Experimental Design Framework Research Articles

An experimental design framework for Markovian gene regulatory networks under stationary control policy

BackgroundA fundamental problem for translational genomics is to find optimal therapies based on gene regulatory intervention. Dynamic intervention involves a control policy that optimally reduces a cost function based on phenotype by externally altering the state of the network over time. When a gene regulatory network (GRN) model is fully known, the problem is addressed using classical dynamic programming based on the Markov chain associated with the network. When the network is uncertain, a Bayesian framework can be applied, where policy optimality is with respect to both the dynamical objective and the uncertainty, as characterized by a prior distribution. In the presence of uncertainty, it is of great practical interest to develop an experimental design strategy and thereby select experiments that optimally reduce a measure of uncertainty.ResultsIn this paper, we employ mean objective cost of uncertainty (MOCU), which quantifies uncertainty based on the degree to which uncertainty degrades the operational objective, that being the cost owing to undesirable phenotypes. We assume that a number of conditional probabilities characterizing regulatory relationships among genes are unknown in the Markovian GRN. In sum, there is a prior distribution which can be updated to a posterior distribution by observing a regulatory trajectory, and an optimal control policy, known as an “intrinsically Bayesian robust” (IBR) policy. To obtain a better IBR policy, we select an experiment that minimizes the MOCU remaining after applying its output to the network. At this point, we can either stop and find the resulting IBR policy or proceed to determine more unknown conditional probabilities via regulatory observation and find the IBR policy from the resulting posterior distribution. For sequential experimental design this entire process is iterated. Owing to the computational complexity of experimental design, which requires computation of many potential IBR policies, we implement an approximate method utilizing mean first passage times (MFPTs) – but only in experimental design, the final policy being an IBR policy.ConclusionsComprehensive performance analysis based on extensive simulations on synthetic and real GRNs demonstrate the efficacy of the proposed method, including the accuracy and computational advantage of the approximate MFPT-based design.

Evaluation of batching and layout on the performance of flexible manufacturing system

Flexible manufacturing system (FMS) is one of the solutions that enables any manufacturing system to withstand changing requirements of the market. An FMS consists of different automated workstations, material handling devices, and storage systems, all of which work under computer control. In the present work, analysis and modeling of a real problem has been considered for performance improvement. Modeling is done for analyzing the performance of an existing model, to propose a new layout for the system. Effect of factors like part mix, layout type, and batching condition, on the system performance in terms of productivity, system utilization rate, and cycle time is analyzed. The work includes simulation study along with Taguchi’s experimental design framework for studying how different factors with varying levels affect system performance. ProModel software is used for simulating existing and proposed models of the manufacturing firm. Further, analysis of variance (ANOVA) is employed for finding the most important factor that affects system performance. Proposed methodology helps in determining best factor-level combination for each performance parameter. The results show that the system performs well with the implementation of loop layout having many numbers of batches. Towards the end, the scope of further is highlighted.

Metabolic flux analysis in Ashbya gossypii using 13C-labeled yeast extract: industrial riboflavin production under complex nutrient conditions

