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     

Computational design of novel protein–protein interactions – An overview on methodological approaches and applications

Protein–protein interactions (PPIs) govern numerous cellular functions in terms of signaling, transport, defense and many others. Designing novel PPIs poses a fundamental challenge to our understanding of molecular interactions. The capability to robustly engineer PPIs has immense potential for the development of novel synthetic biology tools and protein-based therapeutics. Over the last decades, many efforts in this area have relied purely on experimental approaches, but more recently, computational protein design has made important contributions. Template-based approaches utilize known PPIs and transplant the critical residues onto heterologous scaffolds. De novo design instead uses computational methods to generate novel binding motifs, allowing for a broader scope of the sites engaged in protein targets. Here, we review successful design cases, giving an overview of the methodological approaches used for templated and de novo PPI design.     

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.     

The application of MANOVA to analyse <Emphasis Type="Italic">Arabidopsis thaliana</Emphasis> metabolomic data from factorially designed experiments

Metabolomic technologies produce complex multivariate datasets and researchers are faced with the daunting task of extracting information from these data. Principal component analysis (PCA) has been widely applied in the field of metabolomics to reduce data dimensionality and for visualising trends within the complex data. Although PCA is very useful, it cannot handle multi-factorial experimental designs and, often, clear trends of biological interest are not observed when plotting various PC combinations. Even if patterns are observed, PCA provides no measure of their significance. Multivariate analysis of variance (MANOVA) applied to these PCs enables the statistical evaluation of main treatments and, more importantly, their interactions within the experimental design. The power and scope of MANOVA is demonstrated through two different factorially designed metabolomic investigations using Arabidopsis ethylene signalling mutants and their wild-type. One investigation has multiple experimental factors including challenge with the economically important pathogen Botrytis cinerea and also replicate experiments, while the second has different sample preparation methods and one level of replication ‘nested’ within the design. In both investigations there are specific factors of biological interest and there are also factors incorporated within the experimental design, which affect the data. The versatility of MANOVA is displayed by using data from two different metabolomic techniques; profiling using direct injection mass spectroscopy (DIMS) and fingerprinting using fourier transform infra-red (FT-IR) spectroscopy. MANOVA found significant main effects and interactions in both experiments, allowing a more complete and comprehensive interpretation of the variation within each investigation, than with PCA alone. Canonical variate analysis (CVA) was applied to investigate these effects and their biological significance. In conclusion, the application of MANOVA followed by CVA provided extra information than PCA alone and proved to be a valuable statistical addition in the overwhelming task of analysing metabolomic data.     

Urinary proteomics: a tool to discover biomarkers of kidney diseases

There is intense interest in applying proteomics to urine analysis in order to promote a better understanding of kidney disease processes, develop new biomarkers for diagnosis and detect early factors that contribute to end–stage renal diseases. This interest creates numerous opportunities as well as challenges. To fulfill this task, proteomics requires, in its different stages of realization, various technological platforms with high sensitivity, high throughput and large automation ability. In this review, we will give an overview of promising proteomic methods that can be used for analyzing urinary proteome and detecting biomarkers for different kidney diseases. Furthermore, we will focus on the current status and future directions in investigating kidney diseases using urinary proteomics.     

Use of factorial designs to optimize animal experiments and reduce animal use

Optimization of experiments, such as those used in drug discovery, can lead to useful savings of scientific resources. Factors such as sex, strain, and age of the animals and protocol-specific factors such as timing and methods of administering treatments can have an important influence on the response of animals to experimental treatments. Factorial experimental designs can be used to explore which factors and what levels of these factors will maximize the difference between a vehicle control and a known positive control treatment. This information can then be used to design more efficient experiments, either by reducing the numbers of animals used or by increasing the sensitivity so that smaller biological effects can be detected. A factorial experimental design approach is more effective and efficient than the older approach of varying one factor at a time. Two examples of real factorial experiments reveal how using this approach can potentially lead to a reduction in animal use and savings in financial and scientific resources without loss of scientific validity.     

Factorial and time course designs for cDNA microarray experiments

Microarrays are powerful tools for surveying the expression levels of many thousands of genes simultaneously. They belong to the new genomics technologies which have important applications in the biological, agricultural and pharmaceutical sciences. There are myriad sources of uncertainty in microarray experiments, and rigorous experimental design is essential for fully realizing the potential of these valuable resources. Two questions frequently asked by biologists on the brink of conducting cDNA or two-colour, spotted microarray experiments are 'Which mRNA samples should be competitively hybridized together on the same slide?' and 'How many times should each slide be replicated?' Early experience has shown that whilst the field of classical experimental design has much to offer this emerging multi-disciplinary area, new approaches which accommodate features specific to the microarray context are needed. In this paper, we propose optimal designs for factorial and time course experiments, which are special designs arising quite frequently in microarray experimentation. Our criterion for optimality is statistical efficiency based on a new notion of admissible designs; our approach enables efficient designs to be selected subject to the information available on the effects of most interest to biologists, the number of arrays available for the experiment, and other resource or practical constraints, including limitations on the amount of mRNA probe. We show that our designs are superior to both the popular reference designs, which are highly inefficient, and to designs incorporating all possible direct pairwise comparisons. Moreover, our proposed designs represent a substantial practical improvement over classical experimental designs which work in terms of standard interactions and main effects. The latter do not provide a basis for meaningful inference on the effects of most interest to biologists, nor make the most efficient use of valuable and limited resources.     

