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PubMedDate not supplied

Optimal approach for classification of acute leukemia subtypes based on gene expression data.

The classification of cancer subtypes, which is critical for successful treatment, has been studied extensively with the use of gene expression profiles from oligonucleotide chips or cDNA microarrays. Various pattern recognition methods have been successfully applied to gene expression data. However, these methods are not optimal, rather they are high-performance classifiers that emphasize only classification accuracy. In this paper, we propose an approach for the construction of the optimal linear classifier using gene expression data. Two linear classification methods, linear discriminant analysis (LDA) and discriminant partial least-squares (DPLS), are applied to distinguish acute leukemia subtypes. These methods are shown to give satisfactory accuracy. Moreover, we determined optimally the number of genes participating in the classification (a remarkably small number compared to previous results) on the basis of the statistical significance test. Thus, the proposed method constructs the optimal classifier that is composed of a small size predictor and provides high accuracy.

Algorithms↗

PubMedDate not supplied

Use and optimization of a dual-flowrate loading strategy to maximize throughput in protein-a affinity chromatography.

The effect of an alternate strategy employing two different flowrates during loading was explored as a means of increasing system productivity in Protein-A chromatography. The effect of such a loading strategy was evaluated using a chromatographic model that was able to accurately predict experimental breakthrough curves for this Protein-A system. A gradient-based optimization routine is carried out to establish the optimal loading conditions (initial and final flowrates and switching time). The two-step loading strategy (using a higher flowrate during the initial stages followed by a lower flowrate) was evaluated for an Fc-fusion protein and was found to result in significant improvements in process throughput. In an extension of this optimization routine, dynamic loading capacity and productivity were simultaneously optimized using a weighted objective function, and this result was compared to that obtained with the single flowrate. Again, the dual-flowrate strategy was found to be superior.

Adsorption↗

PubMedDate not supplied

Alkaline protease production by an isolated Bacillus circulans under solid-state fermentation using agroindustrial waste: process parameters optimization.

Alkaline protease production using isolated Bacillus circulans under solid-state fermentation environment was optimized by using Taguchi orthogonal array (OA) experimental design (DOE) methodology to understand the interaction of a large number of variables spanned by factors and their settings with a small number of experiments in order to economize the process optimization. The software-designed experiments with an OA worksheet of L-27 was selected to optimize fermentation (temperature, particle size, moisture content and pH), nutrition (yeast extract and maltose), and biomaterial-related (inoculum size and incubation time) factors for the best production yields. Analysis of experimental data using Qualitek-4 methodology showed significant variation in enzyme production levels (32,000-73,000 units per gram material) and dependence on the selected factors and their assigned levels. Validation of experimental results on alkaline protease production by this bacterial strain based on DOE methodology revealed 51% enhanced protease production compared to average performance of the fermentation, indicating the importance of this methodology in the evaluation of main and interaction effects of the selected factors individually and in combination for bioprocess optimization.

Agriculture↗

PubMedDate not supplied

Modeling retrovirus production for gene therapy. 1. Determination Of optimal bioreaction mode and harvest strategy.

Although retroviruses are a promising tool for gene therapy, there are two major problems limiting the establishment of viable industrial processes: retrovirus stability and low final yield in the supernatant. This fact emphasizes the need for an effective process optimization, not only at a genetic level but also at a bioprocess engineering level. In part 1 of this paper a mathematical model was developed to optimize the bioreaction yield by determining the best retrovirus harvest strategy in perfusion cultures. PA317 cells producing recombinant retroviruses were used to develop and test this model. Cell culture was performed in stirred tanks using porous supports. The parameters of the proposed model were experimentally determined for batch and perfusion cultures at 32 and 37 degrees C both with and without additives to enhance production; the model was then validated. This model allowed the determination of the optimal values of all operational variables included: batch and perfusion duration and perfusion rate. The highest productivity (2682 virus cm(-)(3) h(-)(1)) was obtained under the following conditions: batch at 37 degrees C for 53 h followed by perfusion at 32 degrees C for 23 h with a perfusion rate of 0.107 h(-)(1). This value was 3.5-fold higher than the best result obtained in batch cultures for the same conditions of titer and quality. A sensitivity analysis of the parameters showed that the parameters that affect most the final productivity depend on the bioreaction phase: cell growth in batch culture and production and product degradation in perfusion culture. In part 2 of this paper, this model is extended to the first step of downstream processing, and the addition of further steps to the process is discussed in order to achieve global process optimization.

Animals↗

PubMedDate not supplied

High-throughput screening and optimization of photoembossed relief structures.

