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Ramos, António Manuel Pinho; Neves, Ana Rute; Santos, Helena

doi:10.1023/a:1020664422633

Cited by 22 publications

(3 citation statements)

References 46 publications

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“…A number of mathematical modelling approaches have been used in LAB research to integrate experimental data from detailed biochemical studies on transport process [44] and enzyme kinetics (see, e.g. [45,46]), supplemented with flux and metabolites measurements, such as in-vivo NMR [47,48]. The genomics approach to LAB physiology has been accompanied in recent years by the use of genome-scale metabolic network models [15], as discussed above.…”

Section: Reviewmentioning

confidence: 99%

Systems biology of lactic acid bacteria: a critical review

2011

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Understanding the properties of a system as emerging from the interaction of well described parts is the most important goal of Systems Biology. Although in the practice of Lactic Acid Bacteria (LAB) physiology we most often think of the parts as the proteins and metabolites, a wider interpretation of what a part is can be useful. For example, different strains or species can be the parts of a community, or we could study only the chemical reactions as the parts of metabolism (and forgetting about the enzymes that catalyze them), as is done in flux balance analysis. As long as we have some understanding of the properties of these parts, we can investigate whether their interaction leads to novel or unanticipated behaviour of the system that they constitute.There has been a tendency in the Systems Biology community to think that the collection and integration of data should continue ad infinitum, or that we will otherwise not be able to understand the systems that we study in their details. However, it may sometimes be useful to take a step back and consider whether the knowledge that we already have may not explain the system behaviour that we find so intriguing. Reasoning about systems can be difficult, and may require the application of mathematical techniques. The reward is sometimes the realization of unexpected conclusions, or in the worst case, that we still do not know enough details of the parts, or of the interactions between them.We will discuss a number of cases, with a focus on LAB-related work, where a typical systems approach has brought new knowledge or perspective, often counterintuitive, and clashing with conclusions from simpler approaches. Also novel types of testable hypotheses may be generated by the systems approach, which we will illustrate. Finally we will give an outlook on the fields of research where the systems approach may point the way for the near future.

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Section: Reviewmentioning

confidence: 99%

Systems biology of lactic acid bacteria: a critical review

2011

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“…We assume their formation is an analogue reaction (α-acetolactic acid to acetoin), as described for lactic acid bacteria. 19 In our investigations the cyanobacteria released hydroxyketones with a chain and β-ionone-5,6-epoxide increased. β-Ionol, dihydro-β-ionol, 4-oxo-β-ionone and 2,4,4-trimethyl-3-(3-oxobutyl)-2-cyclohexen-1-one were present, but did not accumulate to higher concentrations.…”

Section: Discussionmentioning

confidence: 46%

Off-flavours in water: hydroxyketones andβ-ionone derivatives as new odour compounds of freshwater cyanobacteria

Höckelmann

Jüttner

2005

Flavour Fragr. J.

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Cyanobacteria essentially contribute to the off-flavours of drinking water and freshwater fish. They are primary nuisance microorganisms and their control in lakes and ponds used for the production of drinking water and fish farming is expensive. We describe new odour compounds of freshwater cyanobacteria that are fermentation products and norcarotenoids. These may contribute to the off-flavours frequently observed with cyanobacteria. The fermentation products were represented by 2,3-pentandione, 2,3-hexandione, 2-hydroxy-3-pentanone, 3-hydroxy-2-pentanone, 2-hydroxy-3-hexanone, 3-hydroxy-2-hexanone and 2,3-hexandiol. These compounds are important for odour quality in microbially-processed dairy food, caused by the metabolism of fungi and heterotrophic bacteria. Their cyanobacterial synthesis was shown by 13 C-labelling, growth kinetics and biotransformation experiments. The labelled mass spectra of the hydroxyketones showed unequivocally the cyanobacterial rather than bacterial origin of the compounds. Norcarotenoids of the β β β β β-ionone-type (β β β β β-ionol, β β β β β-ionone-5,6-epoxide, dihydro-β β β β β-ionone, dihydro-β β β β β-ionol, tetrahydroionone, 4-oxo-β β β β β-ionone and 2,4,4-trimethyl-3-(3-oxobutyl)-2-cyclohexen-1-one) were another important group of compounds that were found in axenic cyanobacterial cultures and a monoxenic culture of Phormidium sp. The results implicate the presence of further oxygenases in cyanobacteria. Except for tetrahydroionone, the syntheses of the β β β β β-ionone derivatives were established by biotransformation experiments applying the precursor β β β β β-ionone. The norcarotenoid 6-methyl-5-hepten-2-one, which is regarded as a precursor compound of 1,3,3-trimethyl-2,7-dioxabicylo(2,2,1)heptane (TDH), was 13 C-labelled, while TDH did not show any labelling. There is evidence that TDH arises from the analytical procedure and is an artefact rather than a biogenic compound produced by the cyanobacteria.

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“…One is the generic inhibition and activation model [9]–[12], which is well known and useful in validating our approach. The other is the simplified model of glycolysis of Lactococcus lactis MG1363 [17], [29]–[31]. Actual experimental data are available for this model and it is therefore useful for verifying whether our approach is practical.…”

Section: Methodsmentioning

confidence: 99%

Identification of a Metabolic Reaction Network from Time-Series Data of Metabolite Concentrations

2013

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Recent development of high-throughput analytical techniques has made it possible to qualitatively identify a number of metabolites simultaneously. Correlation and multivariate analyses such as principal component analysis have been widely used to analyse those data and evaluate correlations among the metabolic profiles. However, these analyses cannot simultaneously carry out identification of metabolic reaction networks and prediction of dynamic behaviour of metabolites in the networks. The present study, therefore, proposes a new approach consisting of a combination of statistical technique and mathematical modelling approach to identify and predict a probable metabolic reaction network from time-series data of metabolite concentrations and simultaneously construct its mathematical model. Firstly, regression functions are fitted to experimental data by the locally estimated scatter plot smoothing method. Secondly, the fitted result is analysed by the bivariate Granger causality test to determine which metabolites cause the change in other metabolite concentrations and remove less related metabolites. Thirdly, S-system equations are formed by using the remaining metabolites within the framework of biochemical systems theory. Finally, parameters including rate constants and kinetic orders are estimated by the Levenberg–Marquardt algorithm. The estimation is iterated by setting insignificant kinetic orders at zero, i.e., removing insignificant metabolites. Consequently, a reaction network structure is identified and its mathematical model is obtained. Our approach is validated using a generic inhibition and activation model and its practical application is tested using a simplified model of the glycolysis of Lactococcus lactis MG1363, for which actual time-series data of metabolite concentrations are available. The results indicate the usefulness of our approach and suggest a probable pathway for the production of lactate and acetate. The results also indicate that the approach pinpoints a probable strong inhibition of lactate on the glycolysis pathway.

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Untitled

Cited by 22 publications

References 46 publications

Systems biology of lactic acid bacteria: a critical review

Systems biology of lactic acid bacteria: a critical review

Off-flavours in water: hydroxyketones andβ-ionone derivatives as new odour compounds of freshwater cyanobacteria

Identification of a Metabolic Reaction Network from Time-Series Data of Metabolite Concentrations

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