Kinetic Modeling as a Tool to Integrate Multilevel Dynamic Experimental Data

Mogilevskaya, Ekaterina A.; Bagrova, Natalia; Plyusnina, Т. Yu.; Gizzatkulov, Nail; Metelkin, Eugeniy; Goryacheva, E. A.; Smirnov, Sergey A.; Kosinsky, Yuriy; Dorodnov, Aleksander; Peskov, Kirill; Karelina, Tatiana; Goryanin, Igor; Demin, Oleg

doi:10.1007/978-1-60761-175-2_11

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…In the model, the results present the correlation between mRNA and protein levels as a function of both the kinetic parameters and concentration of ribosomes at the reference state. In another study, Mogilevskaya et al (2009) presented an approach to developing mathematical description of metabolic pathways, and then to integrate reaction kinetics and different types of experimental data including proteomic, mRNA and metabolite data.…”

Section: Methodologies For Integrated Transcriptomics and Proteomicsmentioning

confidence: 99%

Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies

Zhang

Li²,

Nie³

2010

Microbiology

415

241

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Recent advances in various ‘omics’ technologies enable quantitative monitoring of the abundance of various biological molecules in a high-throughput manner, and thus allow determination of their variation between different biological states on a genomic scale. Several popular ‘omics’ platforms that have been used in microbial systems biology include transcriptomics, which measures mRNA transcript levels; proteomics, which quantifies protein abundance; metabolomics, which determines abundance of small cellular metabolites; interactomics, which resolves the whole set of molecular interactions in cells; and fluxomics, which establishes dynamic changes of molecules within a cell over time. However, no single ‘omics’ analysis can fully unravel the complexities of fundamental microbial biology. Therefore, integration of multiple layers of information, the multi-‘omics’ approach, is required to acquire a precise picture of living micro-organisms. In spite of this being a challenging task, some attempts have been made recently to integrate heterogeneous ‘omics’ datasets in various microbial systems and the results have demonstrated that the multi-‘omics’ approach is a powerful tool for understanding the functional principles and dynamics of total cellular systems. This article reviews some basic concepts of various experimental ‘omics’ approaches, recent application of the integrated ‘omics’ for exploring metabolic and regulatory mechanisms in microbes, and advances in computational and statistical methodologies associated with integrated ‘omics’ analyses. Online databases and bioinformatic infrastructure available for integrated ‘omics’ analyses are also briefly discussed.

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Section: Methodologies For Integrated Transcriptomics and Proteomicsmentioning

confidence: 99%

Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies

Zhang

Li²,

Nie³

2010

Microbiology

415

241

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“…Model parameterisation included three main stages, in accordance with the model architecture, and following the strategy of step-by-step integration of multi-level experimental data within kinetic models [32]. The model has been decomposed into smaller sub-systems, which have been parameterised separately with the use of suitable experimental data (Figure 2).…”

Section: Resultsmentioning

confidence: 99%

A model of estrogen-related gene expression reveals non-linear effects in transcriptional response to tamoxifen