A methodology for the rapid design, screening, and optimization of coating systems with surface relief structures, using a combination of statistical experimental design, high-throughput experimentation, data mining, and graphical and mathematical optimization routines was developed. The methodology was applied to photopolymers used in photoembossing applications. A library of 72 films was prepared by dispensing a given amount of sample onto a chemically patterned substrate consisting of hydrophilic areas separated by fluorinated hydrophobic barriers. Film composition and film processing conditions were determined using statistical experimental design. The surface topology of the films was characterized by automated AFM. Subsequently, models explaining the dependence of surface topologies on sample composition and processing parameters were developed and used for screening a virtual 4000-membered in silico library of photopolymer lacquers. Simple graphical optimization or Pareto algorithms were subsequently used to find an ensemble of formulations, which were optimal with respect to a predefined set of properties, such as aspect ratio and shape of the relief structures.

Automation↗

PubMedDate not supplied

Parallel random tunneling algorithm for structural optimization of Lennard-Jones clusters up to N=330.

A random tunneling algorithm (RTA) is derived from the terminal repeller unconstrained subenergy tunneling (TRUST) algorithm, and the parallelization of the RTA is implemented with an island parallel paradigm. Combined with the techniques of angular moving, the parallel random tunneling algorithm (PRTA) is applied to the optimization of Lennard-Jones (LJ) atomic clusters, and all the global minima of LJ clusters with size up to 200 are successfully located. For the optimization of larger cluster, a PRTA with an improved seeding technique is developed and successfully applied to the optimization of LJ151-LJ309. Furthermore, the optimized structures of LJ309-330 with the PRTA, which have not been studied before, are also provided.

Journal Article↗

PubMedDate not supplied

QSPR modeling of lipid-water partition coefficient by optimization of correlation weights of local graph invariants.

The optimization of correlation weights scheme was applied to model lipid-water partition coefficient (log P) of two sets of diverse functional aliphatic and aromatic compounds. In both cases, the optimized descriptors formulated based on the data of training sets generated statistically acceptable relations for the corresponding training sets, test sets, and combined sets. When the relations of log P values with the optimized molecular descriptors formulated based on the data of the training sets were used for calculation of log P values of the corresponding training sets, rpred2 values were found to be satisfactory (above 0.99) in both cases, which is indicative of the predictive potential of the scheme. The results indicate promising potential of the optimization of a correlation weights scheme in modeling studies.

Journal Article↗

PubMedDate not supplied

Improved CoMFA modeling by optimization of settings.

The possibility of improving the predictive ability of comparative molecular field analysis (CoMFA) by settings optimization has been evaluated to show that CoMFA predictive ability can be improved. Ten different CoMFA settings are evaluated, producing a total of 6120 models. This method has been applied to nine different data sets, including the widely used benchmark steroid data set, as well as eight other data sets proposed as QSAR benchmarking data sets by Sutherland et al. (J. Med. Chem. 2004, 47, 5541-5554). All data sets have been studied using training and test sets to allow for both internal (q(2)) and external (r(2)(pred)) predictive ability assessment. CoMFA settings optimization was successful in developing models with improved q(2) and r(2)(pred) as compared to default CoMFA modeling. Optimized CoMFA is compared with comparative molecular similarity indices analysis (CoMSIA) and holographic quantitative structure-activity relationship (HQSAR) models and found to consistently produce models with improved or equivalent q(2) and r(2)(pred). The ability of settings optimization to improve model predictive ability has been validated using both internal and external predictions, and the risk of chance correlation has been evaluated using response variable randomization tests.

Journal Article↗

PubMedDate not supplied

POEM: Parameter Optimization using Ensemble Methods: application to target specific scoring functions.

In computational biology processes such as docking, binding, and folding are often described by simplified, empirical models. These models are fitted to physical properties of the process by adjustable parameters. An appropriate choice of these parameters is crucial for the quality of the models. Locating the best choices for the parameters is often is a difficult task, depending on the complexity of the model. We describe a new method and program, POEM (Parameter Optimization using Ensemble Methods), for this task. In POEM we combine the DOE (Design Of Experiment) procedure with ensembles of different regression methods. We apply the method to the optimization of target specific scoring functions in molecular docking. The method consists of an iterative procedure that uses alternate evaluation and prediction steps. During each cycle of optimization we fit an approximate function to a defined loss function landscape and improve the quality of this fit from cycle to cycle by constantly augmenting our data set. As test applications we fitted the FlexX and Screenscore scoring functions to the kinase and ATPase protein classes. The results are promising: Starting from random parameters we are able to locate parameter sets which show superior performance compared to the original values. The POEM approach converges quickly and the approximated loss function landscapes are smooth, thus making the approach a suitable method for optimizations on rugged landscapes.

