Research ethics symposium 2007
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Research Ethics Symposium 2007. H. F. Gilbert, Ph.D. Assoc Dean for Academics and Postdoctoral Research Graduate School of Biomedical Sciences. Clinical Ethics Symposium (May 21-24). Resources List. Ethics Training Site http://www.bcm.edu/gs/ethics/index.html. On Being a Scientist

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Research Ethics Symposium 2007

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Research ethics symposium 2007

Research Ethics Symposium 2007

  • H. F. Gilbert, Ph.D.

    • Assoc Dean for Academics and Postdoctoral Research

    • Graduate School of Biomedical Sciences


Clinical ethics symposium may 21 24

Clinical Ethics Symposium (May 21-24)


Resources list

Resources List

Ethics Training Site

http://www.bcm.edu/gs/ethics/index.html

On Being a Scientist

http://www.nap.edu/readingroom/books/obas/content.html

Statistics on Line

http://www.statsoftinc.com/textbook/stathome.html


The scientific method scientific integrity

The Scientific Method & Scientific Integrity

Truth

Observations

Hypothesis

or Model

falsifiable

Predictions

controls

Experiments


Error in experiments

Error in Experiments

  • Random error - error that cannot be controlled

  • pipetting error (2-10%)

  • temperature variations

  • biological variability

  • lot to lot differences in reagents

  • measurement errors (instrumental errors)


Error in experiments1

Error in Experiments

  • Systematic error -errors that occur consistently within the same experiment(s)

  • instrument calibration errors

  • reagent concentrations based on weight or incorrect measurement in stock solutions

  • systematic losses of material

  • biological variability - variation between strains


Error in experiments2

Error in Experiments

  • Blunders -catastrophic errors that occur occasionally

  • swapping sample identity

  • adding the wrong reagent

  • failing to add something

  • biological variability - variation between strains


What would you do

What would you do?

Sharon, a graduate student, was putting her thesis together, and constructing a table of doubling times for a series of experiments to determine the effects of glucose vs galactose as a carbon source on a mutant strain of yeast. She was looking for mutations in the galactose transporter that would decrease the import of galactose and make it harder for the cells to grow on galactose but not glucose. In a growth screen, she found one cell line that grew more slowly on galactose than glucose. She has been working the past year on identifying the site of the mutation and has recently found a point mutation in the galactose transporter in this strain.


What would you do1

What would you do?

When writing up the paper for publication her PI wanted to include the actual doubling time of the strain on glucose and galactose. When she looked back in her notebook, she found that she had measured the growth rate for this strain only once. For wt yeast, growth rates on glucose and galactose are usually similar. Since the growth on glucose and galactose medium was so different, Sharon was sure that her conclusion that the mutation caused the slow growth was right. However, she and her PI wanted to be sure of her result so she decided to repeat the measurement.


Research ethics symposium 2007

She repeated the measurements the next day and

obtained the following results. Now what?

Original Data

All Data

2nd expt


Research ethics symposium 2007

What if the experiments had come out this way?

Would she have been finished?

How many times should you repeat an experiment to be certain of the results?

Original Data

All Data


The t test

The t-test

Lets you statistically test to see iftwo means differ significantly

Raw Data

Mean1 - Mean2

mean

stdev

1 1

12 (n1-1)+22(n2-1)

( n1 n2)

n

t =

+

(n1+n2 -2)


How many experiments to you have to do to make up for your mistake can you ever

How many experiments to you have to do to make up for your mistake? Can you ever?


Outliers

(Suspect - Nearest Point)

Q > =

(High Point - Low Point)

Outliers

Throw out when

Q

0.66 0.86 0.66 0.86 0.53 0.81

Throw out when greater than 2 away from average

 2

2.6-5.0 1.3-5.7 2.9-4.9 1.1-5.1 2.8- 5.2 1.1-4.7


Graphical data and correlations

Graphical Data and Correlations

Outliers can affect data drastically. Failing to exclude a true outlier can bias your data. Excluding a false outlier can also bias your data.

IF YOUR CONCLUSION CHANGES WHEN YOU INCLUDE OR EXCLUDE A POINT - YOU NEED MORE POINTS.


