Parametric Sensitivity Analysis of the Most Recent Computational Models of Rabbit Cardiac Pacemaking

The cellular basis of cardiac pacemaking activity, and specifically the quantitative contributions of particular mechanisms, is still debated. Reliable computational models of sinoatrial nodal (SAN) cells may provide mechanistic insights, but competing models are built from different data sets and with different underlying assumptions. To understand quantitative differences between alternative models, we performed thorough parameter sensitivity analyses of the SAN models of Maltsev & Lakatta (2009) and Severi et al (2012). Model parameters were randomized to generate a population of cell models with different properties, simulations performed with each set of random parameters generated 14 quantitative outputs that characterized cellular activity, and regression methods were used to analyze the population behavior. Clear differences between the two models were observed at every step of the analysis. Specifically: (1) SR Ca2+ pump activity had a greater effect on SAN cell cycle length (CL) in the Maltsev model; (2) conversely, parameters describing the funny current (If) had a greater effect on CL in the Severi model; (3) changes in rapid delayed rectifier conductance (GKr) had opposite effects on action potential amplitude in the two models; (4) within the population, a greater percentage of model cells failed to exhibit action potentials in the Maltsev model (27%) compared with the Severi model (7%), implying greater robustness in the latter; (5) confirming this initial impression, bifurcation analyses indicated that smaller relative changes in GKr or Na+-K+ pump activity led to failed action potentials in the Maltsev model. Overall, the results suggest experimental tests that can distinguish between models and alternative hypotheses, and the analysis offers strategies for developing anti-arrhythmic pharmaceuticals by predicting their effect on the pacemaking activity.

Prokaryotic community structure in ultra-slow spreading Southwest Indian Ridge.

The Southwest Indian Ridge segment that extends between 10° and 16° E has the slowest spreading rate of any other oceanic ridge (about 8.4 mm/year). In 2013 during the expedition ANTXXIX/8 seismology, geology, microbiology, heat flow analyses were carried out. Here, no hydrothermal plumes or black smoker systems were found but the results of the survey allowed to identify areas with peculiar characteristics: Area 1 with higher heat flux bsf; Area 2 where in 2002 the presence of hydrothermal emissions was hypothesized (Bach et al., 2002); Area 3 with anomalies of methane, ammonium, sulphide and dissolved inorganic carbon in pore water sediment profiles, and recovery of fauna vents. All these aspects suggest the presence of a hydrothermal circulation. Using Illumina 16S gene tag, statistical tools and phylogenetic trees, I provided a biological proof of the presence of hydrothermal circulation in this ridge segment. At Area 3, alpha and beta diversity indexes showed similarities with those described for venting microbial communities and about 40-70% of the dominant microbial community was found phylogenetically related to clones isolated hydrothermal-driven environments. Although the majority of chemosynthetic environment related taxa were not classified like autotrophic prokaryotes, some of them are key taxa in support of the presence of hydrothermal circulation, since they are partners of consortia or mediate specific reaction typically described for hydrothermal and seep environments, or are specialized organisms in exploiting labile organic substrates. Concluding, these results are remarkable because support the importance of ultra slow spreading ridge systems in contributing to global geochemical cycles and larval dispersion of vent fauna.

Distributed non-cooperative robust economic predictive control for dynamically coupled linear systems.

In this thesis, a tube-based Distributed Economic Predictive Control (DEPC) scheme is presented for a group of dynamically coupled linear subsystems. These subsystems are components of a large scale system and control inputs are computed based on optimizing a local economic objective. Each subsystem is interacting with its neighbors by sending its future reference trajectory, at each sampling time. It solves a local optimization problem in parallel, based on the received future reference trajectories of the other subsystems. To ensure recursive feasibility and a performance bound, each subsystem is constrained to not deviate too much from its communicated reference trajectory. This difference between the plan trajectory and the communicated one is interpreted as a disturbance on the local level. Then, to ensure the satisfaction of both state and input constraints, they are tightened by considering explicitly the effect of these local disturbances. The proposed approach averages over all possible disturbances, handles tightened state and input constraints, while satisfies the compatibility constraints to guarantee that the actual trajectory lies within a certain bound in the neighborhood of the reference one. Each subsystem is optimizing a local arbitrary economic objective function in parallel while considering a local terminal constraint to guarantee recursive feasibility. In this framework, economic performance guarantees for a tube-based distributed predictive control (DPC) scheme are developed rigorously. It is presented that the closed-loop nominal subsystem has a robust average performance bound locally which is no worse than that of a local robust steady state. Since a robust algorithm is applying on the states of the real (with disturbances) subsystems, this bound can be interpreted as an average performance result for the real closed-loop system. To this end, we present our outcomes on local and global performance, illustrated by a numerical example.