Dec 122017

Used to fit experimental data into a variety of model equations. | |||
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File Name | File Size | Zip Size | Zip Type |

——–.- | 1 | 1 | stored |

——–.– | 1 | 1 | stored |

——–.— | 1 | 1 | stored |

ANTAGON.RAW | 243 | 91 | deflated |

COMPET7%.RAW | 833 | 254 | deflated |

EZ-FIT.DOC | 12166 | 4694 | deflated |

EZ-FIT.EXE | 193556 | 89605 | deflated |

MMENTEN.RAW | 162 | 68 | deflated |

# Download File EZ-FIT.ZIP Here

## Contents of the EZ-FIT.DOC file

DOCUMENTATION FOR EZ-FIT V1.1

----------------------------------------------------------------

*** EZ-FIT is an interactive computer software package that

fits experimental data to a variety of model equations. EZ-FIT

runs on an IBM-PC compatible microcomputer with a CGA graphics card

or equivalent, a requirement for the EZ-FIT plot routine. EZ-FIT

was developed specifically for the regression analysis of enzyme or

receptor kinetic data. It provides reliable parameter estimates and

determines the goodness of fit of the data to the selected model.

EZ-FIT has the facility for data entry, editing, filing, and plotting.

Up to 90 data pairs can be analyzed at once.

*** When entering data the default entries are always displayed in

square brackets [ ]. To choose the default value simply enter a

carriage return

A menu of 14 model equations is incorporated in EZ-FIT.

1. Michaelis-Menten,

2. Iso-enzyme or two receptors,

3. Hill,

4. Competitive inhibition,

5. Noncompetitive inhibition,

6. Mixed competitive inhibition,

7. Uncompetitive inhibition,

8. Ping-Pong bi-bi,

9. Ordered bi-bi,

10. Random bi-bi,

11. Modified integrated Michaelis-Menten,

12. Agonist,

13. Antagonist (4 parameter logistic equation).

14. Substrate inhibition

*** Data from enzyme studies that are in the form of cpm,

dpm, or optical densities can be converted by EZ-FIT into moles or

Molar of product/min/mg protein. Similarly, receptor binding data can

be converted to moles bound/mg protein. EZ-FIT automatically

computes the decay of the nuclide given the number of days since its

manufacture.

As an example, we'll enter data in the form of 10,000 dpm bound

from a receptor binding study. EZ-FIT then requests the following:

ENTER THE ISOTOPE TYPE: (1)3H, (2)14C, (3)32P, (4) OTHER:[?] 3

We've entered 3 for 32P.

ENTER # DAYS ELAPSED SINCE MANUFACTURE OF ISOTOPE:[?] 14.3

We've entered 14.3.

ENTER INITIAL SPECIFIC ACTIVITY OF ISOTOPE (Ci/mmole):[?] 20

We've entered 20.

ENTER ASSAY VOLUME (ml):[?] 1000

Enter 1000 if you want your data converted to moles instead of MOLAR.

The conversion to 1000 ml negates the Molar conversion, but does not

alter the data, so that the effect is the same as converting it

to moles.

ENTER PROTEIN CONCENTRATION IN ASSAY (mg):[?] 0.050

We've entered 50 ug.

ENTER TIME OF INCUBATION (min):[?] 1

Enter 1 minute if you want your data converted to moles/min/mg. This

is the same as moles/mg for binding studies; the conversion to one

minute does not change the data and therefore has the same effect as

converting it to moles/mg.

ENTER MOLAR TYPE: (1)pM-, (2)nM-, (3)uM- /min/mg:[?] 1

We've entered 1 for pmoles.

That's all there is to it. EZ-FIT then converts the 10,000

dpm to 9 pmol/min/mg which is equivalent to 9 pmol/mg.

*** When a model function is chosen for fitting a set of data, the

user has the option of weighting or not weighting the fit. Weighting

of data may be necessary when the variance of Y(experimental) is

not constant. In this case, EZ-FIT generates the weights from an

empirical variance function or if the standard errors of Y(expt) are

included in the data set it uses them for weighting. The default

setting for weighting, when chosen, is proportional weighting.

*** When using the models 4 to 11, multiple sets of data (up to 6 sets)

can be analyzed simultaneously, thereby increasing the precision of

the Ki or Ka, Km, and Vmax values. An example of how to enter 2 sets

of data for competitive inhibition is shown below.

The first set of data contains no inhibitor, but subsequent

data sets contain increasing concentrations of inhibitor. The first

row is the velocity or specific binding, the second row is the

substrate or ligand concentration, and the third row is the inhibitor

concentration. Following each data set, enter four nines 9999 and

after the last 9999 of the last data set enter four ones 1111. This

allows EZ-FIT to keep track of the separate sets of data and to

produce separate curves for each data set. Once the data is entered,

it can be filed.

Example:

FIRST DATA GROUP:

8.7 'velocity' \

8.0 'substrate' } Do not include the comments.

0.0 'inhibitor' /

9.45

10.0

0.0

11.3

16.6

0.0

11.6

25.0

0.0

13.3

50.0

0.0

9999

NEXT DATA GROUP:

5.28 'velocity' \

8.0 'substrate' } Second curve

8.0 'inhibitor' /

6.11

10.0

8.0

8.2

16.6

8.0

9.80

25.0

8.0

11.7

50.0

8.0

9999

NEXT DATA GROUP:

1111

* END DATA SET *

Note that when entering more than one independent variable (e.g.

substrate A and inhibitor I, substrates A and B, or time and substrate B) with

each of the four competition models, with the three bi bi models, or with

the integrated MM equation, respectively, the first substrate entered in

each data sub-group is always the variable substrate A or time. Inhibitor

and substrate B are the second variables entered and are held constant in the

data sub-group.