Confidence interval testing of data points

Confidence Interval Testing of Data Points

Linear Fit of DATA1_B

45

45

Linear Fit of DATA1_B

Upper 95% Confidence Limit

Upper 95% Confidence Limit

40

Lower 95% Confidence Limit

40

Lower 95% Confidence Limit

35

35

30

30

25

25

Absorbance

Absorbance

20

20

15

15

10

10

5

5

0

0

0

1

2

3

4

0

1

2

3

4

Concentration

Concentration

Y = A + B * X

ParameterValueError

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

A8.428574.4964

B4.507141.74145

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

RSDNP

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

0.699286.868379 0.03604

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

Y = A + B * X

ParameterValueErrort-ValueProb>|t|

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

A5.416671.39343.887390.0081

B6.916670.5941511.64136<0.0001


Outliers1

Outliers

If you are sure of a blunder record it in your notebook

and eliminate it from consideration. Being sure means

that you are positive that you made a mistake and can document it, not simply that the results are not what you expect

Rule of thumb is that you should not remove more than one

outlying point from a given data set

Each field/PI may have different standards for how data are

selected for inclusion. However, if data are excluded it should

be stated in the paper, including the criteria that were used.


What would you do2

What would you do?

Tom is investigating how vitamin E protects cells against

oxidative stress. He is examining how a human HeLa cell line

responds to hydrogen peroxide treatment.

Using an antibody to superoxide dismutase he used a western

blot of extracts from HeLa cells to observed the amount of

SOD present after peroxide treatment in conjunction with

vitamin E, a known antioxidant.


How should tom present his data

How should Tom present his data?

H2O2

Vit E

- + + -

+ - + -

- + + -

+ - + -

- + + -

+ - + -

Original Data

Contrast enhanced

Cropped


What would you do3

What would you do?

Mai is trying to determine how well a protease activated at the G2-M transition of the cell cycle clips and inactivates a downstream cell cycle repressor.

She treats the purified repressor with a small amount ofher purified protease, waits 10 min and detects the repressor fragments with a polyclonal antibody.

She performs the Western blot and probes with her antibodyand then a second antibody to visualize the protein by ECL.


Which exposure time is the best representation

Which exposure time is the best representation

5s 10s 30s 60s 3min 20 min 1hr


What would you do4

What would you do?

Howard is trying to knock out the gene for thioredoxin in a mouse model. Thioredoxin is one of two cofactors for ribonucleotide reductase, which is responsible for making deoxyribonucleotides for DNA synthesis. Thioredoxin is also thought to have other cellular functions.

After verifying the genotype in his first generation mouse, Howard breeds a male and female heterozygote and examines the genotype of the offspring.


Howard obtained the following results what would you conclude how sure are you

Howard obtained the following results.What would you conclude?How sure are you?


How to be sure

How to be sure?


Can test statistical significance or use rules of thumb established by experience

Can test statistical significance or use “rules of thumb” established by experience


Research ethics symposium 2007

Natasha wanted to make a mutant of a protein kinase that was already known to become fully active when auto-phosphorylated on a Ser102. If this amino acid is mutated to Glu, it mimics the phosphorylated serine residue and keeps the kinase constantly active.

She wanted to express the constitutively active kinase to examine the inactivation mechanisms in mammalian cells.

What should she do to confirm that the kinase is a mutant?


Glyargcystrpalaserlysala wt acgtaaccgtagccgtactctact mut acgtaaccgtagccgtcctctact

GlyArgCysTrpAlaSerLysAlaWT ACGTAACCGTAGCCGTACTCTACT ::::::::::::::::*:::::::Mut ACGTAACCGTAGCCGTCCTCTACT

How sure are you that she has the right mutation?

What would you do to be sure that you had the mutation.

How can you be sure that you have no other mutations in the protein?


Summary

Summary

Selecting which data to believe and use is an integralpart of an experiment

There are ways (statistical and traditional) to maximize theprobability that you will draw the correct conclusions from your experiments.

In your notebooks and any papers/grants derived from them, be sure to state how many times a given experiment was done and give some estimate of how accurate and precise your data are.


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