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.     

Quantitative screening of embryonic stem cell differentiation: endoderm formation as a model

Embryonic stem (ES) cells have attracted much attention as a possible source of functional cells for regenerative medicine. Therapeutic use of ES cells requires control over the types and frequencies of cells generated during their in vitro differentiation. Due to the complexity of factors that impact upon ES cell differentiation, novel approaches for the optimization of tissue-specific development are required. This motivates our use of factorial and composite design methods to make empirical investigations more efficient, and to reveal unexpected interactions missed by conventional dose-response analysis. Factorial experiments would benefit from the high content evaluation of a large number of test conditions, necessitating the development of a quantitative screening technology (QST) capable of reporting the absolute number and frequency of target cells. We have developed and validated such a technology for ES cell differentiation analysis using automated fluorescence microscopy, employing endoderm differentiation as a model system. To test this platform, a two-level factorial experiment was carried out to identify major and interactive effects of glucose, insulin, retinoic acid (RA), basic fibroblast growth factor (bFGF), and epidermal growth factor (EGF) on endoderm formation. RA was found to have inhibitory effects on endoderm formation, while low glucose proved beneficial. QST was demonstrated to be a powerful tool to study factors impacting endoderm-specific ES cell differentiation, and should be applicable to the analysis of a range of ES cell-derived tissues.     

A cross-system synthesis of consumer and nutrient resource control on producer biomass

Nutrient availability and herbivory control the biomass of primary producer communities to varying degrees across ecosystems. Ecological theory, individual experiments in many different systems, and system-specific quantitative reviews have suggested that (i) bottom-up control is pervasive but top-down control is more influential in aquatic habitats relative to terrestrial systems and (ii) bottom-up and top-down forces are interdependent, with statistical interactions that synergize or dampen relative influences on producer biomass. We used simple dynamic models to review ecological mechanisms that generate independent vs. interactive responses of community-level biomass. We calibrated these mechanistic predictions with the metrics of factorial meta-analysis and tested their prevalence across freshwater, marine and terrestrial ecosystems with a comprehensive meta-analysis of 191 factorial manipulations of herbivores and nutrients. Our analysis showed that producer community biomass increased with fertilization across all systems, although increases were greatest in freshwater habitats. Herbivore removal generally increased producer biomass in both freshwater and marine systems, but effects were inconsistent on land. With the exception of marine temperate rocky reef systems that showed positive synergism of nutrient enrichment and herbivore removal, experimental studies showed limited support for statistical interactions between nutrient and herbivory treatments on producer biomass. Top-down control of herbivores, compensatory behaviour of multiple herbivore guilds, spatial and temporal heterogeneity of interactions, and herbivore-mediated nutrient recycling may lower the probability of consistent interactive effects on producer biomass. Continuing studies should expand the temporal and spatial scales of experiments, particularly in understudied terrestrial systems; broaden factorial designs to manipulate independently multiple producer resources (e.g. nitrogen, phosphorus, light), multiple herbivore taxa or guilds (e.g. vertebrates and invertebrates) and multiple trophic levels; and - in addition to measuring producer biomass - assess the responses of species diversity, community composition and nutrient status.     

Meloidogyne incognita Inoculum Source Affects Host Suitability and Growth of Yellow Nutsedge and Chile Pepper

Meloidogyne incognita (Mi) reproduction and host plant responses in chile pepper (Capsicum annuum) and yellow nutsedge (Cyperus esculentus = YNS) to three sources of inoculum obtained by rearing a single Mi population on chile, YNS, and tomato were evaluated in two factorial greenhouse experiments. The interactive effects of Mi inoculum source and crop-weed competition were determined. In the absence of YNS competition, chile growth was reduced less by Mi inoculum from chile than by inoculum from YNS or tomato. When YNS was present, chile root weight was not affected and shoot weight increased with Mi initial inoculation, regardless of inoculum source. Chile plants inoculated with Mi from tomato exhibited double the nematode reproduction observed with inoculum from chile or YNS. With chile present, Mi reproduction on YNS was nearly three times greater with inoculum from tomato, but reproduction was similar among inoculum sources when chile was absent. Reductions in YNS root mass due to competition from chile failed to reduce the total number of Mi eggs produced on YNS plants. Differences in total Mi reproduction among inoculum sources were not attributable to differences in root growth or plant competition. This study illustrates the influence of Mi-YNS interactions and previous hosts on severity of Mi infection.     

