SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget

In the context of industrial engineering, it is important to integrate efficient computational optimization methods in the product development process. Some of the most challenging simulation-based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradients, highly non-linear objectives and a limited function evaluation budget. Although a huge variety of different optimization algorithms is available, the development and selection of efficient algorithms for problems with these industrial relevant characteristics, remains a challenge. In this communication, a hybrid variant of Differential Evolution (DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG) methods within the framework of DE, in order to improve optimization efficiency on problems with the previously mentioned characteristics. The performance of the resulting derivative-free algorithm is compared with other state-of-the-art DE variants on 25 commonly used benchmark functions, under tight function evaluation budget constraints of 1000 evaluations. The experimental results indicate that the new algorithm performs excellent on the 'difficult' (high dimensional, multi-modal, inseparable) test functions. The operations used in the proposed mutation scheme, are computationally inexpensive, and can be easily implemented in existing differential evolution variants or other population-based optimization algorithms by a few lines of program code as an non-invasive optional setting. Besides the applicability of the presented algorithm by itself, the described concepts can serve as a useful and interesting addition to the algorithmic operators in the frameworks of heuristics and evolutionary optimization and computing

Bayesian optimization for materials design

We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian process regression, which allows predicting the performance of a new design based on previously tested designs. After providing a detailed introduction to Gaussian process regression, we introduce two Bayesian optimization methods: expected improvement, for design problems with noise-free evaluations; and the knowledge-gradient method, which generalizes expected improvement and may be used in design problems with noisy evaluations. Both methods are derived using a value-of-information analysis, and enjoy one-step Bayes-optimality

Age-Related Differences in Susceptibility to Carcinogenesis: A Quantitative Analysis of Empirical Animal Bioassay Data

In revising cancer risk assessment guidelines, the U.S. Environmental Protection Agency (EPA) analyzed animal cancer bioassay data over different periods of life. In this article, we report an improved analysis of these data (supplemented with some chemical carcinogenesis observations not included in the U.S. EPA’s original analysis) and animal bioassay studies of ionizing radiation. We use likelihood methods to avoid excluding cases where no tumors were observed in specific groups. We express dosage for animals of different weights on a metabolically consistent basis (concentration in air or food, or per unit body weight to the three-quarters power). Finally, we use a system of dummy variables to represent exposures during fetal, preweaning, and weaning–60-day postnatal periods, yielding separate estimates of relative sensitivity per day of dosing in these intervals. Central estimate results indicate a 5- to 60-fold increased carcinogenic sensitivity in the birth–weaning period per dose ÷ (body weight(0.75)-day) for mutagenic carcinogens and a somewhat smaller increase—centered about 5-fold—for radiation carcinogenesis per gray. Effects were greater in males than in females. We found a similar increased sensitivity in the fetal period for direct-acting nitrosoureas, but no such increased fetal sensitivity was detected for carcinogens requiring metabolic activation. For the birth–weaning period, we found an increased sensitivity for direct administration to the pups similar to that found for indirect exposure via lactation. Radiation experiments indicated that carcinogenic sensitivity is not constant through the “adult” period, but the dosage delivered in 12- to 21-month-old animals appears a few-fold less effective than the comparable dosage delivered in young adults (90–105 days of age)

Structural and functional characterization of Pseudomonas aeruginosa CupB chaperones

Pseudomonas aeruginosa, an important human pathogen, is estimated to be responsible for,10% of nosocomial infections worldwide. The pathogenesis of P. aeruginosa starts from its colonization in the damaged tissue or medical devices (e. g. catheters, prothesis and implanted heart valve etc.) facilitated by several extracellular adhesive factors including fimbrial pili. Several clusters containing fimbrial genes have been previously identified on the P. aeruginosa chromosome and named cup [1]. The assembly of the CupB pili is thought to be coordinated by two chaperones, CupB2 and CupB4. However, due to the lack of structural and biochemical data, their chaperone activities remain speculative. In this study, we report the 2.5 A crystal structure of P. aeruginosa CupB2. Based on the structure, we further tested the binding specificity of CupB2 and CupB4 towards CupB1 (the presumed major pilus subunit) and CupB6 (the putative adhesin) using limited trypsin digestion and strep-tactin pull-down assay. The structural and biochemical data suggest that CupB2 and CupB4 might play different, but not redundant, roles in CupB secretion. CupB2 is likely to be the chaperone of CupB1, and CupB4 could be the chaperone of CupB4:CupB5:CupB6, in which the interaction of CupB4 and CupB6 might be mediated via CupB5

Structural basis of outer membrane protein insertion by the BAM complex

All Gram-negative bacteria, mitochondria and chloroplasts have outer membrane proteins (OMPs) that perform many fundamental biological processes. The OMPs in Gram-negative bacteria are inserted and folded into the outer membrane by the β-barrel assembly machinery (BAM). The mechanism involved is poorly understood, owing to the absence of a structure of the entire BAM complex. Here we report two crystal structures of the Escherichia coli BAM complex in two distinct states: an inward-open state and a lateral-open state. Our structures reveal that the five polypeptide transport-associated domains of BamA form a ring architecture with four associated lipoproteins, BamB–BamE, in the periplasm. Our structural, functional studies and molecular dynamics simulations indicate that these subunits rotate with respect to the integral membrane β-barrel of BamA to induce movement of the β-strands of the barrel and promote insertion of the nascent OMP

A method for detergent-free isolation of membrane proteins in their local lipid environment.

Despite the great importance of membrane proteins, structural and functional studies of these proteins present major challenges. A significant hurdle is the extraction of the functional protein from its natural lipid membrane. Traditionally achieved with detergents, purification procedures can be costly and time consuming. A critical flaw with detergent approaches is the removal of the protein from the native lipid environment required to maintain functionally stable protein. This protocol describes the preparation of styrene maleic acid (SMA) co-polymer to extract membrane proteins from prokaryotic and eukaryotic expression systems. Successful isolation of membrane proteins into SMA lipid particles (SMALPs) allows the proteins to remain with native lipid, surrounded by SMA. We detail procedures for obtaining 25 g of SMA (4 d); explain the preparation of protein-containing SMALPs using membranes isolated from Escherichia coli (2 d) and control protein-free SMALPS using E. coli polar lipid extract (1-2 h); investigate SMALP protein purity by SDS-PAGE analysis and estimate protein concentration (4 h); and detail biophysical methods such as circular dichroism (CD) spectroscopy and sedimentation velocity analytical ultracentrifugation (svAUC) to undertake initial structural studies to characterize SMALPs (∼2 d). Together, these methods provide a practical tool kit for those wanting to use SMALPs to study membrane proteins

Lateral opening in the intact β-barrel assembly machinery captured by cryo-EM

The β-barrel assembly machinery (BAM) is a ~203 kDa complex of five proteins (BamA-E) which is essential for viability in E. coli. BAM promotes the folding and insertion of β-barrel proteins into the outer membrane via a poorly understood mechanism. Several current models suggest that BAM functions through a ‘lateral gating’ motion of the β-barrel of BamA. Here we present a cryo-EM structure of the BamABCDE complex, at 4.9 Å resolution. The structure is in a laterally open conformation showing that gating is independent of BamB binding. We describe conformational changes throughout the complex, and interactions between BamA, B, D, and E and the detergent micelle that suggest communication between BAM and the lipid bilayer. Finally, using an enhanced reconstitution protocol and functional assays, we show that for the outer membrane protein OmpT, efficient folding in vitro requires lateral gating in BAM