Integration of data mining into a nonlinear experimental design approach for improved performance

2009

Biotechnology Progress

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Optimization of fermentation media and processes is a difficult task due to the potential for high dimensionality and nonlinearity. Here we develop and evaluate variations on two novel and highly efficient methods for experimental fermentation optimization. The first approach is based on using a truncated genetic algorithm with a developing neural network model to choose the best experiments to run. The second approach uses information theory, along with Bayesian regularized neural network models, for experiment selection. To evaluate these methods experimentally, we used them to develop a new chemically defined medium for Lactococcus lactis IL1403, along with an optimal temperature and initial pH, to achieve maximum cell growth. The media consisted of 19 defined components or groups of components. The optimization results show that the maximum cell growth from the optimal process of each novel method is generally comparable to or higher than that achieved using a traditional statistical experimental design method, but these optima are reached in about half of the experiments (73-94 vs. 161, depending on the variants of methods). The optimal chemically defined media developed in this work are rich media that can support high cell density growth 3.5-4 times higher than the best reported synthetic medium and 72% higher than a commonly used complex medium (M17) at optimization scale. The best chemically defined medium found using the method was evaluated and compared with other defined or complex media at flask- and fermentor-scales.

Section: Discussionmentioning

confidence: 99%

Using highly efficient nonlinear experimental design methods for optimization of Lactococcus lactis fermentation in chemically defined media

Zhang

2009

Biotechnology Progress

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“…Oftentimes experimenters will employ polynomial models to find optimal culture conditions [ 5 ] but only after extensive DOE to reduce the dimensionality of the problem space to < 5. More advanced modeling techniques are neural networks, decision trees [ 6 ] and Gaussian processes, [ 7 ] which are often better at generalizing noisy, nonlinear, and multi‐modal data. When combined with global optimization methods, Zhang and Block demonstrated that these response surface methods can optimize problems with > 20 variables in less than half the number of experiments as traditional DOE.…”

Section: Introductionmentioning

confidence: 99%

Optimization of muscle cell culture media using nonlinear design of experiments

Cosenza

Baar

2021

Biotechnology Journal

Optimizing media for biological processes, such as those used in tissue engineering and cultivated meat production, is difficult due to the extensive experimentation required, number of media components, nonlinear and interactive responses, and the number of conflicting design objectives. Here we demonstrate the capacity of a nonlinear design‐of‐experiments (DOE) method to predict optimal media conditions in fewer experiments than a traditional DOE. The approach is based on a hybridization of a coordinate search for local optimization with dynamically adjusted search spaces and a global search method utilizing a truncated genetic algorithm using radial basis functions to store and model prior knowledge. Using this method, we were able to reduce the cost of muscle cell proliferation media while maintaining cell growth 48 h after seeding using 30 common components of typical commercial growth medium in fewer experiments than a traditional DOE (70 vs. 103). While we clearly demonstrated that the experimental optimization algorithm significantly outperforms conventional DOE, due to the choice of a 48 h growth assay weighted by medium cost as an objective function, these findings were limited to performance at a single passage, and did not generalize to growth over multiple passages. This underscores the importance of choosing objective functions that align well with process goals.

A generalizable hybrid search framework for optimizing expensive design problems using surrogate models

Cosenza

Engineering Optimization

2020

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Experimental optimization of physical and biological processes is a difficult task.To address this, sequential surrogate models combined with search algorithms have been employed to solve nonlinear high dimensional design problems with expensive objective function evaluations. In this article, a hybrid surrogate framework was built to learn optimal parameters of a diverse set of simulated design problems meant to represent real world physical and biological processes in both dimensionality and nonlinearity. The framework uses a hybrid radial basis function / genetic algorithm with dynamic coordinate search response, utilizing the strengths of both algorithms. The new hybrid method performs better or as good as its constituent algorithms in 19 of 20 high dimensional test functions, making it a very practical surrogate framework for a wide variety of optimization design problems. Experiments also show that the hybrid framework can be improved even more when optimizing processes with simulated noise.