A simulation study comparing the impact of experimental error on the performance of experimental designs and artificial neural networks used for process screening

Many variables and their interactions can affect a biotechnological process. Testing a large number of variables and all their possible interactions is a cumbersome task and its cost can be prohibitive. Several screening strategies, with a relatively low number of experiments, can be used to find which variables have the largest impact on the process and estimate the magnitude of their effect. One approach for process screening is the use of experimental designs, among which fractional factorial and Plackett–Burman designs are frequent choices. Other screening strategies involve the use of artificial neural networks (ANNs). The advantage of ANNs is that they have fewer assumptions than experimental designs, but they render black-box models (i.e., little information can be extracted about the process mechanics). In this paper, we simulate a biotechnological process (fed-batch growth of bakers yeast) to analyze and compare the effect of random experimental errors of different magnitudes and statistical distributions on experimental designs and ANNs. Except for the situation in which the error has a normal distribution and the standard deviation is constant, it was not possible to determine a clear-cut rule for favoring one screening strategy over the other. Instead, we found that the data can be better analyzed using both strategies simultaneously.     

Field experiments to estimate optimum fertilizer levels

Summary Field experiments provide a basis for fertilizer recommendations. Some defects of an incomplete factorial scheme are discussed and the complete factorial briefly reviewed. Using the general polynomial of the second degree as the equation of a response surface, composite designs provide a more uniform precision for the coefficients. These designs are basically augmented 2^m factorials. One such design and a variation on it are compared. A method is given for calculating the economic optimal rates for the fertilizers.     

指数富集配基的系统进化原理及应用进展

过去十几年,指数富集配基的系统进化（SELEX）技术得到了充分的发展,目前已经成为在数量众多的靶分子中,高亲和性和特异性地确定适配子的一种崭新的方法.SELEX的流程即是人工合成随机寡核苷酸序列、适配子筛选、扩增、再循环的过程.在这一过程中,适配子修饰基团文库的应用使得SELEX流程更加经济、快捷、高通量.回顾了SELEX的研究和应用,期待着这一方法能够在理论和实践上得到进一步的发展.     

Designs Suited for Varietal Trials with Multiple Basals

DAS (1960) gave a method of construction of confounded balanced asymmetrical factorial designs of the type v × 2² by using BIB designs. In the present paper a method has been given for construction of balanced asymmetrical factorial designs of the type (v–t) × 2² by using truncated balanced incomplete block designs obtainable by omitting t treatments. Likewise, partially balanced asymmetrical factorial designs can also be obtained by omitting any particular treatment alongwith its first or second associate treatments from the v treatments of a PBIB design. We can get a large number of new designs not available in literature through this technique. These designs are well suited for varietal trials with multiple basals.     

A new efficient mixture screening design for optimization of media

Screening ingredients for the optimization of media is an important first step to reduce the many potential ingredients down to the vital few components. In this study, we propose a new method of screening for mixture experiments called the centroid screening design. Comparison of the proposed design with Plackett‐Burman, fractional factorial, simplex lattice design, and modified mixture design shows that the centroid screening design is the most efficient of all the designs in terms of the small number of experimental runs needed and for detecting high‐order interaction among ingredients. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Reduction in laboratory animal use by factorial design

Factorial experimental designs (FEDs) can be used to study the effects of controllable variables, such as an experimental treatment, sex, strain, age, diet and prior treatment of animals, on some defined response. Such designs have been widely used in optimising manufacturing processes, but have rarely been used in optimising animal experiments in drug discovery. FEDs generally provide more information than the alternative "one-variable-at-a-time" approach, because each animal contributes information on the effect of every factor, and because such designs can highlight any interactions among the variables. Although FEDs can have any number of factors and levels of each factor, where many factors are to be explored, it is common to do an initial experiment using two levels of each factor, and in some cases fractional factorial designs can be used to reduce the total number of treatment combinations to manageable levels. These designs have been used successfully at AstraZeneca in the optimisation of in vivo drug screening experiments, where their use has effectively reduced the numbers of animals used in some routine screens.     

Screening designs for drug development

We propose drug screening designs based on a Bayesian decision-theoretic approach. The discussion is motivated by screening designs for phase II studies. The proposed screening designs allow consideration of multiple treatments simultaneously. In each period, new treatments can arise and currently considered treatments can be dropped. Once a treatment is removed from the phase II screening trial, a terminal decision is made about abandoning the treatment or recommending it for a future confirmatory phase III study. The decision about dropping treatments from the active set is a sequential stopping decision. We propose a solution based on decision boundaries in the space of marginal posterior moments for the unknown parameter of interest that relates to each treatment. We present a Monte Carlo simulation algorithm to implement the proposed approach. We provide an implementation of the proposed method as an easy to use R library available for public domain download (http://www.stat.rice.edu/~rusi/ or http://odin.mdacc.tmc.edu/~pm/).     

