scholarly journals Analyzing Enzyme Kinetic Data Using the Powerful Statistical Capabilities of R

2018 ◽  
Author(s):  
Carly Huitema ◽  
Geoff Horsman
Keyword(s):  
Statistical Analysis ◽  
Enzyme Kinetics ◽  
Enzyme Catalysis ◽  
Kinetic Data ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Command Line ◽  
Enzyme Kinetic ◽  
Non Linear ◽  
Statistical Program

AbstractWe describe a powerful tool for enzymologists to use for typical non-linear fitting of common equations in enzyme kinetics using the statistical program R. Enzyme kinetics is a powerful tool for understanding enzyme catalysis, regulation, and inhibition but tools to perform the analysis have limitations. Software to perform the necessary nonlinear analysis may be proprietary, expensive or difficult to use, especially for a beginner. The statistical program R is ideally suited to analyzing enzyme kinetic data; it is free in two respects: there is no cost and there is freedom to distribute and modify. It is also robust, powerful and widely used in other fields of biology. In this paper we introduce the program R to enzymologists who want to analyze their data but are unfamiliar with R or similar command line statistical analysis programs. Data are inputted and examples of different non-linear models are fitted. Results are extracted and plots are generated to assist judging the goodness of fit. The instructions will allow users to create their own modifications to adapt the protocol to their own experiments. Because of the use of scripts, a method can be modified and used to analyze different datasets in less than one hour.

Generalized Linear Models for Enzyme-Kinetic Data

Biometrics ◽  
1991 ◽  
Vol 47 (4) ◽  
pp. 1605 ◽  
Author(s):  
J. A. Nelder ◽  
D. Ruppert ◽  
N. Cressie ◽  
R. J. Carroll
Keyword(s):  
Generalized Linear Models ◽  
Kinetic Data ◽  
Linear Models ◽  
Enzyme Kinetic

scholarly journals Comparison of Non Linear Models and Artificial Neural Network to Describe the Liveweight from Birth to Maturity in Female Jersey Cattle

2021 ◽  
pp. 114-119
Author(s):  
D. O. Omoniwa ◽  
J. E. T. Akinsola ◽  
R. O. Okeke ◽  
J. M. Madu ◽  
D. S. Bunjah Umar
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Inflection Point ◽  
Linear Models ◽  
Network Models ◽  
Linear Functions ◽  
Growth Data ◽  
Neural Network Models ◽  
Jersey Cattle ◽  
Non Linear

Evaluation of growth data is an important strategy to manage gross feed requirement in female Jersey cattle in the New Derived Guinea Savannah Zone of Nigeria. Two non-linear functions (Gompertz and Logistic) and Neural network models were used to fit liveweight (LW)-age data using the non linear procedure of JMP statistical software. Data used for this study were collected from 150 Jersey female cattle in Shonga Dairy Farm, Kwara, State from 2010-2018. The Neural network function showedthe best goodness of fit. Both the Gompertz and Logistic functions overestimated LW at birth, 3, 36, 48, 60 and 72months respectively. NN function overestimated the LW at 0, 3, 24, 36 and 72 months. The Gompertzfunction had the best estimation of asymptotic weight (649.51 kg) with average absolute growth rate (0.061 kg/day).The inflection point was 15.95, 9.55 and 34.5 months in Logistic, Gompertz and neural network models, respectively. A strong and positive correlation was observed between asymptote and inflection point in Gompertz functions. The metrics of goodness of fit criteria (R2 and RMSE), showed that NN with multilayer perceptron was superior to the other models but Gompertz model, was best in its ability to approximate complex functions of growth curve parametersin female Jersey cattle.

scholarly journals Evaluation of non-linear models to describe development and fertility of codling moth at constant temperatures

2017 ◽  
Vol 20 (1) ◽  
pp. 3 ◽  
Author(s):  
H. Ranjbar Aghdam ◽  
Y. Fathipour ◽  
D. C. Kontodimas
Keyword(s):  
Goodness Of Fit ◽  
Linear Models ◽  
Information Criterion ◽  
Developmental Rate ◽  
Codling Moth ◽  
Coefficient Of Determination ◽  
Temperature Threshold ◽  
Optimum Temperature ◽  
Non Linear ◽  
Lower Temperature