Interactive effects of global change factors on soil respiration and its components: a meta‐analysis

As the second largest carbon (C) flux between the atmosphere and terrestrial ecosystems, soil respiration (Rs) plays vital roles in regulating atmospheric CO₂ concentration ([CO₂]) and climatic dynamics in the earth system. Although numerous manipulative studies and a few meta‐analyses have been conducted to determine the responses of Rs and its two components [i.e., autotrophic (Ra) and heterotrophic (Rh) respiration] to single global change factors, the interactive effects of the multiple factors are still unclear. In this study, we performed a meta‐analysis of 150 multiple‐factor (≥2) studies to examine the main and interactive effects of global change factors on Rs and its two components. Our results showed that elevated [CO₂] (E), nitrogen addition (N), irrigation (I), and warming (W) induced significant increases in Rs by 28.6%, 8.8%, 9.7%, and 7.1%, respectively. The combined effects of the multiple factors, EN, EW, DE, IE, IN, IW, IEW, and DEW, were also significantly positive on Rs to a greater extent than those of the single‐factor ones. For all the individual studies, the additive interactions were predominant on Rs (90.6%) and its components (≈70.0%) relative to synergistic and antagonistic ones. However, the different combinations of global change factors (e.g., EN, NW, EW, IW) indicated that the three types of interactions were all important, with two combinations for synergistic effects, two for antagonistic, and five for additive when at least eight independent experiments were considered. In addition, the interactions of elevated [CO₂] and warming had opposite effects on Ra and Rh, suggesting that different processes may influence their responses to the multifactor interactions. Our study highlights the crucial importance of the interactive effects among the multiple factors on Rs and its components, which could inform regional and global models to assess the climate–biosphere feedbacks and improve predictions of the future states of the ecological and climate systems.     

Optimization of Dimethoate Degradation by Brevundimonas sp. MCM B-427 Using Factorial Design: Studies on Interactive Effects of Environmental Factors

Microbial cultures were isolated from soil exposed to dimethoate, an organophosphorus insecticide. The isolate that showed maximum degradation of dimethoate was identified as Brevundimonassp. MCM B-427. The curve relating biomass accumulation and degradation revealed that aeration appeared to enhance growth rather than degradation. For optimization of environmental factors, a 2⁴ factorial experimental design was used, wherein four factors namely pH, temperature, inoculum density and aeration condition were varied simultaneously. The interaction between the factors was analyzed using MINITAB package. Degradation of dimethoate was affected by a large number of factors interacting in a complex way. Six two-way and two three-way interactions of various environmental factors were found to be statistically significant. In all the two-way interactions, the effect of one factor was more pronounced when the other was at its optimum. The three-way interactions revealed that optimization of pH, inoculum size and aeration was more critical at 40 °C than at 30 °C.The results imply the importance of studying interactions of parameters rather than optimizing individual parameters one by one. These results demonstrate complexity of the interaction and thereby imply the need for better experimental and statistical models.     

Fractional Plans to Estimate Main Effects and Two Factor Interactions Inclusive of a Specific Factor

Balanced incomplete block designs are used to construct non‐geometric 2ⁿ fractional factorial plans to estimate all n main effects and n – 1, 2 factor interactions with a specific factor included in each interaction. When the balanced incomplete block design is of Family (A), the resulting fractional factorial plan has the same number of runs as a fold‐over Hadamard matrix giving same variances for the estimates; however, some new designs are shown to be non‐isomorphic to the fold‐over Hadamard matrix plans.     