A general mixture model approach for mapping quantitative trait loci from diverse cross designs involving multiple inbred lines

Most current statistical methods developed for mapping quantitative trait loci (QTL) based on inbred line designs apply to crosses from two inbred lines. Analysis of QTL in these crosses is restricted by the parental genetic differences between lines. Crosses from multiple inbred lines or multiple families are common in plant and animal breeding programmes, and can be used to increase the efficiency of a QTL mapping study. A general statistical method using mixture model procedures and the EM algorithm is developed for mapping QTL from various cross designs of multiple inbred lines. The general procedure features three cross design matrices, W, that define the contribution of parental lines to a particular cross and a genetic design matrix, D, that specifies the genetic model used in multiple line crosses. By appropriately specifying W matrices, the statistical method can be applied to various cross designs, such as diallel, factorial, cyclic, parallel or arbitrary-pattern cross designs with two or multiple parental lines. Also, with appropriate specification for the D matrix, the method can be used to analyse different kinds of cross populations, such as F2 backcross, four-way cross and mixed crosses (e.g. combining backcross and F2). Simulation studies were conducted to explore the properties of the method, and confirmed its applicability to diverse experimental designs.     

Use of a Fractional Factorial Design to Evaluate Interactions of Environmental Factors Affecting Biodegradation Rates

For investigation of main and interactive effects of six experimentally controlled environmental factors on phenol biodegradation in a shake-flask system, a largely neglected statistical procedure was applied. A major benefit resulting from the application of the orthogonal, fractional factorial design is that the number of experiments necessary to evaluate multifactor interactions is limited. In our investigation, the required number of experiments was reduced to 81 from the 324 necessary with conventional factorial designs; information was sacrificed for only 3 of 15 possible two-factor interactions. Six experimentally controlled factors were investigated at two or three treatment levels each; the six factors were (1) amount of phenol substrate, (2) amount of bacterial inoculum, (3) filtration of inoculum, (4) type of basal salts medium, (5) initial pH of basal salts medium, and (6) flask closure. Significant main effects were found for factors 1, 2, and 4; whereas significant interactive effects were found only for factor 2 with factor 3 and for factor 2 with factor 5. Our results suggest that the application of these statistical designs will greatly reduce the number of experiments necessary to evaluate multifactor effects on degradation rates during optimization of both hazard screening systems and waste treatment systems.     

Application of fractional factorial designs to screen active factors for antibody production by chinese hamster ovary cells

With ever increasing need for cost-effective large-scale production of monoclonal antibodies, it is essential to develop highly productive and commercially viable processes. Previous research showed that growth and production capacity of the culture media can be improved by micronutrient supplements, such as insulin, vitamins, and growth factors. Since these micronutrients may not act independently of one another, factorial designs can expose critical interactions between nutrients as opposed to a serial approach of changing one factor at a time. In this study, fractional factorial designs were applied to observe the effect of several micronutrients on antibody production and culture longevity in shake flasks. Response surface designs were used to investigate the factors in depth and confirm the results of the fractional factorial study. The results demonstrate that fractional factorial design is an effective tool for rapid development of antibody-producing Chinese hamster ovary (CHO) cells.     

Modeling and optimization of fermentative hydrogen production

Biohydrogen is a sustainable energy resource due to its potentially higher efficiency of conversion to usable power, non-polluting nature and high energy density. The purpose of modeling and optimization is to improve, analyze and predict biohydrogen production. Biohydrogen production depends on a number of variables, including pH, temperature, substrate concentration and nutrient availability, among others. Mathematical modeling of several distinct processes such as kinetics of microbial growth and products formation, steady state behavior of organic substrate along with its utilization and inhibition have been presented. Present paper summarizes the experimental design methods used to investigate effects of various factors on fermentative hydrogen production, including one-factor-at-a-time design, full factorial and fractional factorial designs. Each design method is briefly outlined, followed by the introduction of its analysis. In addition, the applications of artificial neural network, genetic algorithm, principal component analysis and optimization process using desirability function have also been highlighted.     

