13C labeling experiments at metabolic nonstationary conditions: An exploratory study

<p>Abstract</p> <p>Background</p> <p>Stimulus Response Experiments to unravel the regulatory properties of metabolic networks are becoming more and more popular. However, their ability to determine enzyme kinetic parameters has proven to be limited with the presently available data. In metabolic flux analysis, the use of ¹³C labeled substrates together with isotopomer modeling solved the problem of underdetermined networks and increased the accuracy of flux estimations significantly.</p> <p>Results</p> <p>In this contribution, the idea of increasing the information content of the dynamic experiment by adding ¹³C labeling is analyzed. For this purpose a small example network is studied by simulation and statistical methods. Different scenarios regarding available measurements are analyzed and compared to a non-labeled reference experiment. Sensitivity analysis revealed a specific influence of the kinetic parameters on the labeling measurements. Statistical methods based on parameter sensitivities and different measurement models are applied to assess the information gain of the labeled stimulus response experiment.</p> <p>Conclusion</p> <p>It was found that the use of a (specifically) labeled substrate will significantly increase the parameter estimation accuracy. An overall information gain of about a factor of six is observed for the example network. The information gain is achieved from the specific influence of the kinetic parameters towards the labeling measurements. This also leads to a significant decrease in correlation of the kinetic parameters compared to an experiment without ¹³C-labeled substrate.</p

An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments

<p>Abstract</p> <p>Background</p> <p>Metabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from ¹³<it>C </it>isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the ¹³<it>C </it>isotopomer data are typically needed.</p> <p>Results</p> <p>We present a novel analytic framework for estimating metabolic flux ratios in the cell from ¹³<it>C </it>isotopomer measurement data. In the presented framework, equation systems constraining the fluxes are derived automatically from the model of the metabolism of an organism. The framework is designed to be applicable with all metabolic network topologies, ¹³<it>C </it>isotopomer measurement techniques, substrates and substrate labelling patterns.</p> <p>By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms <it>Bacillus subtilis </it>and <it>Saccharomyces cerevisiae </it>we show that our framework is able to automatically produce the flux ratios discovered so far by the domain experts with tedious manual analysis. Furthermore, we show by <it>in silico </it>calculability analysis that our framework can rapidly produce flux ratio equations – as well as predict when the flux ratios are unobtainable by linear means – also for substrates not related to glucose.</p> <p>Conclusion</p> <p>The core of ¹³<it>C </it>metabolic flux analysis framework introduced in this article constitutes of flow and independence analysis of metabolic fragments and techniques for manipulating isotopomer measurements with vector space techniques. These methods facilitate efficient, analytic computation of the ratios between the fluxes of pathways that converge to a common junction metabolite. The framework can been seen as a generalization and formalization of existing tradition for computing metabolic flux ratios where equations constraining flux ratios are manually derived, usually without explicitly showing the formal proofs of the validity of the equations.</p

Semi-automatic drawing of Metabolic networks

In the living cell, biochemical reactions catalyzed by enzymes are the drivers for metabolic processes like growth, energy production, and replication. Metabolic networks are the representation of these processes describing the complex interactions of biochemical compounds. The large amount of manifold data concerning metabolic networks continually arising from current research activities in biotechnology leads to the great challenge of information visualization. Visualizing information in networks first of all requires appropriate network diagrams. In the context of metabolic networks, historical conventions regarding the network layout have been established. These layouts are not realizable by prevailing algorithms for automatic graph drawing. Hence, manual graph drawing is the predominating way to set up metabolic network diagrams. This is very time-consuming without software support, especially considering large networks with more than 500 nodes. We present a semi-automatic approach to drawing networks which relies on manual editing supported by two concepts of the interactive and automatic arrangement of nodes and edges. The first concept, called the layout pattern, uses an arbitrarily shaped skeleton as a backbone for the arrangement of nodes and edges. The second concept allows us to wrap a set of repeating drawing steps onto a so-called motif stamp, which can be appended to other parts of a diagram during the drawing process. Finally, a case study demonstrates that both semi-automatic drawing techniques diminish the time to be devoted for the manual network drawing process. </jats:p