optimal experimental design
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2021 ◽  
Vol 200 ◽  
pp. 110747
Author(s):  
Joshua Stuckner ◽  
Matthew Piekenbrock ◽  
Steven M. Arnold ◽  
Trenton M. Ricks

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1053
Author(s):  
Zhaozheng Hou

In recent years, synthetic gene circuits for adding new cell features have become one of the most powerful tools in biological and pharmaceutical research and development. However, because of the inherent non-linearity and noisy experimental data, the experiment-based model calibration of these synthetic parts is perceived as a laborious and time-consuming procedure. Although the optimal experimental design (OED) based on the Fisher information matrix (FIM) has been proved to be an effective means to improve the calibration efficiency, the required calculation increases dramatically with the model size (parameter number). To reduce the OED complexity without losing the calibration accuracy, this paper proposes two OED approaches with different parameter clustering methods and validates the accuracy of calibrated models with in-silico experiments. A model of an inducible synthetic promoter in S. cerevisiae is adopted for bench-marking. The comparison with the traditional off-line OED approach suggests that the OED approaches with both of the clustering methods significantly reduce the complexity of OED problems (for at least 49.0%), while slightly improving the calibration accuracy (11.8% and 19.6% lower estimation error in average for FIM-based and sensitivity-based approaches). This study implicates that for calibrating non-linear models of biological pathways, cluster-based OED could be a beneficial approach to improve the efficiency of optimal experimental design.


2021 ◽  
Author(s):  
Nathan Braniff ◽  
Taylor Pearce ◽  
Zixuan Lu ◽  
Michael Astwood ◽  
William SR Forrest ◽  
...  

Motivation: Modelling in systems and synthetic biology relies on accurate parameter estimates and predictions. Accurate model calibration relies, in turn, on data, and on how well-suited the available data is to a particular modelling task. Optimal experimental design (OED) techniques can be used to identify experiments and data collection procedures that will most efficiently contribute to a given modelling objective. However, implementation of OED is limited by currently available software tools that are not well-suited for the diversity of nonlinear models and non-normal data commonly encountered in biological research. Moreover, existing OED tools do not make use of the state-of-the-art numerical tools, resulting in inefficient computation. Results: Here we present the NLoed software package. NLoed is an open-source Python library providing convenient access to OED methods, with particular emphasis on experimental design for systems biology research. NLoed supports a wide variety of nonlinear, multi-input/output, and dynamic models, and facilitates modelling and design of experiments over a wide variety of data types. To support OED investigations, the NLoed package implements maximum likelihood fitting and diagnostic tools, providing a comprehensive modelling workflow. NLoed offers an accessible, modular, and flexible OED tool-set suited to the wide variety of experimental scenarios encountered in systems biology research. We demonstrate NLOED's capabilities by applying it to experimental design for characterization of a bacterial optogenetic system. Availability: NLoed is available via pip from the PyPi repository; https://pypi.org/project/nloed/. Source code, documentation and examples can be found on Github at https://github.com/ingallslab/NLoed.


Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1010
Author(s):  
Sergio Pozuelo-Campos ◽  
Víctor Casero-Alonso ◽  
Mariano Amo-Salas

In optimal experimental design theory it is usually assumed that the response variable follows a normal distribution with constant variance. However, some works assume other probability distributions based on additional information or practitioner’s prior experience. The main goal of this paper is to study the effect, in terms of efficiency, when misspecification in the probability distribution of the response variable occurs. The elemental information matrix, which includes information on the probability distribution of the response variable, provides a generalized Fisher information matrix. This study is performed from a practical perspective, comparing a normal distribution with the Poisson or gamma distribution. First, analytical results are obtained, including results for the linear quadratic model, and these are applied to some real illustrative examples. The nonlinear 4-parameter Hill model is next considered to study the influence of misspecification in a dose-response model. This analysis shows the behavior of the efficiency of the designs obtained in the presence of misspecification, by assuming heteroscedastic normal distributions with respect to the D-optimal designs for the gamma, or Poisson, distribution, as the true one.


2021 ◽  
Author(s):  
Johanna Fink ◽  
Ralf Seidler

<p>Drilling boreholes during exploration and development of geothermal reservoirs not only involves high cost, but also bears significant risks of failure. In geothermal reservoir engineering, techniques of optimal experimental design (OED) have the potential to improve the decision making process. Previous publications explained the formulation and implementation of this mathematical optimization problem and demonstrated its feasibility for finding borehole locations in two- and three-dimensional reservoir models that minimize the uncertainty of estimating hydraulic permeability of a model unit from temperature measurements. Subsequently, minimizing the uncertainty of the parameter estimation results in a more reliable parametrization of the reservoir simulation, improving the overall process in geothermal reservoir engineering.</p><p>Various OED techniques are implemented in the Environment for Combining Optimization and Simulation Software (EFCOSS). To address problems arising from geothermal modeling, this software framework links mathematical optimization software with SHEMAT-Suite, a geothermal simulation code for fluid flow and heat transport through porous media. This contribution shows how to determine experimental conditions such that the uncertainty when estimating different parameters of model units from temperature measurements in the borehole is minimized. Numerical simulations of synthetic geothermal reservoir scenarios are presented to demonstrate the OED workflow and its applicability to geothermal reservoir modeling</p>


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