Peptide-induced coalescence of bicellar lipid assemblies: sensitivity to anionic lipid and peptide properties

Bicellar lipid mixture dispersions progressively coalesce to larger structures on warming. This phase behaviour is particularly sensitive to interactions that perturb bilayer properties. In this study, ²H NMR was used to study the perturbation of bicellar lipid mixtures by two peptides (SP-B₆₃₋₇₈, a lung surfactant protein fragment and Magainin 2, an antimicrobial peptide) which are structurally similar. Particular attention was paid to the relation between peptide-induced perturbation and lipid composition. In bicellar dispersions containing only zwitterionic lipids (DMPC-d₅₄/DMPC/DHPC (3:1:1)) both peptides had little to no effect on the temperature at which coalescence to larger structures occurred. Conversely, in mixtures containing anionic lipids (DMPC-d₅₄/DMPG/DHPC (3:1:1)), both peptides modified bicellar phase behaviour. In mixtures containing SP-B₆₃₋₇₈, the presence of peptide decreased the temperature of the ribbon-like to extended lamellar phase transition. The addition of Magainin 2 to DMPCd₅₄/ DMPG/DHPC (3:1:1) mixtures, in contrast, increased the temperature of this transition and yielded a series of spectra resembling DMPC/DHPC (4:1) mixtures. Additional studies of lipid dispersions containing deuterated anionic lipids were done to determine whether the observed perturbation involved a peptide-induced separation of zwitterionic and anionic lipids. Comparison of DMPC/DMPG-d₅₄/DHPC (3:1:1) and DMPC-d₅₄/DMPG/DHPC (3:1:1) mixtures showed that DMPC and DMPG occupy similar environments in the presence of SP-B₆₃₋₇₈, but different lipid environments in the presence of Magainin 2. This might reflect the promotion of anionic lipid clustering by Magainin 2. These results demonstrate the variability of mechanisms of peptide-induced perturbation and suggest that lipid composition is an important factor in the peptide-induced perturbation of lipid structures.

Integrated sensitivity analysis, calibration, and uncertainty propagation analysis approaches for supporting hydrological modeling

The successful performance of a hydrological model is usually challenged by the quality of the sensitivity analysis, calibration and uncertainty analysis carried out in the modeling exercise and subsequent simulation results. This is especially important under changing climatic conditions where there are more uncertainties associated with climate models and downscaling processes that increase the complexities of the hydrological modeling system. In response to these challenges and to improve the performance of the hydrological models under changing climatic conditions, this research proposed five new methods for supporting hydrological modeling. First, a design of experiment aided sensitivity analysis and parameterization (DOE-SAP) method was proposed to investigate the significant parameters and provide more reliable sensitivity analysis for improving parameterization during hydrological modeling. The better calibration results along with the advanced sensitivity analysis for significant parameters and their interactions were achieved in the case study. Second, a comprehensive uncertainty evaluation scheme was developed to evaluate three uncertainty analysis methods, the sequential uncertainty fitting version 2 (SUFI-2), generalized likelihood uncertainty estimation (GLUE) and Parameter solution (ParaSol) methods. The results showed that the SUFI-2 performed better than the other two methods based on calibration and uncertainty analysis results. The proposed evaluation scheme demonstrated that it is capable of selecting the most suitable uncertainty method for case studies. Third, a novel sequential multi-criteria based calibration and uncertainty analysis (SMC-CUA) method was proposed to improve the efficiency of calibration and uncertainty analysis and control the phenomenon of equifinality. The results showed that the SMC-CUA method was able to provide better uncertainty analysis results with high computational efficiency compared to the SUFI-2 and GLUE methods and control parameter uncertainty and the equifinality effect without sacrificing simulation performance. Fourth, an innovative response based statistical evaluation method (RESEM) was proposed for estimating the uncertainty propagated effects and providing long-term prediction for hydrological responses under changing climatic conditions. By using RESEM, the uncertainty propagated from statistical downscaling to hydrological modeling can be evaluated. Fifth, an integrated simulation-based evaluation system for uncertainty propagation analysis (ISES-UPA) was proposed for investigating the effects and contributions of different uncertainty components to the total propagated uncertainty from statistical downscaling. Using ISES-UPA, the uncertainty from statistical downscaling, uncertainty from hydrological modeling, and the total uncertainty from two uncertainty sources can be compared and quantified. The feasibility of all the methods has been tested using hypothetical and real-world case studies. The proposed methods can also be integrated as a hydrological modeling system to better support hydrological studies under changing climatic conditions. The results from the proposed integrated hydrological modeling system can be used as scientific references for decision makers to reduce the potential risk of damages caused by extreme events for long-term water resource management and planning.