190,226 research outputs found
Optimal design for goodness-of-fit of the Michaelis-Menten enzyme kinetic function
No full textWe construct efficient designs for the Michaelis-Menten enzyme kinetic model capable of checking model assumption. An extended model, called EMAX model is also considered for this purpose. This model is widely used in pharmacokinetics and reduces to the Michaelis- Menten model for a specific choice of the parameter setting. Our strategy is to find efficient designs for estimating the parameters in the EMAX model and at the same time test the validity of the Michaelis-Menten model against the EMAX model by maximizing a minimum of the D- or D1-efficiencies taken over a range of values for the nonlinear parameters. In addition, we show that the designs obtained from maximizing the D-efficiencies are (i) efficient for estimating parameters in the EMAX model or the Michaelis-Menten model, (ii) efficient for testing the Michaelis-Menten model against the EMAX model and (iii) robust with respect to misspecification of the unknown parameters. --Chebyshev polynomials,EMAX model,goodness of fit test,locally D-optimal design,robust optimal design
Representative Michaelis-Menten curve.
No full textThe conditions in the experimental wells (200 μL) were 100 μM NADPH, 12.4 nM WbDHFR in 1 X MTEN buffer at pH 6.0 with DHF concentrations ranging from 0 to 195 μM. The Michaelis-Menten equation was fitted to the data using KaleidaGraph. The Michaelis-Menten constant for DHF was determined by averaging values from fitting three separate data sets and found to be 3.7 ± 2.0 μM (S.D.).</p
Bayesian model-based inference of transcription factor activity
No full text<b>Background:</b>
In many approaches to the inference and modeling of regulatory interactions using microarray data, the expression of the gene coding for the transcription factor is considered to be an accurate surrogate for the true activity of the protein it produces. There are many instances where this is inaccurate due to post-translational modifications of the transcription factor protein. Inference of the activity of the transcription factor from the expression of its targets has predominantly involved linear models that do not reflect the nonlinear nature of transcription. We extend a recent approach to inferring the transcription factor activity based on nonlinear Michaelis-Menten kinetics of transcription from maximum likelihood to fully Bayesian inference and give an example of how the model can be further developed.<p></p>
<b>Results:</b>
We present results on synthetic and real microarray data. Additionally, we illustrate how gene and replicate specific delays can be incorporated into the model.<p></p>
<b>Conclusion:</b>
We demonstrate that full Bayesian inference is appropriate in this application and has several benefits over the maximum likelihood approach, especially when the volume of data is limited. We also show the benefits of using a non-linear model over a linear model, particularly in the case of repression.<p></p>
Kinetic parameters based on Michaelis-Menten equation.
No full text<p>Kinetic parameters based on Michaelis-Menten equation.</p
Michaelis-Menten values for pNP-based substrates.
No full text<p>Michaelis-Menten values for pNP-based substrates.</p
Michaelis-Menten graph of PPT1 and PPT1 C6S.
No full text<p>The Km and Vmax of PPT1 and PPT1 C6S were calculated using the Michaelis-Menten equation following plotting the results of enzymatic activity obtained with different concentrations of the substrate (N = 9) A) Michaelis-Menten graph derived from the cell lysates. B) Michaelis-Menten graph derived from the cell media.</p
Michaelis-Menten kinetis for native and UVB-exposed ALDH3A1.
No full text<p>Michaelis-Menten kinetic constants for UVB-exposed ALDH3A1. Data were fit to the Michaelis-Menten saturation model using the SigmaPlot® enzyme kinetics software package. Values are mean ± SE (n = 3).</p>†<p>: <i>p</i><0.05, when compared with exposure time = 0 min.</p
Kinetic parameters determined by the Michaelis- Menten model.
No full text<p>Kinetic parameters determined by the Michaelis- Menten model.</p
Controle químico de Phoma sorghina em sementes de arroz (Oryza sativa L.).
No full textO desenvolvimento de epidemias de P. sorghina em arroz é mais frequente com período de chuva prolongados quando a associação do patógeno com a semente assume grande importância. O presente experimento teve como objetivo a avaliação do efeito do tratamento químico de sementes de arroz no controle do agente causal da queima das glumelas, assim como a possibilidade do desenvolvimento de uma escala de avaliação na diferenciação das sementes desta cultura portadora do patógeno. Entre os fungicidas utilizados o Benomyl mostrou-se mais eficiente que o Iprodione e o Captan no controle de P. sorghina. Não foram observadas diferenças estatísticas entre os métodos de avaliação (severidade e incidência) dos fungicidas quanto a eficiência no controle do patógeno. Como o controle obtido não foi absoluto, este resultado indica a necessidade tanto do estudo de novos produtos e/ou dosagens no controle deste patógeno como do efeito do inóculo remanescente nas sementes sob a germinação, o vigor das plântulas e o desenvolvimento de epidemias em condições de campo. A escala de avaliação empregada não colaborou para a diferenciação das sementes de arroz associadas com o patógeno
Emergence of the Michaelis-Menten approximation.
No full textA: Fast complex formation and decay (blue trajectories) result in the Michaelis-Menten approximation, slow formation and decay (orange trajectories) in a significant discrepancy to the data. B: The path in parameter space leading to the Michaelis-Menten approximation runs parallel to the contour lines of the log-likelihood function. C: The log-likelihood defines a significance threshold, which is not exceeded in the limit of fast formation/decay rates. Slower rates quickly lead to significant deviations from the data.</p
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