Abstract: Traditional Protein-Protein Interactions Networks (PPIN), although they have proved their usefulness, fall short to describe the variability of a real system. For instance, the interactions between proteins can be strengthened or weakened under different conditions as a consequence of gene expression variation of the genes codifying the proteins. In order to capture this higher dimensionality, we combine gene expression data and a traditional PPIN of the pathogen Mycobacterium Tuberculosis to build a multilayer network where each layer corresponds to a different experimental condition and contains only those genes that are highly expressed under such environment. Specifically we will focused on a 6 layer network, corresponding to the 6 major stresses that the bacterium faces in the phagosomical environment (Hypoxia, Starvation, Cell Wall Damage, Ion Deprivation, Exposure to NO and Oxidation). The posterior analysis of this multilayer network allows us to identify hubs or modules of genes with high importance for a certain stress condition and to study if they are preserved throughout the different environments or if they appear as a specific response to a particular stimulus. For the specific case of M.Tuberculosis we identify genes of the family of ESAT-6 as network hubs under most types of stresses. These proteins, that are not hubs of the original PPIN, remarkably contain one of the main virulence markers of the pathogen: the ESAT-6 protein itself and its partner cfp-10. This method could provide useful insights to understand the behavior of the pathogen at the infection process and other stages of its cycle. It is easily generalizable to different organisms and datasets, constituting a promising approach to the observation of cellular adaptive response to different conditions at a systemic scale.

Abstract: We present results from a new three dimensional agent-based model, designed to investigate the role of trail pheromone in the self-organised construction of a royal chamber in termite species Macrotermes Subhyalinus (Rambur). The royal chamber is a dome-like structure which worker termites build around a much enlarged termite queen and observations have shown that the building proceeds from the ground upwards, involving the formation of distinct pillars which become joined together laterally at a specific height. Earlier models have demonstrated how an attractive pheromone in the building material used by the termites (cement pheromone) can mediate the building process such that pillars begin to form to some extent, but have not succeeded in reproducing other observed characteristics of the construction, or quantified the dependency of the pillar formation on model parameters. A factor not included in these models, and yet known to be key to the success of royal chamber construction, is the use of trail pheromone by the termites. In order to investigate the dependency of the building process on the use of trail pheromone, our model incorporates a new hypothesis; that the termites use trail pheromone to coordinate their movement to building sites and a combination of both trail and cement pheromones to moderate their building activity. The model is able to show evidence of observed construction features not present in earlier models. In addition, hierarchical cluster analysis is used to identify forming pillars, and a measure of the shape of the pillars, together with the mean intra-pillar angular separation, is used to quantify the dependency of this aspect of the building process on the model parameters and the extent to which the new model differs from the earlier models.

Abstract: Interval timing (IT) is the capacity to count the passing of time. It has been shown that humans can quite accurately memorize the duration of a stimulus, and reuse that information later on. Simpler organisms also show this ability, such as the slime mold physarium polycephalum that can memorize durations despite the absence of a neural system. Our work explores how IT can be implemented using only the dynamics of a neural network, i.e. without synaptic plasticity. We evolved using a genetic algorithm (GA) a continuous time recurrent neural network (CTRNN) to control a simulated robot who must memorize the duration of an initially presented stimulus, to then move to a target location and remain there for the same duration before exiting it. For this experiment, we required the CTRNN to be able to detect and memorize durations from 1s up to 5s with 1s increments, by using the realistic parameter values of the CTRNN. The successfully evolved CTRNNs have proven that a purely dynamical solution to IT can be found, but, more surprisingly, that the evolved strategy can detect and memorize any duration between 1s and 6s. Indeed, the evolved strategy does not implement the memories as stable attractors, but rather through different trajectories within the state space of the network. Each trajectory is unique to each duration, and the memory will decay progressively after having been used. This work presents the first demonstration of an embedded dynamical system implementing IT for a continuous range of durations, differing from a discrete range generally obtained with stable attractors. This result proves that IT could have evolved in living organisms before any kind of structural plasticity appeared, and supports some observations indicating that it could still be so, i.e. purely dynamical, in the human brain for some time related tasks.

Abstract: In this work, we aim to understand the influence of the heterogeneity of infection rates on the Susceptible-Infected-Susceptible (SIS) epidemic spreading. Specifically, we keep the recovery rates the same for all nodes and study the influence of the independently identically distributed (i.i.d.) infection rates on the average fraction y of infected nodes in the meta-stable state, which indicates the severity of the overall infection. Motivated by real-world datasets, we consider the log-normal and gamma distributions for the infection rates and we design as well a symmetric distribution so that we have a systematic view of the influence of various distributions. We compare the average fraction y of infected nodes in the heterogeneous case where the infection rate of each link is an i.i.d. random variable following aforementioned distributions with that of the corresponding homogeneous case where all the links have the same infection rate that equals the average infection rate of the heterogeneous case. By continuous-time simulations on several types of networks, theoretical proofs and physical interpretations, (1) we unveil two contrary effects: the heterogeneity of infection rates could both enhance and mitigate the epidemic spread depending on the network structure and the average infection rate normalized by the recovery rate (2) we illustrate that to what extent the heterogeneous infection rates enhance or mitigate the epidemic spread depends further on their distribution. Finally, we verify our conclusions via real-world networks with their heterogeneous infection rates. Our results indicate to what extent and in which situations real-world heterogeneous viral spreading may differ from what classic homogenous SIS model predicts.

Abstract: The marriage between mobile communication and Internet breeds strange social creatures. Such systems -- let’s call them complexes -- tend to produce emergent behaviors. Eisenhower's notion of the USA Military-Industrial Complex is an excellent real-life example. Legal theory has been some 17 years in confrontation with the music-user community. This is a complex: its agents are diverse (consumers, artists, producers, retail, right enforcers, etc.), connected and reciprocally dependent. The complex is moreover a co-evolving bunch (considering dynamics in its environment). As legal theorists we have some questions here. The foundation of our trade is a conception of individual freedom that supports liability. Thus: pressures on individual agents' capacities to comprehend and to be responsible will disrupt the basic paradigms of our discipline. This is worth to be investigated from a legal-theory perspective, when we value informed regulation in our society. As the current issue concerns a complex, we need the complexity-theory perspective too. In our contribution, we emulate the recent history of the music-using-community in the EU and the USA, from the birth of file sharing until the “appification” via iTunes and Spotify. We construct datasets (1) with American judicial decisions involving “fair use” rule in music file sharing disputes and (2) with European landmark decisions and legislation-changes. Thus we support investigating their influences on the complex (and the other way around) through agent modeling. We use ABM of agents that operate on stochastically distributed preferences to trace the evolution of this rule. We first investigate how history can be emulated best. Subsequently we observe and discuss how differences in comprehensiveness of enforcement (a parameter in the model) turn out.