We have investigated the binding of two porphyrins, meso-tetrakis ( p-sulfonatophenyl) porphyrin (TSPP) and protoporphyrin IX (PPIX), to tubulin alpha,beta-heterodimers. TSPP had been shown to directly target microtubules in cells. A comparative study between TSPP and PPIX was carried out because the latter is used in clinical applications and is hydrophobic, in comparison with the water soluble TSPP. The results presented in this manuscript show that both porphyrins bind tubulin with nearly identical stoichiometry but with different affinity (1.76 x 10 (5) M (-1) for PPIX; 1.1 x 10 (6) M (-1) for TSPP). The combination of spectroscopic data and molecular simulations suggests that both porphyrins bind as monomers and that their binding site is in proximity of one (or more) Trp residues but do not overlap with the binding site of other well characterized ligands. Molecular simulations also show that the sites that yield the lower energy minima place the porphyrins near the surface of the protein. In the case of TSPP, binding is favored by replacing the ion-dipole interaction of monodispersed TSPP in water with ion-ion interactions provided by two basic residues (His and Lys) at the location of the binding site. Although preliminary, the data show that porphyrin binding could be used to explain some of the effects that photosensitizers may directly produce on protein targets.
Energy systems and manufacturing processes of the 21st century are becoming increasingly dynamic and interconnected, which require new capabilities to effectively model and optimize their design and operations. Such next generation computational tools must leverage state-of-the-art techniques in optimization and be able to rapidly incorporate new advances. To address these requirements, we have developed the Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform, which builds on the strengths of both process simulators (model libraries) and algebraic modeling languages (advanced solvers). This paper specifically presents the IDAES Core Modeling Framework (IDAES-CMF), along with a case study demonstrating the application of the framework to solve process optimization problems. Capabilities provided by this framework include a flexible, modifiable, open-source platform for optimization of process flowsheets utilizing state-of-the-art solvers and solution techniques, fully open and extensible libraries of dynamic unit operations models and thermophysical property models, and integrated support for superstructure-based conceptual design and optimization under uncertainty.
A generic tactical model is developed considering third party price policies for the optimization of coordinated and centralized multi-product Supply Chains (SCs). To allow a more realistic assessment of these policies in each marketing situation, different price approximation models to estimate these policies are proposed, which are based on the demand elasticity theory, and result in different model implementations (LP, NLP, and MINLP). The consequences of using the proposed models on the SCs coordination, regarding not only their practical impact on the tactical decisions, but also the additional mathematical difficulties to be solved, are verified through a case study in which the coordination of a production–distribution SC and its energy generation SC is analyzed. The results show how the selection of the price approximation model affects the tactical decisions. The average price approximation leads to the worst decisions with a significant difference in the real total cost in comparison with the best piecewise approximation.Peer ReviewedPostprint (author's final draft
This paper addresses industrial gases supply chains involving multiple products at multiple plants that must be coordinated with multiple depot-truck-routes in order to satisfy customer demands. The full-space optimization problem corresponds to a large-scale mixed-integer linear programming model (MILP). To solve large-scale industrial problems, this paper proposes a rolling horizon approach with two aggregation strategies for solving the smaller subproblems. The first one relies on the linear programming (LP) relaxation for which the binary variables (complicating variables) of the distribution problem are treated as continuous, while the second one uses a novel tailored model for the distribution side constraints that leads to improved solutions. A real case study of an industrial gases supply chain has been addressed obtaining good results in both objective value and with lower computational effort compared with the full-space solution. The extension to longer time horizons through a receding horizon is also considered.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.