a predator, to find food or to communicate information from one side to the other of the school in short times. The distance among individuals in a school has been demonstrated to oscillate with particular frequencies. I will present data of laboratory experiments with a school of fish that showed oscillations in the distance among individuals.

3. The last example is provided by the tracking of fishes in a whole lake by telemetry. I will present some examples of tracking of an ongoing project where individuals of different fish species are tracked to understand their natural behavior and associate it to angler’s behavior (that is tracked as well).

As behavioral ecologist I hope to stimulate your research in complex networks with these three examples. 

Speaker
Grant Lythe (Department of Applied Mathematics, University of Leeds)

Description

TBA