BackgroundThe fungus Ashbya gossypii is an important industrial producer of the vitamin riboflavin. Using this microbe, riboflavin is manufactured in a two-stage process based on a rich medium with vegetable oil, yeast extract and different precursors: an initial growth and a subsequent riboflavin production phase. So far, our knowledge on the intracellular metabolic fluxes of the fungus in this complex process is limited, but appears highly relevant to better understand and rationally engineer the underlying metabolism. To quantify intracellular fluxes of growing and riboflavin producing A. gossypii, studies with different 13C tracers were embedded into a framework of experimental design, isotopic labeling analysis by MS and NMR techniques, and model-based data processing. The studies included the use 13C of yeast extract, a key component used in the process.ResultsDuring growth, the TCA cycle was found highly active, whereas the cells exhibited a low flux through gluconeogenesis as well as pentose phosphate pathway. Yeast extract was the main carbon donor for anabolism, while vegetable oil selectively contributed to the proteinogenic amino acids glutamate, aspartate, and alanine. During the subsequent riboflavin biosynthetic phase, the carbon flux through the TCA cycle remained high. Regarding riboflavin formation, most of the vitamin’s carbon originated from rapeseed oil (81 ± 1%), however extracellular glycine and yeast extract also contributed with 9 ± 0% and 8 ± 0%, respectively. In addition, advanced yeast extract-based building blocks such as guanine and GTP were directly incorporated into the vitamin.ConclusionIntracellular carbon fluxes for growth and riboflavin production on vegetable oil provide the first flux insight into a fungus on complex industrial medium. The knowledge gained therefrom is valuable for further strain and process improvement. Yeast extract, while being the main carbon source during growth, contributes valuable building blocks to the synthesis of vitamin B2. This highlights the importance of careful selection of the right yeast extract for a process based on its unique composition.

Bayesian Monitoring Design for Streambed Heat Tracing: Numerical Simulation and Sandbox Experiments

Heat tracing methods have been widely employed for subsurface characterization. Nevertheless, there were very few studies regarding the optimal monitoring design for heat tracing in heterogeneous streambeds. In this study, we addressed this issue by proposing an efficient optimal design framework to collect the most informative diurnal temperature signal for Bayesian estimation of streambed hydraulic conductivities. The data worth (DW) was measured by the expected relative entropy between the prior and posterior distributions of the conductivity field. An adaptively refined Gaussian process surrogate was employed to alleviate the computational burden, resulting in at least three orders of magnitude of speed-up. The applicability of the optimal experimental design framework was evaluated by both numerical and sandbox experimental cases. Results showed that the most informative locations centered in the transition zones among the main patterns of the hydraulic conductivity field, while the most informative times centered in a short period after the minimum/maximum temperature appeared. With the fixed number of measurements, extending the calibration period was more beneficial than increasing the monitoring frequency in improving the estimation results. To our best knowledge, this work is the first study on Bayesian monitoring design for streambed characterization with the heat tracing method. The method and results can provide guidance on selecting monitoring strategies under budget-limited conditions.

An agent-monitored framework for the output-oriented design of experiments in exploratory modelling

Exploratory modelling is an approach for modelling under uncertainty based on the generation and analysis of computational experiments. The results of exploratory modelling are sensitive to the way that experiments are designed, such as the way that the uncertainty space is delineated. This article introduces an agent-monitored framework—i.e. a design metaphor of the interactions among modellers and stakeholders and the simulation process—for controlling the design of experiments based on monitoring model behaviour in the output space. To demonstrate the benefits of the suggested framework in the exploratory modelling process, the article shows how the use of the framework with an output-oriented approach informs the delineation of an appropriate uncertainty space with an illustrative example in the decision-making context. The article concludes that the design of experiments based on feedback from the output space can be a useful approach: to control simulations in exploratory modelling; to build more confidence in final results; and to inform the design of other aspects of experiments, such as selecting policy levers and sampling method.

A supermartingale approach to Gaussian process based sequential design of experiments

Gaussian process (GP) models have become a well-established framework for the adaptive design of costly experiments, and notably of computer experiments. GP-based sequential designs have been found practically efficient for various objectives, such as global optimization (estimating the global maximum or maximizer(s) of a function), reliability analysis (estimating a probability of failure) or the estimation of level sets and excursion sets. In this paper, we study the consistency of an important class of sequential designs, known as stepwise uncertainty reduction (SUR) strategies. Our approach relies on the key observation that the sequence of residual uncertainty measures, in SUR strategies, is generally a supermartingale with respect to the filtration generated by the observations. This observation enables us to establish generic consistency results for a broad class of SUR strategies. The consistency of several popular sequential design strategies is then obtained by means of this general result. Notably, we establish the consistency of two SUR strategies proposed by Bect, Ginsbourger, Li, Picheny and Vazquez (Stat. Comput. 22 (2012) 773–793) – to the best of our knowledge, these are the first proofs of consistency for GP-based sequential design algorithms dedicated to the estimation of excursion sets and their measure. We also establish a new, more general proof of consistency for the expected improvement algorithm for global optimization which, unlike previous results in the literature, applies to any GP with continuous sample paths.