Optimization of the Salmonella/mammalian microsome assay for urine mutagenesis by experimental designs

Assessing urine mutagenicity with the Salmonella mutagenicity test is often limited by the volumes of the samples. Optimization of the assay was performed with factorial and Doehlert designs. Two fractional factorial designs 2^3-1 (3 factors, 4 experiments) were used to estimate the main effects of the percent S9 in the mix, the time of liquid incubation, the inoculum size and the growth conditions. A Doehlert design (3 factors, 13 experiments) was used to study the main effects and the interactions of the NADP, G6P and S9 in the mix. The positive markers were benzo[a]pyrene (BaP, 0.3 μg/plate) and a pool of smokers' urine (SU, 1.25 ml equivalent/plate). The response was limited to the induction factor (IF, number of induced revertants/number of spontaneous revertants) with Salmonella typhimurium TA98. The optimal conditions for BaP were: a 60 min period of liquid incubation and a volume of 0.1 ml (approx. 10⁸ cells/plate) of an overnight culture grown in 50 ml of Nutrient Broth No. 2 from a 250 ml flask. The S9 mix (0.1 ml, final volume) included 1.5% of S9, 1.0 mM NADP and 4.4 mM G6P. The maximal IF was 15.79. The optimal conditions for SU were: a 60 min period of liquid incubation and a volume of 0.1 ml (approx. 10⁸ cells/plate) of an overnight culture grown in 7 ml of Nutrient Broth No. 2 from a 20 × 180 mm tube. The S9 mix (0.1 ml, final volume) included: 4% S9, 4.2 mM NADP and 5.2 mM G6P. The maximal I7F was 10.95. These optimal conditions did not modify the spontaneous frequencies of the tester strains: TA97a, TA98, TA100 and TA102. The dose-response curves of mutagenic urine samples were found to be non-linear. This micromethod required 8-fold less urine sample and 12.5-fold less liver homogenate as compared to the standard plate incorporation assay and was from 6.2- to 11.8-fold more sensitive to evaluate urine mutagenicity. The sensitivity of this technique was found to be limited to individuals smoking more than approx. 5 cigarettes/day by the standard extraction-concentration procedure.     

L-asparaginase production by isolated Staphylococcus sp. - 6A: design of experiment considering interaction effect for process parameter optimization

AIMS: Evaluation of fermentation process parameter interactions for the production of l-asparaginase by isolated Staphylococcus sp. - 6A. METHODS AND RESULTS: Fractional factorial design of experimentation (L18 orthogonal array of Taguchi methodology) was adopted to optimize nutritional (carbon and nitrogen sources), physiological (incubation temperature, medium pH, aeration and agitation) and microbial (inoculum level) fermentation factors. The experimental results and software predicted enzyme production values were comparable. CONCLUSION: Incubation temperature, inoculum level and medium pH, among all fermentation factors, were major influential parameters at their individual level, and contributed to more than 60% of total l-asparaginase production. Interaction data of selected fermentation parameters could be classified as least and most significant at individual and interactive levels. Aeration and agitation were most significant at interactive level, but least significant at individual level, and showed maximum severity index and vice versa at enzyme production. SIGNIFICANCE AND IMPACT OF THE STUDY: All selected factors showed impact on l-asparaginase enzyme production by this isolated microbial strain either at the individual or interactive level. Incubation temperature, inoculum concentration, pH of the medium and nutritional source (glucose and ammonium chloride) had impact at individual level, while aeration, agitation and incubation time showed influence at interactive level. Significant improvement (ninefold increase) in enzyme production by this microbial isolate was noted under optimized environment.     