Response surface designs for experiments in bioprocessing

Many processes in the biological industries are studied using response surface methodology. The use of biological materials, however, means that run-to-run variation is typically much greater than that in many experiments in mechanical or chemical engineering and so the designs used require greater replication. The data analysis which is performed may involve some variable selection, as well as fitting polynomial response surface models. This implies that designs should allow the parameters of the model to be estimated nearly orthogonally. A class of three-level response surface designs is introduced which allows all except the quadratic parameters to be estimated orthogonally, as well as having a number of other useful properties. These subset designs are obtained by using two-level factorial designs in subsets of the factors, with the other factors being held at their middle level. This allows their properties to be easily explored. Replacing some of the two-level designs with fractional replicates broadens the class of useful designs, especially with five or more factors, and sometimes incomplete subsets can be used. It is very simple to include a few two- and four-level factors in these designs by excluding subsets with these factors at the middle level. Subset designs can be easily modified to include factors with five or more levels by allowing a different pair of levels to be used in different subsets.     

Impact of mating design on selection response in Brassica rapa L.

The impact of four mating designs on selection response for leaf area was assessed at four different population sizes, using fast-cycling Brassica rapa L. Mating designs were either balanced (partial diallel or pair mating) or unbalanced (factorial mating designs with either one or two testers). When balanced, the mating designs required different numbers of crossings for the same number of parents: the partial diallel design, in the configuration retained here, required three times as many crossings as pair mating. Population sizes were 4, 8, 16, and 32. The percentage of selected individuals was kept constant at 25%. Despite an average estimated heritability around 0.4, the overall response to selection after five generations was fairly weak in all three replicates. For a given population size, selection response was larger under balanced mating designs than under unbalanced ones. There was no difference among balanced mating designs. Both results indicate that effective population size is more important than population size or the number of crossings in maintaining genetic gain.     

Closure procedures for monotone bi-factorial dose-response designs

Summary.   Two goals of multiple-dose factorial trials are (i) demonstrating improved effectiveness of a fixed combination over each of its components as well as (ii) identifying a safe and effective dose range. The authors address both goals though with focus on the second by closure procedures that guarantee strong control of the familywise error rate. Two different families of null hypotheses are investigated for bi-factorial dose–response designs that are monotone with respect to the matrix partial order. One is suitable to find the minimum effective dose(s) and the other one is large enough to identify the highest effective dose step(s). Likelihood ratio tests and appropriate multiple contrast tests are applied to an unbalanced clinical trial example taken from Hung (2000, Statistics in Medicine 19, 2079–2087). Full computer code written in the R language is available from the Internet.     

Design,Optimization, and Evaluation of Lurasidone Hydrochloride Nanocrystals

The present investigation was carried out to design, optimize, and evaluate lurasidone hydrochloride nanocrystals for improving its solubility and dissolution characteristics. Nanocrystals were prepared by media milling technique using zirconium oxide beads with 0.1 mm diameter. Various stabilizers, viz. poloxamer 188, PVP K30, SLS, HPMC E15, and PVP S 630 D, were evaluated to stabilize the nanocrystals. The Pareto chart obtained through Plackett-Burman screening design revealed that HPMC E 15 showed the highest standardized effect (p value <0.05) on percent dissolution efficiency at 2 min. In subsequent studies, a 3² factorial design was employed to quantify the effect of two independent variables, namely amount of stabilizer and milling time on predetermined response variables mean particle size, saturation solubility, and percent dissolution efficiency at 2 min. Statistical analysis of the factorial design revealed that all predetermined response variables were significantly dependent (p value <0.05) on the independent variables. The observed response of the optimized batch prepared as per the desirability function was in close agreement with predicted response, and mathematical model generated was validated. The optimized batch was lyophilized, and X-ray powder diffraction studies indicated that there was no substantial change in crystallinity of the drug. The optimized formulation showed mean particle size of 228 nm and released almost all the drug within first 5 min. Since the crystallinity of the drug is maintained, improvement in saturation solubility and dissolution efficiency could be attributed to decrease in mean particle size of the drug.     

Group screening for rare events based on incomplete block designs

Fields such as, diagnostic testing, biotherapeutics, drug development, and toxicology among others, center on the premise of searching through many specimens for a rare event. Scientists in the business of “searching for a needle in a haystack” may greatly benefit from the use of group screening design strategies. Group screening, where specimens are composited into pools with each pool being tested for the presence of the event, can be much more cost-efficient than testing each individual specimen. A number of group screening designs have been proposed in the literature. Incomplete block screening designs are described here and compared with other group screening designs. It is shown under certain conditions, that incomplete block screening designs can provide nearly a 90% cost saving compared to other group screening designs such as when prevalence is 0.001 and screening 3876 specimens with an ICB-sequential design vs. a Dorfman design. In other cases, previous group screening designs are shown to be most efficient. Overall, when prevalence is small (≤0.05) group screening designs are shown to be quite cost effective at screening a large number of specimens and in general there is no one design that is best in all situations. © 2018 American Institute of Chemical Engineers Biotechnol Progress, 35: e2770, 2019.     