基因设计对重组蛋白表达的影响研究进展

利用异源表达系统生产重组蛋白已成为现代基因工程和生物工程研究热点和重点。但是研究者发现并非所有的基因都能在异源宿主中高效表达,除了宿主、分泌途径、启动子等因素外,基因自身的序列也蕴含了多种影响蛋白表达的因素,如密码子偏爱性,密码子对偏爱性,GC含量,mRNA二级结构,mRNA稳定性等。从基因设计的角度对影响蛋白表达的因素和方法进行了综述,尤其是对密码子优化和密码子对优化,详细讨论了与传统基因优化理念截然不同的密码子协调化及密码子对协调化等最新进展。     

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     

Strategies for developing design spaces for viral clearance by anion exchange chromatography during monoclonal antibody production

The quality‐by‐design (QbD) regulatory initiative promotes the development of process design spaces describing the multidimensional effects and interactions of process variables on critical quality attributes of therapeutic products. However, because of the complex nature of production processes, strategies must be devised to provide for design space development with reasonable allocation of resources while maintaining highly dependable results. Here, we discuss strategies for the determination of design spaces for viral clearance by anion exchange chromatography (AEX) during purification of monoclonal antibodies. We developed a risk assessment for AEX using a formalized method and applying previous knowledge of the effects of certain variables and the mechanism of action for virus removal by this process. We then use design‐of‐experiments (DOE) concepts to perform a highly fractionated factorial experiment and show that varying many process parameters simultaneously over wide ranges does not affect the ability of the AEX process to remove endogenous retrovirus‐like particles from CHO‐cell derived feedstocks. Finally, we performed a full factorial design and observed that a high degree of viral clearance was obtained for three different model viruses when the most significant process parameters were varied over ranges relevant to typical manufacturing processes. These experiments indicate the robust nature of viral clearance by the AEX process as well as the design space where removal of viral impurities and contaminants can be assured. In addition, the concepts and methodology presented here provides a general approach for the development of design spaces to assure that quality of biotherapeutic products is maintained. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Determination of experimental domain factors of polyphenols,phenolic acids and flavonoids of lemon (Citrus limon) peel using two-level factorial design

This study aimed to evaluate the significant extraction factors in achieving higher recovery yield of total polyphenols, phenolic acids and flavonoids content from Citrus limon peel using two-level factorial design. The effect of five independent factors including drying temperature (40–60 °C), methanol concentration (20–60%), extraction temperature (28–60 °C), extraction time (30–60 min) and storage duration (0–14 days) were evaluated. Among all the examined factors, results showed that drying temperature, storage duration and extraction temperature were the most significant and contributing factors affecting the total polyphenols, phenolic acids and flavonoids content of lemon peel at P < 0.05. On the contrary, methanol concentration and extraction time exhibited the least significant and contribution at P greater than 0.05. In conclusion, the experimental domain factors were successfully obtained from this experiment, Therefore, further study on optimization of the obtained factors will be conducted in the future study using response surface methodology.     

Recent advances on conversion and co-production of acetone-butanol-ethanol into high value-added bioproducts

Butanol is an important bulk chemical and has been regarded as an advanced biofuel. Large-scale production of butanol has been applied for more than 100 years, but its production through acetone–butanol–ethanol (ABE) fermentation process by solventogenic Clostridium species is still not economically viable due to the low butanol titer and yield caused by the toxicity of butanol and a by-product, such as acetone. Renewed interest in biobutanol as a biofuel has spurred technological advances to strain modification and fermentation process design. Especially, with the development of interdisciplinary processes, the sole product or even the mixture of ABE produced through ABE fermentation process can be further used as platform chemicals for high value added product production through enzymatic or chemical catalysis. This review aims to comprehensively summarize the most recent advances on the conversion of acetone, butanol and ABE mixture into various products, such as isopropanol, butyl-butyrate and higher-molecular mass alkanes. Additionally, co-production of other value added products with ABE was also discussed.     

大尺度景观规划设计的思想与实践——荷兰风景园林师瑞克·德·菲索专访

瑞克·德·菲索先生从他在瓦格宁根度过的学生时代便积累了对大尺度项目的浓厚兴趣,从业多年,他参与并主持了多项重要的大型景观项目,包括马肯湖—瓦登海项目、弗和米尔圩田项目等。通过实践,他展示了自然过程和人类的需求在大尺度项目中很好地整合在一起。他的主要工作方法包括叠图分析、与不同学科背景的专家合作等。此外,他还指出,除了设计的品质,与决策者的沟通也是项目成功的关键。     

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.     