Interactions between stressors on coral reefs: analytical approaches, re-analysis of old data, and different conclusions

Recently, some of the older literature purported to demonstrate a positive interaction between solar radiation and thermal stress on the phenomenon of coral bleaching have been called into question (Dunne 2010). Dunne (2010) correctly points out important experimental design and analysis issues related to the interpretation of multi-factorial designs, and both positive and negative interactive effects, with emphasis on the point that once interactive effects are detected no interpretation of the main effects of a multi-factorial design is appropriate. Dunne (2010) presents a number of statistical analyses to address this issue that are valuable and deserve significant discussion. While Dunne (2010) was specifically addressing the issue of the misuse of the term “synergy” some of the analyses presented may have understated the presence of such interactions. Here, I conduct a re-analysis of specific datasets using another potentially useful approach that shows both positive and negative interactions during experiments on bleaching.     

Modeled interactive effects of precipitation, temperature, and [CO2] on ecosystem carbon and water dynamics in different climatic zones

Interactive effects of multiple global change factors on ecosystem processes are complex. It is relatively expensive to explore those interactions in manipulative experiments. We conducted a modeling analysis to identify potentially important interactions and to stimulate hypothesis formulation for experimental research. Four models were used to quantify interactive effects of climate warming (T), altered precipitation amounts [doubled (DP) and halved (HP)] and seasonality (SP, moving precipitation in July and August to January and February to create summer drought), and elevated [CO₂] (C) on net primary production (NPP), heterotrophic respiration (R_h), net ecosystem production (NEP), transpiration, and runoff. We examined those responses in seven ecosystems, including forests, grasslands, and heathlands in different climate zones. The modeling analysis showed that none of the three‐way interactions among T, C, and altered precipitation was substantial for either carbon or water processes, nor consistent among the seven ecosystems. However, two‐way interactive effects on NPP, R_h, and NEP were generally positive (i.e. amplification of one factor's effect by the other factor) between T and C or between T and DP. A negative interaction (i.e. depression of one factor's effect by the other factor) occurred for simulated NPP between T and HP. The interactive effects on runoff were positive between T and HP. Four pairs of two‐way interactive effects on plant transpiration were positive and two pairs negative. In addition, wet sites generally had smaller relative changes in NPP, R_h, runoff, and transpiration but larger absolute changes in NEP than dry sites in response to the treatments. The modeling results suggest new hypotheses to be tested in multifactor global change experiments. Likewise, more experimental evidence is needed for the further improvement of ecosystem models in order to adequately simulate complex interactive processes.     

Multifactorial screening design and analysis of SELDI-TOF ProteinChip array optimization experiments

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry is a powerful tool for rapidly generating protein expression data (peptide and protein profiles) from a large number of samples. However, as with any technology, it must be optimized and reproducible for one to have confidence in the results. Using a classical statistical method called the fractional factorial design of experiments, we assessed the effects of 11 different experimental factors. We also developed several metrics that reflect trace quality and reproducibility. These were used to measure the effect of each individual factor, and the interactions between factors, to determine optimal factor settings and thus ultimately produce the best possible traces. Significant improvements to output traces were seen by simultaneously altering several parameters, either in the sample preparation procedure or during the matrix preparation and application procedure. This has led to the implementation of an improved method that gives a better quality, reproducible, and robust output.     

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.     

Statistical analysis of optimal culture conditions for Gluconacetobacter hansenii cellulose production

AIM: The purpose of this study was to analyse the effects of different culture parameters on Gluconacetobacter hansenii (ATCC 10821) to determine which conditions provided optimum cellulose growth. METHODS AND RESULTS: Five culture factors were investigated: carbon source, addition of ethanol, inoculation ratio, pH and temperature. jmp Software (SAS, Cary, NC, USA) was used to design this experiment using a fractional factorial design. After 22 days of static culture, the cellulose produced by the bacteria was harvested, purified and dried to compare the cellulose yields. The results were analysed by fitting the data to a first-order model with two-factor interactions. CONCLUSIONS: The study confirmed that carbon source, addition of ethanol, and temperature were significant factors in the production of cellulose of this G. hansenii strain. While pH alone does not significantly affect average cellulose production, cellulose yields are affected by pH interaction with the carbon source. Culturing the bacteria on glucose at pH 6.5 produces more cellulose than at pH 5.5, while using mannitol at pH 5.5 produces more cellulose than at pH 6.5. The bacteria produced the most cellulose when cultured on mannitol, at pH 5.5, without ethanol, at 20 degrees C. Inoculation ratio was not found to be a significant factor or involved in any significant two-factor interaction. SIGNIFICANCE AND IMPACT OF THE STUDY: These findings give insight into the conditions necessary to maximize cellulose production from this G. hansenii strain. In addition, this work demonstrates how the fractional factorial design can be used to test a large number of factors using an abbreviated set of experiments. Fitting a statistical model determined the significant factors as well as the significant two-factor interactions.     