Fractional factorial design optimization of the separation of pilocarpine and its degradation products by capillary electrophoresis

The separation of pilocarpine and its degradation products by micellar electrokinetic capillary chromatography (MECC) has been optimized by using fractional factorial design of the experiments. Critical parameters were identified in a screening design, and an optimization design was used to optimize the separation. The optimal separation method was based on a borate buffer with sodium dodecyl sulfate (SDS). It is concluded that by using fractional factorial design it is possible to improve the separation of pilocarpine, it trans epimer, isopilocarpine and their hydrolysis products, pilocarpic acid and isopilocarpic acid.     

Use of uniform designs in combination with neural networks for viral infection process development

This work aimed to compare the predictive capacity of empirical models, based on the uniform design utilization combined to artificial neural networks with respect to classical factorial designs in bioprocess, using as example the rabies virus replication in BHK‐21 cells. The viral infection process parameters under study were temperature (34°C, 37°C), multiplicity of infection (0.04, 0.07, 0.1), times of infection, and harvest (24, 48, 72 hours) and the monitored output parameter was viral production. A multilevel factorial experimental design was performed for the study of this system. Fractions of this experimental approach (18, 24, 30, 36 and 42 runs), defined according uniform designs, were used as alternative for modelling through artificial neural network and thereafter an output variable optimization was carried out by means of genetic algorithm methodology. Model prediction capacities for all uniform design approaches under study were better than that found for classical factorial design approach. It was demonstrated that uniform design in combination with artificial neural network could be an efficient experimental approach for modelling complex bioprocess like viral production. For the present study case, 67% of experimental resources were saved when compared to a classical factorial design approach. In the near future, this strategy could replace the established factorial designs used in the bioprocess development activities performed within biopharmaceutical organizations because of the improvements gained in the economics of experimentation that do not sacrifice the quality of decisions. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:532–540, 2015     

Estimating effects of a single gene and polygenes on quantitative traits from a diallel design

A genetic model is developed with additive and dominance effects of a single gene and polygenes as well as general and specific reciprocal effects for the progeny from a diallel mating design. The methods of ANOVA, minimum norm quadratic unbiased estimation (MINQUE), restricted maximum likelihood estimation (REML), and maximum likelihood estimation (ML) are suggested for estimating variance components, and the methods of generalized least squares (GLS) and ordinary least squares (OLS) for fixed effects, while best linear unbiased prediction, linear unbiased prediction (LUP), and adjusted unbiased prediction are suggested for analyzing random effects. Monte Carlo simulations were conducted to evaluate the unbiasedness and efficiency of statistical methods involving two diallel designs with commonly used sample sizes, 6 and 8 parents, with no and missing crosses, respectively. Simulation results show that GLS and OLS are almost equally efficient for estimation of fixed effects, while MINQUE (1) and REML are better estimators of the variance components and LUP is most practical method for prediction of random effects. Data from a Drosophila melanogaster experiment (Gilbert 1985a, Theor appl Genet 69:625–629) were used as a working example to demonstrate the statistical analysis. The new methodology is also applicable to screening candidate gene(s) and to other mating designs with multiple parents, such as nested (NC Design I) and factorial (NC Design II) designs. Moreover, this methodology can serve as a guide to develop new methods for detecting indiscernible major genes and mapping quantitative trait loci based on mixture distribution theory. The computer program for the methods suggested in this article is freely available from the authors.     

Statistical Screening of Medium Components for Recombinant Production of Pseudomonas aeruginosa ATCC 9027 Rhamnolipids by Nonpathogenic Cell Factory Pseudomonas putida KT2440

Rhamnolipids (RLs) produced by the opportunistic human pathogen Pseudomonas aeruginosa are considered as potential candidates for the next generation of surfactants. Large-scale production of RLs depends on progress in strain engineering, medium design, operating strategies, and purification procedures. In this work, the rhlAB genes extracted from a mono_RLs_producing strain of P. aeruginosa (ATCC 9027) were introduced to an appropriate safety host Pseudomonas putida KT2440. The capability of the recombinant strain was evaluated in various media. As a prerequisite for optimal medium design, a set of 32 experiments was performed in two steps for screening a number of macro-nutritional compounds. In the experiments, a two-level fractional factorial design resolution IV was followed by a two-level full factorial one. By means of this approach, it was observed that glycerol, yeast extract, and peptone have significant positive influence on recombinant RLs production while the yeast extract/peptone two-factor and glycerol/yeast extract/peptone three-factor interactions have considerable negative effects. A wide range of variation from 0 to 570 mg/l was obtained for RLs production during the screening experiments indicating the importance of medium optimization. The results point out the opportunity for possible higher yields of RLs through further screening, mixture/combined mixture designs, and high-cell-density cultivations.