Selection of best conditions of inoculum preparation for optimum performance of the pigment production process by Talaromyces spp. using the Taguchi method

Process optimisation techniques increasingly need to be used early on in research and development of processes for new ingredients. There are different approaches and this article illustrates the main issues at stake with a method that is an industry best practice, the Taguchi method, suggesting a procedure to assess the potential impact of its drawbacks. The Taguchi method has been widely used in various industrial sectors because it minimises the experimental requirements to define an optimum region of operation, which is particularly relevant when minimising variability is a target. However, it also has drawbacks, especially the intricate confoundings generated by the experimental designs used. This work reports a process optimisation of the synthesis of red pigments by a fungal strain, Talaromyces spp. using the Taguchi methodology and proposes an approach to assess from validation trials whether the conclusions can be accepted with confidence. The work focused on optimising the inoculum characteristics, and the studied factors were spore age and concentration, agitation speed and incubation time. It was concluded that spore age was the most important factor for both responses, with optimum results at 5 days old, with the best other conditions being spores concentration, 100,000 (spores/mL); agitation, 200 rpm; and incubation time, 84 h. The interactive effects can be considered negligible and therefore this is an example where a simple experimental design approach was successful in speedily indicating conditions able to increase pigment production by 63% compared to an average choice of settings. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:621–632, 2017     

Systematic comparison of the empirical and factorial methods used to estimate the nutrient requirements of growing pigs

Empirical and factorial methods are currently used to estimate nutrient requirements for domestic animals. The purpose of this study was to estimate the nutrient requirements of a given pig population using the empirical and factorial methods; to establish the relationship between the requirements estimated with these two methods; and to study the limitations of the methods when used to determine the level of a nutrient needed to optimize individual and population responses of growing pigs. A systematic analysis was carried out on optimal lysine-to-net-energy (Lys : NE) ratios estimated by the empirical and factorial methods using a modified InraPorc® growth model. Sixty-eight pigs were individually simulated based on detailed experimental data. In the empirical method, population responses were estimated by feeding pigs with 11 diets of different Lys : NE ratios. Average daily gain and feed conversion ratio were the chosen performance criteria. These variables were combined with economic information to estimate the economic responses. In the factorial method, the Lys : NE ratio for each animal was estimated by model inversion. Optimal Lys : NE ratios estimated for growing pigs (25 to 105 kg) differed between the empirical and the factorial method. When the average pig is taken to represent a population, the factorial method does not permit estimation of the Lys : NE ratio that maximizes the response of heterogeneous populations in a given time or weight interval. Although optimal population responses are obtained by the empirical method, the estimated requirements are fixed and cannot be used for other growth periods or populations. This study demonstrates that the two methods commonly used to estimate nutrient requirements provide different nutrient recommendations and have important limitations that should be considered when the goal is to optimize the response of individuals or pig populations.     

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies

As cognitive neuroscience methods develop, established experimental tasks are used with emerging brain imaging modalities. Here transferring a paradigm (the visual oddball task) with a long history of behavioral and electroencephalography (EEG) experiments to a functional magnetic resonance imaging (fMRI) experiment is considered. The aims of this paper are to briefly describe fMRI and when its use is appropriate in cognitive neuroscience; illustrate how task design can influence the results of an fMRI experiment, particularly when that task is borrowed from another imaging modality; explain the practical aspects of performing an fMRI experiment. It is demonstrated that manipulating the task demands in the visual oddball task results in different patterns of blood oxygen level dependent (BOLD) activation. The nature of the fMRI BOLD measure means that many brain regions are found to be active in a particular task. Determining the functions of these areas of activation is very much dependent on task design and analysis. The complex nature of many fMRI tasks means that the details of the task and its requirements need careful consideration when interpreting data. The data show that this is particularly important in those tasks relying on a motor response as well as cognitive elements and that covert and overt responses should be considered where possible. Furthermore, the data show that transferring an EEG paradigm to an fMRI experiment needs careful consideration and it cannot be assumed that the same paradigm will work equally well across imaging modalities. It is therefore recommended that the design of an fMRI study is pilot tested behaviorally to establish the effects of interest and then pilot tested in the fMRI environment to ensure appropriate design, implementation and analysis for the effects of interest.     

Nonorthogonal tight-binding model with H–C–N–O parameterisation

A parametric nonorthogonal tight-binding model (NTBM1) with the set of parameters for H–C–N–O systems is presented. This model compares well with widely used semi-empirical AM1 and PM3/PM7 models but contains less fitting parameters per atom. All NTBM1 parameters are derived based on a criterion of the best agreement between the calculated and experimental values of bond lengths, valence angles and binding energies for various H–C–N–O molecules. Results for more than 200 chemical compounds are reported. Parameters are currently available for hydrogen, carbon, nitrogen, oxygen atoms and corresponding interatomic interactions. The model has a good transferability and can be used for both relaxation of large molecular systems (e.g., high-molecular compounds or covalent cluster complexes) and long-timescale molecular dynamics simulation (e.g., modelling of thermal decomposition processes). The program package based on this model is available for download at no cost from http://ntbm.info.