Logical Inference Framework for Experimental Design for Mechanical Characterization Procedures.

Optimizing an experimental design is a complex task when a model is required for indirect reconstruction of physical parameters from the sensor readings. In this work, a formulation is proposed to unify the probabilistic reconstruction of mechanical parameters and an optimization problem. An information-theoretic framework combined with a new metric of information density is formulated providing several comparative advantages: (i) a straightforward way to extend the formulation to incorporate additional concurrent models, as well as new unknowns such as experimental design parameters in a probabilistic way; (ii) the model causality required by Bayes’ theorem is overridden, allowing generalization of contingent models; and (iii) a simpler formulation that avoids the characteristic complex denominator of Bayes’ theorem when reconstructing model parameters. The first step allows the solving of multiple-model reconstructions. Further extensions could be easily extracted, such as robust model reconstruction, or adding alternative dimensions to the problem to accommodate future needs.

An Exploration of Arts Advocates’ Use of Experimentally Designed Arts Program Research

In the current political climate, there is an understandable pressure on arts advocates to demonstrate irrefutable proof of the arts’ impact. How, then, can arts advocates use evidence-based research to advance their agendas when some have raised concern regarding rigor in arts research? This article shares findings from two recent studies of arts programs in Philadelphia, PA, which used a rigorous experimental design framework. The opportunities and challenges these types of studies present for arts advocates, including quality control, translation, the costs of rigor, intra-arts competition, and theories of change, are discussed.

Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems

We develop a framework for goal-oriented optimal design of experiments (GOODE) for large-scale Bayesian linear inverse problems governed by PDEs. This framework differs from classical Bayesian optimal design of experiments (ODE) in the following sense: we seek experimental designs that minimize the posterior uncertainty in the experiment end-goal, e.g. a quantity of interest (QoI), rather than the estimated parameter itself. This is suitable for scenarios in which the solution of an inverse problem is an intermediate step and the estimated parameter is then used to compute a QoI. In such problems, a GOODE approach has two benefits: the designs can avoid wastage of experimental resources by a targeted collection of data, and the resulting design criteria are computationally easier to evaluate due to the often low-dimensionality of the QoIs. We present two modified design criteria, A-GOODE and D-GOODE, which are natural analogues of classical Bayesian A- and D-optimal criteria. We analyze the connections to other ODE criteria, and provide interpretations for the GOODE criteria by using tools from information theory. Then, we develop an efficient gradient-based optimization framework for solving the GOODE optimization problems. Additionally, we present comprehensive numerical experiments testing the various aspects of the presented approach. The driving application is the optimal placement of sensors to identify the source of contaminants in a diffusion and transport problem. We enforce sparsity of the sensor placements using an -norm penalty approach, and propose a practical strategy for specifying the associated penalty parameter.

Simulation of turbulent flow in a rapid compression machine: Large Eddy Simulation and computationally efficient alternatives for the design of ignition delay time experiments

