Predicting outcomes of steady-state 13C isotope tracing experiments using Monte Carlo sampling

Schellenberger, Jan; Zielinski, Daniel C.; Choi, Wing-Kit; Madireddi, Sunthosh; Portnoy, Vasiliy A.; Scott, David A.; Reed, Jennifer L.; Osterman, Andrei L.; Palsson, Bernhard Ø.

doi:10.1186/1752-0509-6-9

Cited by 28 publications

(34 citation statements)

References 37 publications

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“…In addition, there are several potential multiplicative factors on the number of measurable like time series measurements, the use of multiple stable isotopes ( 13 C, 15 N, 2 H), and the use of multiple isotope labeling source metabolites. In fact, the design of metabolomics is coming full circle, where metabolic models are being used to design optimal stable isotope labeling experiments [93-96]. However, this approach for metabolomics experimental design appears more robust for high quality metabolic models from model prokaryotic organisms.…”

Section: Methodsmentioning

confidence: 99%

Error Analysis and Propagation in Metabolomics Data Analysis

Moseley

2013

Computational and Structural Biotechnology Journal

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Error analysis plays a fundamental role in describing the uncertainty in experimental results. It has several fundamental uses in metabolomics including experimental design, quality control of experiments, the selection of appropriate statistical methods, and the determination of uncertainty in results. Furthermore, the importance of error analysis has grown with the increasing number, complexity, and heterogeneity of measurements characteristic of ‘omics research. The increase in data complexity is particularly problematic for metabolomics, which has more heterogeneity than other omics technologies due to the much wider range of molecular entities detected and measured. This review introduces the fundamental concepts of error analysis as they apply to a wide range of metabolomics experimental designs and it discusses current methodologies for determining the propagation of uncertainty in appropriate metabolomics data analysis. These methodologies include analytical derivation and approximation techniques, Monte Carlo error analysis, and error analysis in metabolic inverse problems. Current limitations of each methodology with respect to metabolomics data analysis are also discussed.

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Section: Methodsmentioning

confidence: 99%

Error Analysis and Propagation in Metabolomics Data Analysis

Moseley

2013

Computational and Structural Biotechnology Journal

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“…PCC6802 [40]. Schellenberger et al applied a Monte Carlo sampling technique for experimental tracer design to a large-scale Escherichia coli network and found positional [1- 13 C] or [6- 13 C] labeled glucoses to be superior over a commonly used mixture of 20% uniform and 80% unlabeled glucose [41]. Here, unusual multi-positional labeling, in particular [5,6- 13 C]-, [1,2,5- 13 C]-, [1,2- 13 C]-, [1,2,3- 13 C]-, and [2,3- 13 C]-glucose, resulted in a higher identifiability than single positional labeling.…”

Section: Methods and Modelsmentioning

confidence: 99%

A Pareto approach to resolve the conflict between information gain and experimental costs: Multiple-criteria design of carbon labeling experiments

Nöh¹,

Niedenführ²,

Beyß³

et al. 2018

PLoS Comput Biol

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Science revolves around the best way of conducting an experiment to obtain insightful results. Experiments with maximal information content can be found by computational experimental design (ED) strategies that identify optimal conditions under which to perform the experiment. Several criteria have been proposed to measure the information content, each emphasizing different aspects of the design goal, i.e., reduction of uncertainty. Where experiments are complex or expensive, second sight is at the budget governing the achievable amount of information. In this context, the design objectives cost and information gain are often incommensurable, though dependent. By casting the ED task into a multiple-criteria optimization problem, a set of trade-off designs is derived that approximates the Pareto-frontier which is instrumental for exploring preferable designs. In this work, we present a computational methodology for multiple-criteria ED of information-rich experiments that accounts for virtually any set of design criteria. The methodology is implemented for the case of 13C metabolic flux analysis (MFA), which is arguably the most expensive type among the ‘omics’ technologies, featuring dozens of design parameters (tracer composition, analytical platform, measurement selection etc.). Supported by an innovative visualization scheme, we demonstrate with two realistic showcases that the use of multiple criteria reveals deep insights into the conflicting interplay between information carriers and cost factors that are not amendable to single-objective ED. For instance, tandem mass spectrometry turns out as best-in-class with respect to information gain, while it delivers this information quality cheaper than the other, routinely applied analytical technologies. Therewith, our Pareto approach to ED offers the investigator great flexibilities in the conception phase of a study to balance costs and benefits.

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“…The Monte Carlo approach is used frequently in MFA studies [26]. In this work, the method is used because of the non-linearity and constraints in the optimization problem.…”

Section: Methodsmentioning

confidence: 99%

Dynamic estimation of specific fluxes in metabolic networks using non-linear dynamic optimization

2014

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BackgroundMetabolic network models describing the biochemical reaction network and material fluxes inside microorganisms open interesting routes for the model-based optimization of bioprocesses. Dynamic metabolic flux analysis (dMFA) has lately been studied as an extension of regular metabolic flux analysis (MFA), rendering a dynamic view of the fluxes, also in non-stationary conditions. Recent dMFA implementations suffer from some drawbacks, though. More specifically, the fluxes are not estimated as specific fluxes, which are more biologically relevant. Also, the flux profiles are not smooth, and additional constraints like, e.g., irreversibility constraints on the fluxes, cannot be taken into account. Finally, in all previous methods, a basis for the null space of the stoichiometric matrix, i.e., which set of free fluxes is used, needs to be chosen. This choice is not trivial, and has a large influence on the resulting estimates.ResultsIn this work, a new methodology based on a B-spline parameterization of the fluxes is presented. Because of the high degree of non-linearity due to this parameterization, an incremental knot insertion strategy has been devised, resulting in a sequence of non-linear dynamic optimization problems. These are solved using state-of-the-art dynamic optimization methods and tools, i.e., orthogonal collocation, an interior-point optimizer and automatic differentiation. Also, a procedure to choose an optimal basis for the null space of the stoichiometric matrix is described, discarding the need to make a choice beforehand. The proposed methodology is validated on two simulated case studies: (i) a small-scale network with 7 fluxes, to illustrate the operation of the algorithm, and (ii) a medium-scale network with 68 fluxes, to show the algorithm’s capabilities for a realistic network. The results show an accurate correspondence to the reference fluxes used to simulate the measurements, both in a theoretically ideal setting with no experimental noise, and in a realistic noise setting.ConclusionsBecause, apart from a metabolic reaction network and the measurements, no extra input needs to be given, the resulting algorithm is a systematic, integrated and accurate methodology for dynamic metabolic flux analysis that can be run online in real-time if necessary.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-014-0132-0) contains supplementary material, which is available to authorized users.

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Predicting outcomes of steady-state 13C isotope tracing experiments using Monte Carlo sampling

Cited by 28 publications

References 37 publications

Error Analysis and Propagation in Metabolomics Data Analysis

Error Analysis and Propagation in Metabolomics Data Analysis

A Pareto approach to resolve the conflict between information gain and experimental costs: Multiple-criteria design of carbon labeling experiments

Dynamic estimation of specific fluxes in metabolic networks using non-linear dynamic optimization

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