Intelligent Models of the Quantitative Behavior of Microbial Systems

Patnaik, Pratap R.

doi:10.1007/s11947-008-0112-8

Cited by 8 publications

(7 citation statements)

References 78 publications

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“…To avoid the limitations and difficulties with FNNs (Patnaik 2009b;Yegnanarayana 2004), some research workers have preferred GAs. Because GA is a stochastic global search method that is inspired by the principles of natural biological evolution, it has a close correspondence with the dynamics of cellular processes such as the present fermentation with S. cerevisiae.…”

Section: Application and Discussionmentioning

confidence: 99%

Supervisory Expert System-Based Intelligent Optimization of a Microbioreactor

Patnaik

2014

Applied Artificial Intelligence

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Microbioreactors with immobilized yeast cells are conventionally packed uniformly. A recent study has shown, however, that a topologically optimized distribution of cells yields much greater outputs of the desired product. Because topology optimization is a complex method requiring a good mathematical model, artificial intelligence (AI) has been employed here as an alternative method. For the same system-in other words, immobilized genetically modified yeast cells-an expert system selected online the better of two AI methods-a fuzzy neural network (FNN) and a genetic algorithm (GA)-according to the output of the product recombinant glucoamylase. Progressing in short time intervals enables the expert system toshift continually between the FNN and the GA, thereby maintaining optimal performance at all times. This method is more robust than topology optimization, easier to implement, does not require a mathematical model, and improves glucoamylase output even further.

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Section: Application and Discussionmentioning

confidence: 99%

Supervisory Expert System-Based Intelligent Optimization of a Microbioreactor

Patnaik

2014

Applied Artificial Intelligence

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“…This method, widely used in prior research [25,67-69], incorporates a genetic algorithm and a ranking procedure to select nondominated solutions. Specifically, we used the Matlab function gamultiobj , from the Global Optimization Toolbox.…”

Section: Methodsmentioning

confidence: 99%

Comparative multi-goal tradeoffs in systems engineering of microbial metabolism

2012

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BackgroundMetabolic engineering design methodology has evolved from using pathway-centric, random and empirical-based methods to using systems-wide, rational and integrated computational and experimental approaches. Persistent during these advances has been the desire to develop design strategies that address multiple simultaneous engineering goals, such as maximizing productivity, while minimizing raw material costs.ResultsHere, we use constraint-based modeling to systematically design multiple combinations of medium compositions and gene-deletion strains for three microorganisms (Escherichia coli, Saccharomyces cerevisiae, and Shewanella oneidensis) and six industrially important byproducts (acetate, D-lactate, hydrogen, ethanol, formate, and succinate). We evaluated over 435 million simulated conditions and 36 engineering metabolic traits, including product rates, costs, yields and purity.ConclusionsThe resulting metabolic phenotypes can be classified into dominant clusters (meta-phenotypes) for each organism. These meta-phenotypes illustrate global phenotypic variation and sensitivities, trade-offs associated with multiple engineering goals, and fundamental differences in organism-specific capabilities. Given the increasing number of sequenced genomes and corresponding stoichiometric models, we envisage that the proposed strategy could be extended to address a growing range of biological questions and engineering applications.

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“…(1) Knowledge is acquired through learning (training) processes, and (2) Knowledge is stored via inter-neuron connection strengths (weights). While neural networks have been used to various capacities within food and bioprocess technologies, such as the work of [9], [10], and [11], our focus will be on the application of neural network towards solving large scale problem for biosecurity purpose.…”

Section: Introductionmentioning

confidence: 99%

A neural network structure for prediction of chemical agent fate

2014

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This work presents the development of a multi-input, multi-output neural network structure to predict the time dependent concentration of chemical agents as they participate in chemical reaction with environmental substrates or moisture content within these substrates. The neural network prediction is based on a computationally or experimentally produced database that includes the concentration of all chemicals presents (reactants and products) as a function of the chemical agent droplet size, wind speed, temperature, and turbulence. The utilization of this prediction structure is made userfriendly via an easy-to-use graphical user interface. Furthermore, upon the knowledge of the time-varying environmental parameters (wind speed and temperature that are usually recorded and available), the time varying concentration of all chemicals can be predicted almost instantaneously by recalling the previously trained network. The network prediction was compared with actual open air test data and the results were found to match.

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Intelligent Models of the Quantitative Behavior of Microbial Systems

Cited by 8 publications

References 78 publications

Supervisory Expert System-Based Intelligent Optimization of a Microbioreactor

Supervisory Expert System-Based Intelligent Optimization of a Microbioreactor

Comparative multi-goal tradeoffs in systems engineering of microbial metabolism

A neural network structure for prediction of chemical agent fate

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