Developmental rate of immature stages and age-specific fertility of females of codling moth at constant temperatures was modeled using non-linear models. The equations of Enkegaard, Analytis, and Bieri 1 and 2 were evaluated based on the value of adjusted R2 (R2adj) and Akaike information criterion (AIC) besides coefficient of determination (R2) and residual sum of squares (RSS). All models have goodness of fit to data especially for development [R2, R2adj, RSS and AIC ranged 0.9673-0.9917, 0.8601-0.9861, 0.08-6.7x10-4 and (-75.29) – (-46.26) respectively]. Optimum temperature (Topt) and upper threshold (Tmax) were calculated accurately (Topt and Tmax ranged 29.9-31.2oC and 35.9-36.7oC) by all models. Lower temperature threshold (Tmin) was calculated accurately by Bieri-1 model (9,9-10,8oC) whereas Analytis model (7,0-8,4oC) underestimated it. As far as fertility is concerned the respective values were better fitted near the optimum temperature (in 30oC) [R2 ,R2adj, RSS and AIC ranged 0,6966-0,7744, 0,5756-0,6455, 2,44-3,33 x10-4 and (-9,15)-7,15 respectively].

scholarly journals Comparison of the parameters of the lactation curve between normal and difficult calvings in Iranian Holstein cows

2019 ◽  
Vol 17 (1) ◽  
pp. e0401
Author(s):  
Navid Ghavi Hossein-Zadeh
Keyword(s):  
Dairy Cows ◽  
Milk Yield ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Information Criterion ◽  
Holstein Cows ◽  
Coefficient Of Determination ◽  
Lactation Curve ◽  
Non Linear ◽  
Best Fit

To evaluate effect of dystocia on the lactation curve characteristics for milk yield and composition in Holstein cows, six non-linear models (Brody, Wood, Sikka, Nelder, Dijkstra and Rook) were fitted on 5,917,677 test day records for milk yield (MY), fat (FP) and protein (PP) percentages, fat to protein ratio (FPR) and somatic cell score (SCS) of 643,625 first lactation Holstein cows with normal calving or dystocia from 3146 herds which were collected by the Animal Breeding Center of Iran. The models were tested for goodness of fit using adjusted coefficient of determination, root means square error, Akaike’s information criterion and Bayesian information criterion. Rook model provided the best fit of the lactation curve for MY and SCS in normal and difficult calvers and dairy cows with dystocia for FP. Dijkstra model provided the best fit of the lactation curve for PP and FPR in normal and difficult calvers and dairy cows with normal calving for FP. Dairy cows with dystocia had generally lower 100-d, 200-d and 305-d cumulative milk yield compared with normal calvers. Time to the peak milk yield was observed later for difficult calvers (89 days in milk vs. 79 days in milk) with lower peak milk yield (31.45 kg vs. 31.88 kg) compared with normal calvers. Evaluation of the different non-linear models indicated that dystocia had important negative effects on milk yield and lactation curve characteristics in dairy cows and it should be reduced as much as possible in dairy herds.

scholarly journals Choice of non-linear models to determine the growth curve of meat-type quail

2021 ◽  
Vol 51 (2) ◽  
Author(s):  
Marta Jeidjane Borges Ribeiro ◽  
Fabyano Fonseca Silva ◽  
Maíse dos Santos Macário ◽  
José Aparecido Santos de Jesus ◽  
Claudson Oliveira Brito ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Linear Models ◽  
Growth Curves ◽  
Information Criterion ◽  
Metabolizable Energy ◽  
Coefficient Of Determination ◽  
Parameter Estimates ◽  
Coturnix Coturnix ◽  
Non Linear ◽  
Meat Type

ABSTRACT: The objective of this study was to compare non-linear models fitted to the growth curves of quail to determine which model best describes their growth and check the similarity between models by analyzing parameter estimates.Weight and age data of meat-type European quail (Coturnix coturnix coturnix) of three lines were used, from an experiment in a 2 × 4 factorial arrangement in a completely randomized design, consisting of two metabolizable energy levels, four crude protein levels and six replicates. The non-linear Brody, Von Bertalanffy, Richards, Logistic and Gompertz models were used. To choose the best model, the Adjusted Coefficient of Determination, Convergence Rate, Residual Mean Square, Durbin-Watson Test, Akaike Information Criterion and Bayesian Information Criterion were applied as goodness-of-fit indicators. Cluster analysis was performed to check the similarity between models based on the mean parameter estimates. Among the studied models, Richards’ was the most suitable to describe the growth curves. The Logistic and Richards models were considered similar in the analysis with no distinction of lines as well as in the analyses of Lines 1, 2 and 3.