Rapid compression machines (RCMs) are widely used by the fuel research community to provide engine-relevant conditions to study ignition delay time (IDT), which is a crucial target for validating chemical kinetic mechanisms for fuels. Creviced piston heads are routinely used to ensure temperature homogeneity within the combustion chambers of RCMs. However, due to the exponential dependence of kinetic rate coefficients on temperature, homogeneity of the temperature field is vital for a clear interpretation of results and identification of chemical kinetic mechanisms. The overall aims of this work are to (1) support operators of RCMs in ensuring that their devices achieve sufficient levels of in-chamber temperature homogeneity and (2) validate performance of a previously-developed correlation for temperature inhomogeneity in RCMs at different conditions. Large Eddy Simulation (LES) is conducted to resolve 3D unsteady structure of turbulent flow, throughout compression and long post-compression times, at two representative operating conditions in the NUI Galway RCM. Results show that at higher pressures, 3D LES temperature and velocity fields are well approximated by a previous 2D laminar model. At low pressures, 2D laminar and 3D LES agree well for flow during compression, but predict different evolution of roll-up vortices and temperature distribution after compression. However, across all conditions there is a satisfactory agreement between 2D laminar and 3D LES results for global temperature inhomogeneity. Moreover, the previously developed correlation (based on 2D laminar simulations) for temperature inhomogeneity is found to agree with 3D LES to within ±20%. Therefore, the correlation may be used for preliminary design and evaluation of RCMs and RCM experiments. We propose a framework for design of RCM experiments based on the use of correlations, supported by 2D laminar simulations, and finally 3D LES of selected cases for confirmation.

Optimal Expert Knowledge Elicitation for Bayesian Network Structure Identification

Bayesian network (BN) has been a popular tool for gaining mechanistic understanding of variables by revealing how the variables influence each other. It has been found very effective in a few studies in quality control and process monitoring. However, for complex problems where the structure of a BN is unknown, a common approach is to learn the BN structure from observational data. A fundamental bottleneck of this approach is that observational data can only be used to discover part of the influential relationships among variables. To overcome this problem, we propose to combine observational data and expert knowledge. To the best of the author’s knowledge, our approach is the first of its kind that formulates an experimental design framework to automate the expert elicitation process and collect the most informative expert knowledge, optimally matched to the observational data, to learn the BN structure. Note to Practitioners —As design of experiment techniques have been widely used in practice to automate the systematic data collection and experimental procedures by perturbing the system in order to better understand the relationships between the factors, here, we provide practitioners a counterpart approach for systematic expert knowledge elicitation. It can be used in combination with a imperfect data set, while the data could provide a starting point and further inform the expert knowledge elicitation by prioritizing the information queries. The data and expert knowledge gathered will be fused to learn the relationships between the factors with the estimation of the uncertainties.

Model-based analysis of experimental data from interconnected, row-configured huts elucidates multifaceted effects of a volatile chemical on Aedes aegypti mosquitoes

BackgroundInsecticides used against Aedes aegypti and other disease vectors can elicit a multitude of dose-dependent effects on behavioral and bionomic traits. Estimating the potential epidemiological impact of a product requires thorough understanding of these effects and their interplay at different dosages. Volatile spatial repellent (SR) products come with an additional layer of complexity due to the potential for altered movement of affected mosquitoes and diffusion of volatile particles of the product beyond the treated house. Here, we propose a paired experimental design and statistical inference framework for estimating these nuanced effects of volatile SRs.MethodWe fitted a continuous-time Markov chain model in a Bayesian framework to data on marked mosquitoes released in interconnected experimental huts conducted in Iquitos, Peru. We estimated the effects of two dosages of transfluthrin on Ae. aegypti behaviors associated with human-vector contact: repellency, exiting and knockdown in the treated space and in connected, adjacent huts. We validated the framework using simulated data.ResultsThe odds of a female Ae. aegypti being repelled, and thus prevented from entering a treated hut (HT), increased at both dosages (low dosage: odds = 1.64, 95% highest density interval (HDI) = 1.30–2.09; high dosage: odds = 1.35, HDI = 1.04–1.67). The relative risk of exiting from the treated hut was reduced (low: RR = 0.70, HDI = 0.62–1.09; high: RR = 0.70, HDI = 0.40–1.06), with this effect carrying over to untreated spaces two huts away from the treated hut (H2) (low: RR = 0.79, HDI = 0.59–1.01; high: RR = 0.66, HDI = 0.50–0.87). Knockdown rates were increased in both treated and downstream huts, particularly under high dosage (HT: RR = 8.37, HDI = 2.11–17.35; H1: RR = 1.39, HDI = 0.52–2.69; H2: RR = 2.22, HDI = 0.96–3.86).ConclusionsOur framework is effective at elucidating multiple effects of volatile chemicals used in SR products, as well as their downstream effects. For the examined formulations of transfluthrin, we found notable dose-dependent effects on repellency, movement and knockdown that carry over to adjacent, untreated spaces.

