The gate and drain control coefficients effect on CNTFET performance under different temperature

2015 ◽  
Author(s):  
Safayat-Al Imam ◽  
Nasheen Kalam ◽  
Sharmin Abdhullah
Keyword(s):  
Control Coefficients

scholarly journals Fluxes and Metabolic Pools as Model Traits for Quantitative Genetics. I. The L-Shaped Distribution of Gene Effects

Genetics ◽  
1999 ◽  
Vol 153 (4) ◽  
pp. 2001-2012 ◽  
Author(s):  
Bruno Bost ◽  
Christine Dillmann ◽  
Dominique de Vienne
Keyword(s):  
Enzyme Activity ◽  
Metabolic Pathways ◽  
Quantitative Traits ◽  
Intrinsic Property ◽  
Gene Effects ◽  
Analytical Approximations ◽  
Mean And Variance ◽  
The Individual ◽  
Metabolic Control Theory ◽  
Control Coefficients

Abstract The fluxes through metabolic pathways can be considered as model quantitative traits, whose QTL are the polymorphic loci controlling the activity or quantity of the enzymes. Relying on metabolic control theory, we investigated the relationships between the variations of enzyme activity along metabolic pathways and the variations of the flux in a population with biallelic QTL. Two kinds of variations were taken into account, the variation of the average enzyme activity across the loci, and the variation of the activity of each enzyme of the pathway among the individuals of the population. We proposed analytical approximations for the flux mean and variance in the population as well as for the additive and dominance variances of the individual QTL. Monte Carlo simulations based on these approximations showed that an L-shaped distribution of the contributions of individual QTL to the flux variance (R2) is consistently expected in an F2 progeny. This result could partly account for the classically observed L-shaped distribution of QTL effects for quantitative traits. The high correlation we found between R2 value and flux control coefficients variance suggests that such a distribution is an intrinsic property of metabolic pathways due to the summation property of control coefficients.

Control analysis of photosynthetic sucrose synthesis: assignment of elasticity coefficients and flux-control coefficients to the cytosolic fructose 1,6-bisphosphatase and sucrose phosphate synthase

1989 ◽  
Vol 323 (1216) ◽  
pp. 327-338 ◽  
Keyword(s):  
Sucrose Phosphate Synthase ◽  
Control Analysis ◽  
Sucrose Synthesis ◽  
Flux Control ◽  
Fructose 1,6 Bisphosphatase ◽  
Flux Control Coefficients ◽  
Sucrose Phosphate ◽  
Control Coefficients ◽  
Phosphate Synthase

The use of elasticity coefficients and flux-control coefficients in a quantitative treatment of control is discussed, with photosynthetic sucrose synthesis as an example. Experimental values for elasticities for the cytosolic fructose 1,6-bisphosphatase and sucrose phosphate synthase are derived from their in vitro properties, and from an analysis of the in vivo relation between fluxes and metabolite levels. An empirical factor α , describing the response of the fructose 2,6-bisphosphate regulator cycle to fructose 6-phosphate is described, and an expression is derived relating α to the elasticities of the enzymes involved in this regulator cycle. The in vivo values for elasticities and α are then used in a modified form of the connectivity theorem to estimate the flux control coefficients of the cytosolic fructose 1,6-bisphosphatase and sucrose phosphate synthase during rapid photosynthetic sucrose synthesis.

scholarly journals Flux control coefficients determined by inhibitor titration: the design and analysis of experiments to minimize errors

1993 ◽  
Vol 296 (2) ◽  
pp. 423-433 ◽  
Author(s):  
J R Small
Keyword(s):  
Random Errors ◽  
Flux Control ◽  
Graph Method ◽  
Function Fitting ◽  
Fitting Method ◽  
Simple Extrapolation ◽  
Experimental Errors ◽  
Flux Control Coefficients ◽  
Fitted Function ◽  
Control Coefficients

This paper is a study into the effects of experimental error on the estimated values of flux control coefficients obtained using specific inhibitors. Two possible techniques for analysing the experimental data are compared: a simple extrapolation method (the so-called graph method) and a non-linear function fitting method. For these techniques, the sources of systematic errors are identified and the effects of systematic and random errors are quantified, using both statistical analysis and numerical computation. It is shown that the graph method is very sensitive to random errors and, under all conditions studied, that the fitting method, even under conditions where the assumptions underlying the fitted function do not hold, outperformed the graph method. Possible ways of designing experiments to minimize the effects of experimental errors are analysed and discussed.

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