![]() ![]() ; Noor, Fozia ![]() in Journal of biotechnology (2011), 155(3), 299-307 Verapamil has been shown to inhibit glucose transport in several cell types. However, the consequences of this inhibition on central metabolism are not well known. In this study we focused on verapamil ... [more ▼] Verapamil has been shown to inhibit glucose transport in several cell types. However, the consequences of this inhibition on central metabolism are not well known. In this study we focused on verapamil induced changes in metabolic fluxes in a murine atrial cell line (HL-1 cells). These cells were adapted to serum free conditions and incubated with 4 muM verapamil and [U-(1)(3)C(5)] glutamine. Specific extracellular metabolite uptake/production rates together with mass isotopomer fractions in alanine and glutamate were implemented into a metabolic network model to calculate metabolic flux distributions in the central metabolism. Verapamil decreased specific glucose consumption rate and glycolytic activity by 60%. Although the HL-1 cells show Warburg effect with high lactate production, verapamil treated cells completely stopped lactate production after 24 h while maintaining growth comparable to the untreated cells. Calculated fluxes in TCA cycle reactions as well as NADH/FADH(2) production rates were similar in both treated and untreated cells. This was confirmed by measurement of cell respiration. Reduction of lactate production seems to be the consequence of decreased glucose uptake due to verapamil. In case of tumors, this may have two fold effects; firstly depriving cancer cells of substrate for anaerobic glycolysis on which their growth is dependent; secondly changing pH of the tumor environment, as lactate secretion keeps the pH acidic and facilitates tumor growth. The results shown in this study may partly explain recent observations in which verapamil has been proposed to be a potential anticancer agent. Moreover, in biotechnological production using cell lines, verapamil may be used to reduce glucose uptake and lactate secretion thereby increasing protein production without introduction of genetic modifications and application of more complicated fed-batch processes. [less ▲] Detailed reference viewed: 98 (0 UL)![]() He, Feng ![]() ![]() in Journal of Biotechnology (2009), 144(3), 190-203 Reverse engineering of gene networks aims at revealing the structure of the gene regulation network in a biological system by reasoning backward directly from experimental data. Many methods have recently ... [more ▼] Reverse engineering of gene networks aims at revealing the structure of the gene regulation network in a biological system by reasoning backward directly from experimental data. Many methods have recently been proposed for reverse engineering of gene networks by using gene transcript expression data measured by microarray. Whereas the potentials of the methods have been well demonstrated, the assumptions and limitations behind them are often not clearly stated or not well understood. In this review, we first briefly explain the principles of the major methods, identify the assumptions behind them and pinpoint the limitations and possible pitfalls in applying them to real biological questions. With regard to applications, we then discuss challenges in the experimental verification of gene networks generated from reverse engineering methods. We further propose an optimal experimental design for allocating sampling schedule and possible strategies for reducing the limitations of some of the current reverse engineering methods. Finally, we examine the perspectives for the development of reverse engineering and urge the need to move from revealing network structure to the dynamics of biological systems. [less ▲] Detailed reference viewed: 184 (20 UL)![]() ![]() Sauter, Thomas ![]() in Journal of Biotechnology (2004), 110(2), 181-99 Bacterial signal processing was investigated concerning the sucrose phosphotransferase system (sucrose PTS) in the bacterium Escherichia coli as an example. The about 20 different phosphotransferase ... [more ▼] Bacterial signal processing was investigated concerning the sucrose phosphotransferase system (sucrose PTS) in the bacterium Escherichia coli as an example. The about 20 different phosphotransferase systems (PTSs) of the cell fulfill besides the transport of various carbohydrates, also the function of one signal processing system. Extra- and intracellular signals are converted within the PTS protein chain to important regulatory signals affecting, e.g. carbon metabolism and chemotaxis. A detailed dynamical model of the sucrose PTS was developed describing transport and signal processing function. It was formulated using a detailed description of complex formation and phosphate transfer between the chain proteins. Model parameters were taken from literature or were identified with own experiments. Simulation studies together with experimental hints showed that the dynamic behavior of phosphate transfer in the PTS runs within 1 s. Therefore a description of steady state characteristics is sufficient for describing the signaling properties of the sucrose PTS. A steady state characteristic field describes the degree of phosphorylation of the PTS protein EIIACrr as a function of the input variables extracellular sucrose concentration and intracellular phosphoenolpyruvate (PEP):pyruvate ratio. The model has been validated with different experiments performed in a CSTR using a sucrose positive E. coli W3110 derivative. A method for determining intracellular metabolite concentrations has been developed. A sample preparation technique using a boiling ethanol buffer solution was successfully applied. The PTS output signal degree of phosphorylation of EIIACrr was also measured. Steady state conditions with varying dilution rate and dissolved oxygen concentration and dynamical variations applying different stimuli to the culture were considered. Pulse, and stop feeding experiments with limiting sucrose concentrations were performed. Simulation and experimental results matched well. The same holds for the expanded sucrose PTS and glycolysis model. [less ▲] Detailed reference viewed: 128 (1 UL) |
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