Comprehensive experimental design for chemical engineering processes: A two-layer iterative design approach

A systematic framework for optimal experimental design (OED) of multiple experimental factors is proposed to support data collection in chemical engineering systems with the purpose to obtain the most informative data for modeling. The structural identifiability is firstly investigated through a combined procedure with the generating series method and the identifiability tableau. Next the parameter estimability is analyzed via the orthogonalized sensitivity analysis in order to identify crucial and identifiable model parameters. Traditionally OED treats separate problems such as the choice of input conditions, the selection of variables to measure, and the design of sampling time profile. A new OED strategy is proposed that optimizes these interdependent factors in one framework. An iterative two-layer design structure is developed. In the lower layer for observation design, the sampling profile and the measurement set selection are combined and formulated as a single integrated observation design problem, which is relaxed to a convex optimization problem that can be solved with a local method. Thus the measurement set selection and the sampling profile can be determined simultaneously. In the upper layer for input design, the optimization of input intensities is obtained through stochastic global searching. In this way, the multi-factor optimization problem is solved through the integration of a stochastic method, for the upper layer, and a deterministic method, for the lower layer. Case studies are conducted on two biochemical systems with different complexities, one is an enzyme kinetically controlled synthesis system and the other one is a lab-scale enzymatic biodiesel production system. Numerical results demonstrate the effectiveness of this iterative double-layer OED optimization strategy in reducing parameter estimation uncertainties compared with conventional approaches.

A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data

Accurate forecasts of solar energy are important for photovoltaic (PV) based energy plants to facilitate an early participation in energy auction markets and efficient resource planning. The study concentrates on Long Short Term Memory (LSTM), a novel forecasting method from the family of deep neural networks, and compares its forecasting accuracy to alternative methods with a proven track record in solar energy forecasting. To provide a comprehensive and reliable assessment of LSTM, the study employs remote-sensing data for testing predictive accuracy at 21 locations, 16 of which are in mainland Europe and 5 in the US. To that end, a novel framework to conduct empirical forecasting comparisons is introduced, which includes the generation of virtual PV plants. The framework enables richer comparisons with higher coverage of geographical regions. Empirical results suggest that LSTM outperforms a large number of alternative methods with substantial margin and an average forecast skill of 52.2% over the persistence model. An implication for energy management practice is that LSTM is a promising technique, which deserves a place in forecasters’ toolbox. From an academic point of view, LSTM and the proposed framework for experimental design provide a valuable environment for future studies that assess new forecasting technology.

Robust estimation and testing in one-way ANOVA for Type II censored samples: skew normal error terms

ABSTRACTCensoring can be occurred in many statistical analyses in the framework of experimental design. In this study, we estimate the model parameters in one-way ANOVA under Type II censoring. We assume that the distribution of the error terms is Azzalini's skew normal. We use Tiku's modified maximum likelihood (MML) methodology which is a modified version of the well-known maximum likelihood (ML) in the estimation procedure. Unlike ML methodology, MML methodology is non-iterative and gives explicit estimators of the model parameters. We also propose new test statistics based on the proposed estimators. The performances of the proposed estimators and the test statistics based on them are compared with the corresponding normal theory results via Monte Carlo simulation study. A real life data is analysed to show the implementation of the methodology presented in this paper at the end of the study.

The Effectiveness of Emotional Schema Therapy on the Emotional Schemas and Emotional Regulation in Irritable Bowel Syndrome: Single Subject Design.