A computer program for analyzing enzyme kinetic data using graphical display and statistical analysis

1984 ◽  
Vol 17 (3) ◽  
pp. 289-301 ◽  
Author(s):  
Serge D. Schremmer ◽  
Mark R. Waser ◽  
Michael C. Kohn ◽  
David Garfinkel
Keyword(s):  
Statistical Analysis ◽  
Computer Program ◽  
Kinetic Data ◽  
Graphical Display ◽  
Enzyme Kinetic

scholarly journals Modelling growth in seven chicken genotypes

2021 ◽  
Vol 32 (1) ◽  
pp. 28-38
Author(s):  
S. O. Peters ◽  
C. O. N. Ikeobi ◽  
M. O. Ozoje ◽  
O. A. Adebambo
Keyword(s):  
Body Weight ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Growth Models ◽  
The Body ◽  
Richards Model ◽  
Non Linear ◽  
Chest Girth ◽  
Mature Size ◽  
Early Phases

Three non-linear growth models were used to fit weight-age data for seven chicken genotypes: Comparisons were made among these models for goodness of fit, biological interpretability and computational case. Monomolecular and Richards Models overestimated body weight at the early phases of growth. All the three models underestimated the asymptotic mature weight but Gumpertz function gave a better estimate than the other two. Maturing rates were also variable and Richards Model gave the best estimate of K. Using these three non-linear models to describe growth rate of chest girth of the seven chicken genotypes yields a different result from that of the body weight. The point of inflection ranged from - 3 56 for FINA (F/Na) genotype to 28.26 for frizzled (Frx Fr) genotype. Genetic variations in rates of gain, maluring rute und mature size were observed. 

scholarly journals On Catalysis by Biological Macromolecular Enzymes

2020 ◽  
Author(s):  
Jianshu Dong
Keyword(s):  
Enzyme Kinetics ◽  
Enzyme Catalysis ◽  
Kinetic Data ◽  
Time Course ◽  
Molecular Level ◽  
Catalytic Efficiency ◽  
Molecular Behavior ◽  
Catalytic Rate

Classical enzyme kinetics are interpreted from a new angle here, and biological macromolecular enzyme catalysis is viewed and explored at the molecular level. The time course of sequential catalytic events is analyzed, the relationships between catalytic efficiency, catalytic rate/velocity and the amount of time consumed are established. This writing tries to connect the microscopic molecular behavior of enzymes to kinetic data obtained in experiment, and the equations proposed here can be testified and examined by future experiments.

scholarly journals Fitting of enzyme kinetic data without prior knowledge of weights

1981 ◽  
Vol 193 (3) ◽  
pp. 1005-1008 ◽  
Author(s):  
A Cornish-Bowden ◽  
L Endrenyi
Keyword(s):  
Steady State ◽  
Large Deviations ◽  
Computer Program ◽  
Enzyme Kinetics ◽  
Prior Knowledge ◽  
Kinetic Data ◽  
Enzyme Kinetic ◽  
Error Structure ◽  
Wide Range

A method is described for fitting equations to enzyme kinetic data that requires minimal assumptions about the error structure of the data. The dependence of the variances on the velocities is not assumed, but is deduced from internal evidence in the data. The effect of very bad observations (‘outliers’) is mitigated by decreasing the weight of observations that give large deviations from the fitted equation. The method works well in a wide range of circumstances when applied to the Michaelis-Menten equation, but it is not limited to this equation. It can be applied to most of the equations in common use for the analysis of steady-state enzyme kinetics. It has been implemented as a computer program that can fit a wide variety of equations with two, three or four parameters and two or three variables.

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