A global optimization approach for metabolic flux analysis based on labeling balances

Riascos, Carlos A.M.; Gombert, Andreas; Pinto, José M.

doi:10.1016/j.compchemeng.2004.08.012

Cited by 19 publications

(8 citation statements)

References 21 publications

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“…A simple representation of how these networks might cascade and interact is shown in Figure 2c, with drug interacting with cell membrane receptors or targets, squares representing genomic information, triangles depicting components of the interactome (molecular interactions within the cell), 21 and circles identifying the elements of metabolic flux networks. 22 …”

Section: Horizontal Flow Of Information and Materialsmentioning

confidence: 99%

Network-Based Approaches in Drug Discovery and Early Development

Harrold

Ramanathan

Mager

2013

Clin Pharmacol Ther

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Identification of novel targets is a critical first step in the drug discovery and development process. Most diseases such as cancer, metabolic disorders, and neurological disorders are complex, and their pathogenesis involves multiple genetic and environmental factors. Finding a viable drug target–drug combination with high potential for yielding clinical success within the efficacy–toxicity spectrum is extremely challenging. Many examples are now available in which network-based approaches show potential for the identification of novel targets and for the repositioning of established targets. The objective of this article is to highlight network approaches for identifying novel targets with greater chances of gaining approved drugs with maximal efficacy and minimal side effects. Further enhancement of these approaches may emerge from effectively integrating computational systems biology with pharmacodynamic systems analysis. Coupling genomics, proteomics, and metabolomics databases with systems pharmacology modeling may aid in the development of disease-specific networks that can be further used to build confidence in target identification.

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Section: Horizontal Flow Of Information and Materialsmentioning

confidence: 99%

Network-Based Approaches in Drug Discovery and Early Development

Harrold

Ramanathan

Mager

2013

Clin Pharmacol Ther

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“…Metabolic flux analysis was carried out for the calculation of volumetric rates of formation of intracellular metabolites (Nielsen 2001;Riascosa et al 2005). Metabolic flux analysis is based on the pseudo-steady-state assumption for intracellular metabolites, which means that there is no accumulation of any intermediates:…”

Section: Metabolic Flux Analysismentioning

confidence: 99%

Screening, breeding and metabolic modulating of a strain producing succinic acid with corn straw hydrolyte

Zheng

Wei

et al. 2008

World J Microbiol Biotechnol

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Purpose In this paper, the high yield mutant strain was expected to be obtained for the industrial bioconversion of corn straw hydrolyte to succinic acid. Methods API biochemical reactions and 16S r RNA sequence analysis were carried out for identification, and then the strain's metabolic pathway and its relevant enzymes were discussed for the metabolic flux analysis (MFA). The X-ray of synchronous radiation and sitedirected mutagenesis were imported for decreasing those byproducts, and the metabolic technics was also modulated. Results Identification showed that the succinicacid-producing strain S.JST belonged to Actinobacillus succinogenes species. Metabolic pathway analysis indicated that this strain had the character of utilizing glucose and xylose simultaneously, which was a great advantage considering the fact that most crop straw hydrolyte included glucose and xylose. Metabolic flux analysis showed that acetic acid and alcohol competed with the flux of succinic acid, and the analysis of [H] reducing power balance investigated that the [H] electronic donor produced in the cell was not enough for the metabolism of succinic acid. Then X-ray mutagenesis showed that the flux of byproduct acetic acid was decreased to 0.666 from 1.233 mmol/g DW h and that two mutated sites were found in pta (gene of phosphotransacetylase) from mutant strain M.JSTP, but less succinic acid was produced due to the worse lacking in the supply of [H] reducing power. The site-directed mutagenesis showed that the flux of byproduct alcohol was successfully decreased to 0.029 from 1.303 mmol/g DW h. Then, surrounding the flux ratio of HMP to EMP, the metabolic technics was modulated with the addition of citrate in order to improve the balance of [H] reducing power, thus the succinic acid flux of the mutant M.JSTA was increased to 3.163 from 2.480 mmol/g DW h comparing with the parent strain S.JST. Conclusions The mutant strain obtained in this paper deserves to be scaled-up.

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“…Development of computational methods for 13 C-based flux analysis has, therefore, focused on speeding up the calculation of amino acid/metabolite labeling from an assumed set of fluxes. Several methods have been proposed, including the iterative averaging isotopomer method (Schmidt, Nielsen, & Villadsen, 1999a), the cumomer method (Wiechert et al, 1999), the Elementary Metabolic Unit (EMU) method (Antoniewicz, Kelleher, & Stephanopoulos, 2007b), the isotopomer path tracing method (Forbes, Clark, & Blanch, 2001), and the fractional labeling method (Riascos, Gombert, & Pinto, 2005). In practice, the only methods under continuous development are the cumomer and EMU method.…”

Section: High-performance Flux Calculation Algorithmsmentioning

confidence: 99%

Advances in analysis of microbial metabolic fluxes via ¹³C isotopic labeling

Tang

Martín

Myers

et al. 2008

Mass Spectrometry Reviews

128

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Metabolic flux analysis via (13)C labeling ((13)C MFA) quantitatively tracks metabolic pathway activity and determines overall enzymatic function in cells. Three core techniques are necessary for (13)C MFA: (1) a steady state cell culture in a defined medium with labeled-carbon substrates; (2) precise measurements of the labeling pattern of targeted metabolites; and (3) evaluation of the data sets obtained from mass spectrometry measurements with a computer model to calculate the metabolic fluxes. In this review, we summarize recent advances in the (13)C-flux analysis technologies, including mini-bioreactor usage for tracer experiments, isotopomer analysis of metabolites via high resolution mass spectrometry (such as GC-MS, LC-MS, or FT-ICR), high performance and large-scale isotopomer modeling programs for flux analysis, and the integration of fluxomics with other functional genomics studies. It will be shown that there is a significant value for (13)C-based metabolic flux analysis in many biological research fields.

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A global optimization approach for metabolic flux analysis based on labeling balances

Cited by 19 publications

References 21 publications

Network-Based Approaches in Drug Discovery and Early Development

Network-Based Approaches in Drug Discovery and Early Development

Screening, breeding and metabolic modulating of a strain producing succinic acid with corn straw hydrolyte

Advances in analysis of microbial metabolic fluxes via ¹³C isotopic labeling

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A global optimization approach for metabolic flux analysis based on labeling balances

Cited by 19 publications

References 21 publications

Network-Based Approaches in Drug Discovery and Early Development

Network-Based Approaches in Drug Discovery and Early Development

Screening, breeding and metabolic modulating of a strain producing succinic acid with corn straw hydrolyte

Advances in analysis of microbial metabolic fluxes via 13C isotopic labeling

Contact Info

Product

Resources

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Advances in analysis of microbial metabolic fluxes via ¹³C isotopic labeling