Xylanase production in solid state fermentation by Aspergillus niger mutant using statistical experimental designs

The initial moisture content, cultivation time, inoculum size and concentration of basal medium were optimized in solid state fermentation (SSF) for the production of xylanase by an Aspergillus niger mutant using statistical experimental designs. The cultivation time and concentration of basal medium were the most important factors affecting xylanase activity. An inoculum size of 5 x 10(5) spores/g, initial moisture content of 65%, cultivation time of 5 days and 10 times concentration of basal medium containing 50 times concentration of corn steep liquor were optimum for xylanase production in SSF. Under the optimized conditions, the activity and productivity of xylanase obtained after 5 days of fermentation were 5,071 IU/g of rice straw and 14,790 IU l(-1) h(-1), respectively. The xylanase activity predicted by a polynomial model was 5,484 IU/g of rice straw.     

Challenging terrestrial biosphere models with data from the long‐term multifactor Prairie Heating and CO2 Enrichment experiment

Multifactor experiments are often advocated as important for advancing terrestrial biosphere models (TBMs), yet to date, such models have only been tested against single‐factor experiments. We applied 10 TBMs to the multifactor Prairie Heating and CO₂ Enrichment (PHACE) experiment in Wyoming, USA. Our goals were to investigate how multifactor experiments can be used to constrain models and to identify a road map for model improvement. We found models performed poorly in ambient conditions; there was a wide spread in simulated above‐ground net primary productivity (range: 31–390 g C m^?2 yr^?1). Comparison with data highlighted model failures particularly with respect to carbon allocation, phenology, and the impact of water stress on phenology. Performance against the observations from single‐factors treatments was also relatively poor. In addition, similar responses were predicted for different reasons across models: there were large differences among models in sensitivity to water stress and, among the N cycle models, N availability during the experiment. Models were also unable to capture observed treatment effects on phenology: they overestimated the effect of warming on leaf onset and did not allow CO₂‐induced water savings to extend the growing season length. Observed interactive (CO₂ × warming) treatment effects were subtle and contingent on water stress, phenology, and species composition. As the models did not correctly represent these processes under ambient and single‐factor conditions, little extra information was gained by comparing model predictions against interactive responses. We outline a series of key areas in which this and future experiments could be used to improve model predictions of grassland responses to global change.     

一株高效苯酚降解真菌的分离鉴定及其菌剂的制备

【背景】含酚废水是普遍存在的有毒、难降解的有机污染物之一,生物法处理含酚废水因成本低、无二次污染而具有广阔的应用前景。可降解苯酚的微生物中,真菌比细菌对恶劣环境的适应性更好。针对液态菌液保存时间较短和运输困难的瓶颈,制备固体菌剂可以提高菌体存活率和储藏稳定性。【目的】筛选一株能够高效降解苯酚的真菌,优化其降酚性能并选择合适的载体制备菌剂。【方法】通过逐级驯化和纯化分离降酚菌,筛选得到降酚性能较强的真菌并通过ITS r DNA基因测序进行种属鉴定,通过参数优化进一步提高菌株降解苯酚的性能;以不同材料为载体制备菌剂,通过稀释平板计数法和苯酚降解实验探究菌剂在不同温度下的保存效果。【结果】分离筛选得到一株高效降解苯酚真菌QWD1,通过鉴定证明其属于Magnusiomyces capitatus,其最适降解条件:(NH_4)_2SO_4为氮源,接种量为15%,pH为7.0,温度为35°C,氮源浓度为14 mmol/L。在此条件下,28 d内对1 600 mg/L苯酚去除率可以达到97.15%;制备菌剂最合适载体为谷糠,适宜保存温度为4°C,保存时间可达到90 d甚至更长,活菌数高达2.5×10~8 CFU/g左右,降解苯酚效果良好。【结论】筛选得到了一株高效降解苯酚真菌,优化其降解性能并将其制备成菌剂,为处理含酚废水提供了新菌种和理论支持。     