Adenosine Triphosphatases↗

PubMed2000

Heterogeneous catalyst design using stochastic optimization algorithms

We describe the application of two stochastic optimization algorithms to heterogeneous catalyst design. In particular, we discuss the optimal design of a two-component catalyst for the diffusion limited A + B --> 0 and A + B2 --> 0 reactions in which each of the reactants are adsorbed specifically on one of the two distinct catalytic sites. The geometric arrangement of the catalytic sites that maximizes the catalyst activity is determined by the use of a genetic algorithm and a simulated annealing algorithm. In the case of the A + B --> 0 reaction, it is found that the catalyst surface with the optimal active site distribution, that of a checkerboard, is approximately 25% more active than a random site distribution. A similar increase in catalytic activity is obtained for the A + B2 --> 0 reaction. While both the genetic and simulated annealing algorithms obtain identical optimal solutions for a given reaction, the simulated annealing algorithm is shown to be more efficient.

Journal Article↗

PubMed2005

Multi objective optimization of the setup of a surfactant-enhanced DNAPL remediation.

Surfactant-enhanced aquifer remediation (SEAR) is widely considered a promising technique to remediate dense nonaqueous phase liquid (DNAPL) contaminations in-situ. The costs of a SEAR remediation are important and depend mostly on the setup of the remediation. Costs can be associated with the installation of injection and extraction wells, the required time of the remediation (and thus labor costs, lease of installations, and energy), the extracted water volume (the purification of the extracted water), and the injected surfactant amount. A cost-effective design of the remediation setup allows an optimal use of resources. In this work, a SEAR remediation was simulated for a hypothetical typical DNAPL contamination. A constrained multi-objective optimization of the model was applied to obtain a Pareto set of optimal remediation strategies with different weights for the two objectives of the remediation: (i) the maximal removal of DNAPL mass (ii) with a minimal total cost. A relatively sharp Pareto front was found, showing a considerable tradeoff between DNAPL removal and total remediation costs. These Pareto curves can help decision makers select an optimal remediation strategy in terms of cost and remediation efficiency depending on external constraints such as the available budget and obligatory remediation goals.

Computer Simulation↗

PubMed2006

Designing molecules by optimizing potentials.

The astronomical number of accessible discrete chemical structures makes rational molecular design extremely challenging. We formulate the design of molecules with specific tailored properties as performing a continuous optimization in the space of electron-nuclear attraction potentials. The optimization is facilitated by using a linear combination of atomic potentials (LCAP), a general framework that creates a continuous property landscape from an otherwise unlinked set of discrete molecular-property values. A demonstration of this approach is given for the optimization of molecular electronic polarizability and hyperpolarizability. We show that the optimal structures can be determined without enumerating and separately evaluating the characteristics of the combinatorial number of possible structures, a process that would be much slower. The LCAP approach may be used with quantum or classical Hamiltonians, suggesting possible applications to drug design and new materials discovery.

Journal Article↗

PubMed2006

Optimization of benzoxazinones as natural herbicide models by lipophilicity enhancement.

Benzoxazinones are plant allelochemicals well-known for their phytotoxic activity and for taking part in the defense strategies of Gramineae, Ranunculaceae, and Scrophulariceae plants. These properties, in addition to the recently optimized methodologies for their large-scale isolation and synthesis, have made some derivatives of natural products, 2,4-dihydroxy-(2H)-1,4-benzoxazin-3-(4H)-one (DIBOA) and its 7-methoxy analogue (DIMBOA), successful templates in the search for natural herbicide models. These new chemicals should be part of integrated methodologies for weed control. In ongoing research about the structure-activity relationships of benzoxazinones and the structural requirements for their phytotoxicity enhancement and after characterization of the optimal structural features, a new generation of chemicals with enhanced lipophilicity was developed. They were tested on selected standard target species and weeds in the search for the optimal aqueous solubility-lipophilicity rate for phytotoxicity. This physical parameter is known to be crucial in modern drug and agrochemical design strategies. The new compounds obtained in this way had interesting phytotoxicity profiles, empowering the phytotoxic effect of the starting benzoxazinone template in some cases. Quantitative structure-activity relationships were obtained by bioactivity-molecular parameters correlations. Because optimal lipophilicity values for phytotoxicity vary with the tested plant, these new derivatives constitute a more selective way to take advantage of benzoxazinone phytotoxic capabilities.

Agrochemicals↗

PubMed2007

Designing molecules with optimal properties using the linear combination of atomic potentials approach in an AM1 semiempirical framework.

