Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data

Network Theory is a prolific and lively field, especially when it approaches Biology. New concepts from this theory find application in areas where extensive datasets are already available for analysis, without the need to invest money to collect them. The only tools that are necessary to accomplish an analysis are easily accessible: a computing machine and a good algorithm. As these two tools progress, thanks to technology advancement and human efforts, wider and wider datasets can be analysed. The aim of this paper is twofold. Firstly, to provide an overview of one of these concepts, which originates at the meeting point between Network Theory and Statistical Mechanics: the entropy of a network ensemble. This quantity has been described from different angles in the literature. Our approach tries to be a synthesis of the different points of view. The second part of the work is devoted to presenting a parallel algorithm that can evaluate this quantity over an extensive dataset. Eventually, the algorithm will also be used to analyse high-throughput data coming from biology.

Micropollutants affect the ability of phytoplankton communities to track environmental changes

Human activities strongly influence environmental processes, and while human domination increases, biodiversity progressively declines in ecosystems worldwide. High genetic and phenotypic variability ensures functionality and stability of ecosystem processes through time and increases the resilience and the adaptive capacity of populations and communities, while a reduction in functional diversity leads to a decrease in the ability to respond in a changing environment. Pollution is becoming one of the major threats in aquatic ecosystem, and pharmaceutical and personal care products (PPCPs) in particular are a relatively new group of environmental contaminants suspected to have adverse effects on aquatic organisms. There is still a lake of knowledge on the responses of communities to complex chemical mixtures in the environment. We used an individual-trait-based approach to assess the response of a phytoplankton community in a scenario of combined pollution and environmental change (steady increasing in temperature). We manipulated individual-level trait diversity directly (by filtering out size classes) and indirectly (through exposure to PPCPs mixture), and studied how reduction in trait-diversity affected community structure, production of biomass and the ability of the community to track a changing environment. We found that exposure to PPCPs slows down the ability of the community to respond to an increasing temperature. Our study also highlights how physiological responses (induced by PPCPs exposure) are important for ecosystem processes: although from an ecological point of view experimental communities converged to a similar structure, they were functionally different.