et al. 2012

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BackgroundEstrogen receptors alpha (ER) are implicated in many types of female cancers, and are the common target for anti-cancer therapy using selective estrogen receptor modulators (SERMs, such as tamoxifen). However, cell-type specific and patient-to-patient variability in response to SERMs (from suppression to stimulation of cancer growth), as well as frequent emergence of drug resistance, represents a serious problem. The molecular processes behind mixed effects of SERMs remain poorly understood, and this strongly motivates application of systems approaches. In this work, we aimed to establish a mathematical model of ER-dependent gene expression to explore potential mechanisms underlying the variable actions of SERMs.ResultsWe developed an equilibrium model of ER binding with 17β-estradiol, tamoxifen and DNA, and linked it to a simple ODE model of ER-induced gene expression. The model was parameterised on the broad range of literature available experimental data, and provided a plausible mechanistic explanation for the dual agonism/antagonism action of tamoxifen in the reference cell line used for model calibration. To extend our conclusions to other cell types we ran global sensitivity analysis and explored model behaviour in the wide range of biologically plausible parameter values, including those found in cancer cells. Our findings suggest that transcriptional response to tamoxifen is controlled in a complex non-linear way by several key parameters, including ER expression level, hormone concentration, amount of ER-responsive genes and the capacity of ER-tamoxifen complexes to stimulate transcription (e.g. by recruiting co-regulators of transcription). The model revealed non-monotonic dependence of ER-induced transcriptional response on the expression level of ER, that was confirmed experimentally in four variants of the MCF-7 breast cancer cell line.ConclusionsWe established a minimal mechanistic model of ER-dependent gene expression, that predicts complex non-linear effects in transcriptional response to tamoxifen in the broad range of biologically plausible parameter values. Our findings suggest that the outcome of a SERM’s action is defined by several key components of cellular micro-environment, that may contribute to cell-type-specific effects of SERMs and justify the need for the development of combinatorial biomarkers for more accurate prediction of the efficacy of SERMs in specific cell types.

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“…Moreover, these processes can be either relatively fast or slow depending on the values of rate constants obtained from experimental data fitting. This grouping of all processes into two sets (fast and slow processes) allowed us to reduce the initial complexity of the catalytic cycle and derive rate equations describing the operation of 5-LO according to the methods described in [27]. …”

Section: Methodsmentioning

confidence: 99%

“…Under these conditions F a represents the sum of active states of 5-LO and changes with time. All rate equations were derived on the basis of the quasi-steady state approach [27]. …”

Section: Methodsmentioning

confidence: 99%

Regulation of leukotriene and 5oxoETE synthesis and the effect of 5-lipoxygenase inhibitors: a mathematical modeling approach

Karelina

Zhudenkov

Demin

et al. 2012

BMC Systems Biology

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Background5-lipoxygenase (5-LO) is a key enzyme in the synthesis of leukotrienes and 5-Oxo-6E,8Z,11Z,14Z-eicosatetraenoic acid (oxoETE). These inflammatory signaling molecules play a role in the pathology of asthma and so 5-LO inhibition is a promising target for asthma therapy. The 5-LO redox inhibitor zileuton (Zyflo IR/CR®) is currently marketed for the treatment of asthma in adults and children, but widespread use of zileuton is limited by its efficacy/safety profile, potentially related to its redox characteristics. Thus, a quantitative, mechanistic description of its functioning may be useful for development of improved anti-inflammatory targeting this mechanism.ResultsA mathematical model describing the operation of 5-LO, phospholipase A2, glutathione peroxidase and 5-hydroxyeicosanoid dehydrogenase was developed. The catalytic cycles of the enzymes were reconstructed and kinetic parameters estimated on the basis of available experimental data. The final model describes each stage of cys-leukotriene biosynthesis and the reactions involved in oxoETE production. Regulation of these processes by substrates (phospholipid concentration) and intracellular redox state (concentrations of reduced glutathione, glutathione (GSH), and lipid peroxide) were taken into account. The model enabled us to reveal differences between redox and non-redox 5-LO inhibitors under conditions of oxidative stress. Despite both redox and non-redox inhibitors suppressing leukotriene A4 (LTA4) synthesis, redox inhibitors are predicted to increase oxoETE production, thus compromising efficacy. This phenomena can be explained in terms of the pseudo-peroxidase activity of 5-LO and the ability of lipid peroxides to transform 5-LO into its active form even in the presence of redox inhibitors.ConclusionsThe mathematical model developed described quantitatively different mechanisms of 5-LO inhibition and simulations revealed differences between the potential therapeutic outcomes for these mechanisms.

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Kinetic Modeling as a Tool to Integrate Multilevel Dynamic Experimental Data

Cited by 13 publications

References 18 publications

Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies

Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies

A model of estrogen-related gene expression reveals non-linear effects in transcriptional response to tamoxifen

Regulation of leukotriene and 5oxoETE synthesis and the effect of 5-lipoxygenase inhibitors: a mathematical modeling approach

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