The linear combination of atomic potentials (LCAP) approach is implemented in the AM1 semiempirical framework and is used to design molecular structures with optimized properties. The optimization procedure uses property derivative information to search molecular space and thus avoid direct enumeration and evaluation of each molecule in a library. Two tests are described: the optimization of first hyperpolarizabilities of substituted aromatics and the optimization of a figure of merit for n-type organic semiconductors.

Journal Article↗

PubMed1996

Preparation and evaluation of zidovudine-loaded sustained-release microspheres. 2. Optimization of multiple response variables.

The formulation of zidovudine-loaded (AZT-loaded) sustained-release microspheres was prepared and optimized using response surface methodology. The objective was to use this statistical procedure to obtain a formulation with optimized overall properties. The effect of formulation variables (emulsifier concentration, drug to polymer ratio, and composition of the internal phase of the emulsion) on a number of response variables was systemically investigated. The response variables were t85, entrapment efficiency, yield, and percentage of loose surface crystals. A desirability function that combines these four response variables was constructed. A second-order polynomial equation was fitted to the data, and the resulting equation was used to predict the responses in the optimal region. All the investigated response variable were found to be highly dependent on the formulation variables, with strong interactions observed between the formulation variables. It was found that optimum overall desirability of AZT microspheres can be obtained at low levels of SDS and ethyl acetate concentrations and at intermediate levels of drug to polymer ratio. An optimized formulation was prepared under these experimental conditions and evaluated for individual responses and overall desirability. The experimental values of the response variables highly agreed with the predicted values.

Chemistry, Pharmaceutical↗

PubMed1999

Optimal sequencing of urgent surgical cases. Scheduling cases using operating room information systems.

Optimal sequencing of urgent cases (i.e., selecting which urgent case should be performed first and which second) may enhance patient safety, increase patient satisfaction with timeliness of surgery, and minimize surgeons' complaints. Before determining the optimal sequence of urgent cases, an operating room (OR) suite must identify the primary scheduling objective to be satisfied when prioritizing pending urgent cases. These scheduling objectives may include: 1) perform the cases in the sequence that minimizes the average length of time each surgeon and patient waits; 2) perform the cases in the order that they were submitted; or 3) perform the cases based on medical priority, as prioritized by an OR director, or surgeons discussing the cases among themselves. We provide mathematical structure which can be used to program a computerized surgical services information system to assist in optimizing the sequence of urgent cases. We use an example to illustrate that the optimal sequence varies depending on the scheduling objective chosen.

Algorithms↗

PubMed2001

Multiple-point variance analysis for optimal adjustment of a monitoring network.

The selection of sampling sites is one of the major tasks in the design of a monitoring network. Many environmental networks suffer from either insufficient information or redundant information. This study presents a new, effective algorithm that addresses the issues of insufficient and reduction information. The new algorithm is denoted as Multiple-Point Variance Analysis (MPV). MPV includes both Multiple-Point Variance Reduction Analysis (MPVR) for adding information-effectives sites, and Multiple-Point Variance Increase Analysis (MPVI) for deleting information-redundant sites. The MPVR and MPVI equations are verified under two hypothetical cases. The optimal procedures of this new algorithm include determination of simultaneous additions or deletions of groups of sampling points. These proposed optimization procedures eliminate the need for any spatial discretizations or sequential selections. The efficiency of these optimal procedures is tested under actual field conditions. The results show that the optimal MPV is an effective tool for adjustment of existing sampling networks.

Algorithms↗

PubMed1999

Artificial neural network as a novel method to optimize pharmaceutical formulations.

One of the difficulties in the quantitative approach to designing pharmaceutical formulations is the difficulty in understanding the relationship between causal factors and individual pharmaceutical responses. Another difficulty is desirable formulation for one property is not always desirable for the other characteristics. This is called a multi-objective simultaneous optimization problem. A response surface method (RSM) has proven to be a useful approach for selecting pharmaceutical formulations. However, prediction of pharmaceutical responses based on the second-order polynomial equation commonly used in RSM, is often limited to low levels, resulting in poor estimations of optimal formulations. The aim of this review is to describe the basic concept of the multi-objective simultaneous optimization technique in which an artificial neural network (ANN) is incorporated. ANNs are being increasingly used in pharmaceutical research to predict the non-linear relationship between causal factors and response variables. The usefulness and reliability of this ANN approach is demonstrated by the optimization for ketoprofen hydrogel ointment as a typical numerical example, in comparison with the results obtained with a classical RSM approach.

Chemistry, Pharmaceutical↗