Biological invasions are one of the major threats to biodiversity at the global scale, causing numerous environmental impacts and having high direct and indirect costs associated with their management, control and eradication. In this work, we present a system-dynamic modelling approach for the biocontrol of the invasive plant species Alternanthera philoxeroides using its natural predator, Agasicles hygrophila, as a biocontrol agent. We have simulated different scenarios in the Finisterre region (Spain), where a single population of the invasive plant has been recently described. To assess the effectiveness of A. hygrophila as a biocontrol agent in the region, a population dynamic model was developed in order to include the life-cycle of both species, as well as the interaction among them. The results of the simulations indicate that the control of this new invasive plant is possible, as long as several releases of the biocontrol agent are made over time. The proposed model can support the control or even the eradication of the population of A. philoxeroides with a minimal impact on the environment. Additionally, the proposed framework also represents a versatile dynamic tool, adjustable to different local management specificities (objectives and parameters) and capable of responding under different contexts. Hence, this approach can be used to guide eradication efforts of new invasive species, to improve the applicability of early management measures as biocontrol, and to support decision-making by testing several alternative management scenarios.

The African trypanosome, Trypanosoma brucei, is a unicellular parasite causing African Trypanosomiasis (sleeping sickness in humans and nagana in animals). Due to some of its unique properties, it has emerged as a popular model organism in systems biology. A predictive quantitative model of glycolysis in the bloodstream form of the parasite has been constructed and updated several times. The Silicon Trypanosome is a project that brings together modellers and experimentalists to improve and extend this core model with new pathways and additional levels of regulation. These new extensions and analyses use computational methods that explicitly take different levels of uncertainty into account. During this project, numerous tools and techniques have been developed for this purpose, which can now be used for a wide range of different studies in systems biology.