Optimized nanospherical layered alternating metal-dielectric probes for optical sensing.

Multishell nanospheres have been proposed as a class of layered alternating metal-dielectric probes (LAMPs) that can greatly enhance sensitivity and multiplexing capabilities of optical molecular imaging . Here we theoretically demonstrate that the interplasmonic coupling within these spheres and hence their spectral responses can be tuned by a rational selection of layer thicknesses. As a proof-of-concept, layered Mie theory calculations of near- and far-field characteristics followed by a genetic algorithm-based selection are presented for gold-silica, silver-silica and copper-silica LAMPs.

Optimally designed nanolayered metal-dielectric particles as probes for massively multiplexed and ultrasensitive molecular assays.

An outstanding challenge in biomedical sciences is to devise a palette of molecular probes that can enable simultaneous and quantitative imaging of tens to hundreds of species down to ultralow concentrations. Addressing this need using surface-enhanced Raman scattering-based probes is potentially possible. Here, we theorize a rational design and optimization strategy to obtain reproducible probes using nanospheres with alternating metal and reporter-filled dielectric layers.

Invariability of central metabolic flux distribution in Shewanella oneidensis MR-1 under environmental or genetic perturbations.

An environmentally important bacterium with versatile respiration, Shewanella oneidensis MR-1, displayed significantly different growth rates under three culture conditions: minimal medium (doubling time approximately 3 h), salt stressed minimal medium (doubling time approximately 6 h), and minimal medium with amino acid supplementation (doubling time approximately 1.5 h). (13)C-based metabolic flux analysis indicated that fluxes of central metabolic reactions remained relatively constant under the three growth conditions, which is in stark contrast to the reported significant changes in the transcript and metabolite profiles under various growth conditions.

Metabolic flux analysis of Shewanella spp. reveals evolutionary robustness in central carbon metabolism.

Shewanella spp. are a group of facultative anaerobic bacteria widely distributed in marine and freshwater environments. In this study, we profiled the central metabolic fluxes of eight recently sequenced Shewanella species grown under the same condition in minimal medium with [3-13C] lactate.

Evolutionary computation and multimodal search: a good combination to tackle molecular diversity in the field of peptide design.

The awesome degree of structural diversity accessible in peptide design has created a demand for computational resources that can evaluate a multitude of candidate structures. In our specific case, we translate the peptide design problem to an optimization problem, and use evolutionary computation (EC) in tandem with docking to carry out a combinatorial search. However, the use of EC in huge search spaces with different optima may pose certain drawbacks.

Automated global structure extraction for effective local building block processing in XCS.

Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are specialized, propagated, and recombined to provide increasingly accurate subsolutions. Recently, it was shown that, as in conventional genetic algorithms (GAs), some problems require efficient processing of subsets of features to find problem solutions efficiently.

ENPDA: an evolutionary structure-based de novo peptide design algorithm.

One of the goals of computational chemists is to automate the de novo design of bioactive molecules. Despite significant advances in computational approaches to ligand design and binding energy evaluation, novel procedures for ligand design are required. Evolutionary computation provides a new approach to this design endeavor.

Design of enhanced agonists through the use of a new virtual screening method: application to peptides that bind class I major histocompatibility complex (MHC) molecules.

A new screening procedure is described that uses docking calculations to design enhanced agonist peptides that bind to major histocompatibility complex (MHC) class I receptors. The screening process proceeds via single mutations of one amino acid at the positions that directly interact with the MHC receptor. The energetic and structural effects of these mutations have been studied using fragments of the original ligand that vary in length.

Evolutionary combinatorial chemistry, a novel tool for SAR studies on peptide transport across the blood-brain barrier. Part 2. Design, synthesis and evaluation of a first generation of peptides.

The use of high-throughput methods in drug discovery allows the generation and testing of a large number of compounds, but at the price of providing redundant information. Evolutionary combinatorial chemistry combines the selection and synthesis of biologically active compounds with artificial intelligence optimization methods, such as genetic algorithms (GA). Drug candidates for the treatment of central nervous system (CNS) disorders must overcome the blood-brain barrier (BBB).

Bounding the effect of noise in multiobjective learning classifier systems.

This paper analyzes the impact of using noisy data sets in Pittsburgh-style learning classifier systems. This study was done using a particular kind of learning classifier system based on multiobjective selection. Our goal was to characterize the behavior of this kind of algorithms when dealing with noisy domains.