Mathematical model for the alcoholic fermentation in batch culture: Comparison between complete and incomplete factorial (33) designs

Surface response methodology was used to study the effects of three variables, viz. total reducing sugars (116–222 g l^?1), yeast inoculum concentration (10–30%) and supplemented inorganic nitrogen as (NH₄)₂SO₄ (1–3 g l^?1) on the production of ethanol.The complete and incomplete factorial designs (3³) were compared through the residual analysis of variance. Since there was no statistically significant difference at the 5% level, on the basis of cost-efficiency and space limitations, it was recommended to use incomplete factorial design constituting 12 treatments with three repetitions in central point totalling 15 experiments which control the batch alcoholic fermentation.     

Optimization of methane fermentation from effluent of bio-hydrogen fermentation process using response surface methodology

The individual effects and interactive effects of substrate concentration, ratio of inoculum to substrate, Ca(2+) concentration on the methane yield from the effluent of bio-hydrogen fermentation of food waste were investigated in this study. A central composite design (CCD) and response methodology (RSM) were employed in designing the experiments, in order to determine the optimum conditions for methane fermentation. The experiment results showed that the effects of substrate concentration, ratio of inoculum to substrate, Ca(2+) concentration were statistically significant at 5% level. The interactive effect of substrate concentration and ratio of inoculum to substrate was significant, however the interactive effect of substrate concentration and Ca(2+) concentration, ratio of inoculum to substrate and Ca(2+) concentration were found to be insignificant at 5% level. A maximum yield of 565.76 ml CH(4)/g VS(added) was estimated under the optimum conditions for substrate concentration 7.77 g of VS/l, inoculum to substrate ratio of 2.81 and calcium concentration of 380.82 mg/l. Verification experiment of the estimated optimum conditions confirmed that the RSM was useful for optimizing the methane yield from effluent of bio-hydrogen fermentation of food waste.     

UGMDR: a unified conceptual framework for detection of multifactor interactions underlying complex traits

Biological outcomes are governed by multiple genetic and environmental factors that act in concert. Determining multifactor interactions is the primary topic of interest in recent genetics studies but presents enormous statistical and mathematical challenges. The computationally efficient multifactor dimensionality reduction (MDR) approach has emerged as a promising tool for meeting these challenges. On the other hand, complex traits are expressed in various forms and have different data generation mechanisms that cannot be appropriately modeled by a dichotomous model; the subjects in a study may be recruited according to its own analytical goals, research strategies and resources available, not only consisting of homogeneous unrelated individuals. Although several modifications and extensions of MDR have in part addressed the practical problems, they are still limited in statistical analyses of diverse phenotypes, multivariate phenotypes and correlated observations, correcting for potential population stratification and unifying both unrelated and family samples into a more powerful analysis. I propose a comprehensive statistical framework, referred as to unified generalized MDR (UGMDR), for systematic extension of MDR. The proposed approach is quite versatile, not only allowing for covariate adjustment, being suitable for analyzing almost any trait type, for example, binary, count, continuous, polytomous, ordinal, time-to-onset, multivariate and others, as well as combinations of those, but also being applicable to various study designs, including homogeneous and admixed unrelated-subject and family as well as mixtures of them. The proposed UGMDR offers an important addition to the arsenal of analytical tools for identifying nonlinear multifactor interactions and unraveling the genetic architecture of complex traits.