To list your raw data files, use the 'DIRECTORY OF DATA FILES'

option on the main menu. The file name MUST contain the suffix

'.RAW' for the files to be listed.

*** Model equations 1-3 and 12-14 use only a SINGLE set of data. They

can NOT accept multiple data sets. As an example, a set of data to

be fit by the Michaelis-Menten or one-site receptor saturation model is

shown below. Notice that there are only two entries per group in this

example. The first entry is the velocity or specific binding, and the

second entry is the substrate or ligand concentration.

Example:

FIRST DATA GROUP:

8.7 'velocity' \ one curve only

8.0 'substrate' /

9.4

10.0

11.3

16.6

11.6

25.0

13.3

50.0

9999

NEXT DATA GROUP:

1111

* END DATA SET *

*** When analyzing the progress of a reaction, either enzyme or

receptor, with time, the integrated Michaelis-Menten model is

used. This model computes the initial velocity (Vo) of the reaction;

the computed values of Km and Vmax may not be accurate and caution should

be used when interpreting them. The estimates of Km and Vmax from progress

curves are most accurate when multiple curves are evaluated at different

substrate concentrations.

Since many in vitro biochemical reactions do not obey the

standard integrated form of this equation due to enzyme instability

during incubation, EZ-FIT uses a modified form of this equation that

includes a decay constant (Kvmax). This constant represents the

inactivation of the enzyme or receptor with time, during in vitro reactions.

When using this model, the units of the initial substrate (So)

concentration MUST be identical to those of the product of the

reaction (e.g. So = nmol, Product = nmol). For initial parameter

estimates, it is suggested to enter a value for Km equal to So, a

value for Vmax equal to 100 times the estimated value of Product/min,

and a Kvmax equal to 0.0001.

*** EZ-FIT requires that initial estimates of the fitted parameters be

provided. The initial estimates of the parameters (e.g. Km, Vmax, Ki, etc.)

can usually be an order of magnitude or two away from the true values.

EZ-FIT improves these rough estimates by using the Nelder-Mead Simplex

routine; convergence to a feasible area of the least-squares is likely,

given an appropriate step size. Using these values, the Gauss/Newton-

/Marquardt/Levenberg/Nash routine improves these estimates to final

values. The parameter values are always constrainted to values greater

than zero. The regression routine may be aborted at any time by press a

capital X.

A report of the results is printed and includes the original

data, the best fit curve data, the residuals, the standard error of

regression, the final parameter values (e.g. Km, Ki, Vmax, etc.) and

their asymptotic standard errors, a paired Student t test at p<0.05

to determine the accuracy of the parameter values, a Runs statistic

test of the residuals to determine if a systematic nonrandom departure

of the data from the fitted curve exists, and identification of

individual data points which depart by greater than 20 percent from the

behavior of the fitted curve (outliers). Parameter values that are not

significantly different from zero, as determined by the Student's t

test, are identified by an arrow (<--) to the right of the value.

Outlying data are also identified by arrows. The result of the Runs

test of residuals is returned as either a PASS or FAIL. To aide in discriminating between rival models for the same data

set, a goodness of fit test is used. The Akaike's information criterion

(AIC) provides an estimate of the goodness of fit for the fitted curve.

When comparing models, the model with a AIC value that is at least 2

units smaller than the rival model is the best fit.

When standard errors of Yobs are included in the data set, the

Chi-Square statistic can also be used to aide in determining the best fit

model. This is done by chosing the weighting option that uses standard

errors instead of the empirical weighting function. When comparing

models, the model with a Chi-Square value closest to 1.0 is the best fit.

*** EZ-FIT produces graphs of the data and fitted curve. Five

types of plots can be displayed for models 1 to 10. These include

X-Y, Scatchard, Eadie-Hofstee, Lineweaver-Burk, and residual plots.

Models 11, 12 and 14 display only an X-Y and residual plot. Model 13

displays four plots, an X-Y plot, a modified Scatchard plot of

(%inhibition/[Inhibitor]) vs (%inhibition), a semi-log inhibition plot,

and a residual plot.

To obtain a copy of the graph on a local printer, press the

SHIFT and PRTSC keys simultaneously while the graph is being displayed

on the monitor. NOTE: The MS-DOS program GRAPHICS.COM must be run

before the graphics screen dump will work.

*** The scale of each graph can be expanded or reduced by 30% by

entering either a '+' or '-' at the scale request statement. To

expand or reduce the graph further, exit the graph using a

reenter the same graph again, and enter either a '+' or '-' again.

This has the effect of expanding or reducing the expanded- or

reduced-graph by an additional 30%. This process can be repeated to

provide the proper scale so that the plotted curve can intersect an

axes instead of being clipped. If, however, an '=' is entered instead

of a '+' or '-', the scale defaults back to its original size.

Entering a '*' causes the expanded or reduced graph to be maintained

at that scale. In addition, two '++' or '--' can be used to change

the size of graph even further.

*** If upon inspection of a graph, outlying data points are observed,

the corresponding data points can be removed from the analysis using

the EDIT option and the fitting process can be repeated. A new report

is printed, and a new graph is displayed with the new fitted curve. EZ-FIT may be rerun as many times as you like, with different

models or data sets, without exiting the program.

*** END OF DOCUMENTATION **

*************************************

* *

* EZ-FIT was developed by *

* *

* Frank W. Perrella, Ph.D. *

* E.I. DuPont de Nemours & Co. *

* Glenolden Laboratory *

* 500 South Ridgeway Ave. *

* Glenolden, PA 19036 *

* *

*************************************

December 12, 2017
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