Background:Positive and negative emotional states are the most important factors in treatment and prevention process of psychosomatic diseases. This research aimed to investigate the effectiveness of emotional schemas' therapy on emotional schemas' modification and difficulties of emotion regulation in women with irritable bowel syndrome (IBS).Materials and Methods:This research was implemented in the framework of single-subject experimental design using step-wise multiple baselines plan. Five patients with IBS were selected as convenience sampling on the base of their willingness to participate and then they received emotional schema therapy (EST). Research tools included ROME III scale, SCID interview, emotional schemas questionnaire, and difficulties of emotional regulation. The analysis of data was done using visual analysis charts, recovery percentage, and reliable change index.Results:The results showed a decrease of scores in some maladaptive schemas and increase of some adaptive schemas than baseline in patients who received EST (P ≤ 0.05). Furthermore, this treatment decreased scores of some emotion regulation difficulty components (P ≤ 0.05).Conclusion:It seems that EST is an appropriate option for treatment of these patients because it is effective in improvement of emotional schemas and difficulties of emotional regulation.

Efficient D-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems

We develop a computational framework for D-optimal experimental design for PDE-based Bayesian linear inverse problems with infinite-dimensional parameters. We follow a formulation of the experiment...

The effectiveness of emotional schema therapy on psychological symptoms of patients with irritable Bowel syndrome

AIM: positive and negative emotional states are the most important factors in the process of treatment and prevention of psychosomatic diseases. This research aimed to investigate the effectiveness of emotional schemas therapy on the reduction of psychological symptoms of patients with irritable bowel syndrome (IBS). METHODS: This research was implemented in the framework of single-subject experimental design using of stepwise multiple-baselines plan. Five patients with IBS were selected as convenience sampling on the base of the participation willingness of individuals to the research then received emotional schema therapy (EST). Research tools included ROME III scale, semi-structured interview for making DSM-IV interview, and depression, anxiety, and stress scale (DASS-21) questionnaire. The analysis of data was done using visual analysis charts, recovery percentage, and reliable change index. RESULTS: The results showed patients that received EST detected decrease of the scores in DASS-21 rate than baseline (P ≤ 0.05). CONCLUSIONS: EST decreases psychological symptoms of patients with IBS. Therefore, it can be proposed as an alternative therapy or together with other therapies for these patients.

PEITH(Θ): perfecting experiments with information theory in Python with GPU support.

MotivationDifferent experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches and general advances in quantitative systems biology, methods that inform us about the information content that a given experiment carries about the question we want to answer, become crucial.ResultsPEITH(Θ) is a general purpose, Python framework for experimental design in systems biology. PEITH(Θ) uses Bayesian inference and information theory in order to derive which experiments are most informative in order to estimate all model parameters and/or perform model predictions.Availability and implementation https://github.com/MichaelPHStumpf/Peitho

Framework for Reliable Experimental Design (FRED): A research framework to ensure the dependable interpretation of digital data for digital forensics

The establishment of fact forms the cornerstone of any forensic discipline, with digital analysis being no exception. Practitioners are under an obligation as expert witnesses to provide factual accounts of digital scenarios, which must be underpinned by robust knowledge and evidential findings. To achieve this level of reliability, investigatory research must be suitably planned, implemented and analysed in a way which instills confidence in the accuracy of any findings. This is particularly important as digital forensic organisations are now facing the impending requirement to have acquired ISO/IEC 17025 accreditation. This article proposes the Framework for Reliable Experimental Design (FRED) to support those engaged in the field of digital forensics research to contribute reliable, robust findings. FRED focuses on the underpinning procedures involved within undertaking the reverse engineering of digital data structures and the process of extracting and interpreting digital content in a reliable way. The proposed framework is designed to be a resource for those operating within the digital forensic field, both in industry and academia, to support and develop research best practice within the discipline.