Charge Isomers of Myelin Basic Protein: Structure and Interactions with Membranes, Nucleotide Analogues, and Calmodulin

As an essential structural protein required for tight compaction of the central nervous system myelin sheath, myelin basic protein (MBP) is one of the candidate autoantigens of the human inflammatory demyelinating disease multiple sclerosis, which is characterized by the active degradation of the myelin sheath. In this work, recombinant murine analogues of the natural C1 and C8 charge components (rmC1 and rmC8), two isoforms of the classic 18.5-kDa MBP, were used as model proteins to get insights into the structure and function of the charge isomers. Various biochemical and biophysical methods such as size exclusion chromatography, calorimetry, surface plasmon resonance, small angle X-ray and neutron scattering, Raman and fluorescence spectroscopy, and conventional as well as synchrotron radiation circular dichroism were used to investigate differences between these two isoforms, both from the structural point of view, and regarding interactions with ligands, including calmodulin (CaM), various detergents, nucleotide analogues, and lipids. Overall, our results provide further proof that rmC8 is deficient both in structure and especially in function, when compared to rmC1. While the CaM binding properties of the two forms are very similar, their interactions with membrane mimics are different. CaM can be used to remove MBP from immobilized lipid monolayers made of synthetic lipids - a phenomenon, which may be of relevance for MBP function and its regulation. Furthermore, using fluorescently labelled nucleotides, we observed binding of ATP and GTP, but not AMP, by MBP; the binding of nucleoside triphosphates was inhibited by the presence of CaM. Together, our results provide important further data on the interactions between MBP and its ligands, and on the differences in the structure and function between MBP charge isomers

Effect of macromolecular crowding on the rate of diffusion-limited enzymatic reaction

The cytoplasm of a living cell is crowded with several macromolecules of different shapes and sizes. Molecular diffusion in such a medium becomes anomalous due to the presence of macromolecules and diffusivity is expected to decrease with increase in macromolecular crowding. Moreover, many cellular processes are dependent on molecular diffusion in the cell cytosol. The enzymatic reaction rate has been shown to be affected by the presence of such macromolecules. A simple numerical model is proposed here based on percolation and diffusion in disordered systems to study the effect of macromolecular crowding on the enzymatic reaction rates. The model explains qualitatively some of the experimental observations.Comment: 6 pages, 4 figure

Robust Predictive Energy Management of Connected Power-Split Hybrid Electric Vehicles Using Dynamic Traffic Data

Abstract This research focuses on the predictive energy management of connected human-driven hybrid electric vehicles (HEVs) to improve their fuel efficiency while robustly satisfying system constraints. We propose a hierarchical control framework that effectively exploits long-term and short-term decision-making benefits by integrating real-time traffic data into the energy management strategy. A pseudo-spectral optimal controller (PSOC) with discounted cost is utilized at the high level to find an approximate optimal solution for the entire driving cycle. At the low-level, a long short-term memory neural network (NN) is developed for higher quality velocity predictions over the low-level's short time horizons. Tube-based model predictive controller is then used at the low level to ensure constraints satisfaction in the presence of velocity prediction errors. Simulation results over real-world traffic data show an improvement in fuel economy for the proposed controller that is real-time applicable and robust to the driving cycle's uncertainty.</jats:p

Distributed Model Predictive Control for Connected and Automated Vehicles in the Presence of Uncertainty

Abstract This article focuses on the development of distributed robust model predictive control (MPC) methods for multiple connected and automated vehicles (CAVs) to ensure their safe operation in the presence of uncertainty. The proposed layered control framework includes reference trajectory generation, distributionally robust obstacle occupancy set computation, distributed state constraint set evaluation, data-driven linear model representation, and robust tube-based MPC design. To enable distributed operation among the CAVs, we present a method, which exploits sampling-based reference trajectory generation and distributed constraint set evaluation methods, that decouples the coupled collision avoidance constraint among the CAVs. This is followed by data-driven linear model representation of the nonlinear system to evaluate the convex equivalent of the nonlinear control problem. Finally, to ensure safe operation in the presence of uncertainty, this article employs a robust tube-based MPC method. For a multiple CAV lane change problem, simulation results show the efficacy of the proposed controller in terms of computational efficiency and the ability to generate safe and smooth CAV trajectories in a distributed fashion.</jats:p

Velocity Optimization and Robust Energy Management of Connected Power-Split Hybrid Electric Vehicles

Abstract This paper focuses on an eco-driving-based hierarchical robust energy management strategy for connected and automated hybrid electric vehicles in the presence of uncertainty. The proposed control strategy includes a velocity optimizer, which evaluates the optimal vehicle velocity, and a powertrain energy manager, which evaluates the optimal power split between the engine and the battery in a hierarchical framework. The velocity optimizer accounts for regenerative braking and minimizes the total driving power and friction braking over a short control horizon. The hierarchical powertrain energy manager employs a long- and short-term strategy where it first approximately solves its problem over a long time horizon (the whole trip time in this paper) using the traffic data obtained from vehicle-to-infrastructure (V2I) connectivity. This is followed by a short-term decision maker that utilizes the velocity optimizer and long-term solution, and solves the energy management problem over a relatively short time horizon using robust model predictive control (MPC) methods to factor in any uncertainty in the velocity profile due to uncertain traffic. We solve the long-term energy management problem using pseudo-spectral optimal control method, and the short-term problem using robust tube-based MPC method. Simulation results with standard driving cycle velocity profile and real-world traffic data show the competence of our proposed approach. Our proposed co-optimization approach with long- and short-term solution results in ≈12% more energy efficiency than a baseline co-optimization approach.</jats:p