Laboratory 2-Real time evolution: bacteria
Natural Selection in action—Evolution as we speak…
Before this lab
1. Read this lab chapter carefully; print a copy to bring to lab. You will have a quiz at the
beginning of lab based on the procedure for this lab including SAFE SCIENCE relevant
to this exercise, the procedure and the underlying concepts for the antimicrobial
experiment (see Key Terms), and the basics of the scientific method covered in
Exercises 2 & 3.
2. Read Freeman Chapter 1 Section 1.4 Doing Biology
3. Do the Freeman WEB Tutorials (also on your text CD):
a. Freeman 23.1.Natural Selection for Antibiotic Resistance
b. Freeman 1.2 Introduction to Experimental Design
Evaluation: 6%
1% In-class quiz (individual!) on the procedure and background on bacterial &
antimicrobials
2% In-class TEAM exercises (your group will hand in ONE answer from the team for
each exercise) All group members must actively contribute or they risk getting a lower
mark than the group.
¾ Exercise 2 Sampling error (1%)
¾ Exercise 3 Deconstruct the science (1%)
3% Individual assignment due in Lab 4 analyzing the data collected in Lab 3
Objectives by the end of this exercise you should be able to:
1. List and describe safe lab practices to work with bacteria.
2. Discuss the relative effectiveness of antiseptics, disinfectants, antibiotics to control
bacterial growth.
3. Discuss how natural selection affects bacterial sensitivity and resistance to antimicrobial
agents.
4. Describe the component steps of the scientific method (observation, hypothesis,
prediction, experimentation, and conclusion) and identify these steps from a description
of an experiment.
5. Discuss sampling error and the difference between sample size and sampling error.
6. Be able to distinguish a controlled from a comparative experiment.
Key Terms & Concepts
1) Antimicrobials and E. coli bacteria sensitivity
1. resistance versus sensitivity
2. antimicrobial—antibiotic, disinfectant, antiseptic, spice
3. bactericidal versus bacteriostatic
4. bacteria-- prokaryote
5. clonal reproduction
6. pathogen
7. sterilization
8. zone of inhibition
BIO152H5F 2006 University of Toronto at Mississauga
Real time evolution 2 - 2
2) The scientific process and experimental design
1. data
2. evidence
3. experimental method (procedure also called protocol)
4. hypothesis (and null hypothesis) and prediction
5. hypothesis testing by controlled and comparative experiments
6. observation
7. replication
8. sample size and the possibility of sampling error
9. variables: dependent, independent, standardized
Lab Timeline: (3 hours)
2:10 – 2:25
(15 minutes) Introduction, Lab assignment collected
2:25 -- 2:45 (20 minutes) Quiz
2:45 – 3:45
(60 minutes) Exercise 1 Setup plates for bacteria experiment
3:45 – 4:20
(35 minutes) Exercise 2 Sample size and sampling error
4:20 – 4:55
(35 minutes) Exercise 3 Deconstruct the science
4:55 – 5:00
cleaning up!
( 5 minutes) final clean up—marks deducted from any group leaving without
Exercise 1 Genetic variation and selection: E. coli sensitivity to antimicrobials
(antibiotics, disinfectants, antiseptics, food spices)
Background
Bacteria are found almost everywhere. While most species are beneficial, some are harmful or
even pathogenic (cause disease). Chemical and physical agents may be used to control
bacterial growth, yet used inappropriately can by natural selection lead to bacterial species
which are resistant to our attempts to control them. In this exercise you will learn more about the
genetic diversity of bacteria and about different methods which may be more or less effective at
controlling bacterial growth.
The combined effects of fast growth rates, high concentrations of cells, genetic processes of
mutation and selection, and the ability to exchange genes, account for the extraordinary rates of
adaptation and evolution that can be observed in the bacteria. For these reasons bacterial
adaptation (resistance) to the antibiotic environment seems to take place very rapidly in
evolutionary time: bacteria evolve fast!
For example, the bacterium Escherichia coli (E. coli) used in this lab has about 5000 genes and
has a mutation rate of about 1 mutation in every 1 x106 (1 million) copies. The generation time
(time from parent to daughter cells) can be every 20 minutes under optimum conditions.
Therefore, one could expect to find 1 mutant gene in every 200 bacteria. A typical spoonful of
soil contains over a billion bacteria; within this population over 5 million of these bacteria would
contain a mutation.
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Real time evolution 2- 3
Overview: Your team will be half the students at the bench (3-4 students). You will set up the
various bacterial plates in this lab, record the results next lab.
(This week) Materials per team ( half bench of about 4 students)
4 agar plates one for each of the following
plate 1 antibiotics
plate 2 disinfectants
plate 3 antiseptics
plate 4 food spices
4 sterile swabs
11 sterile disks—3 (controls); 4 disinfectants; 4 antiseptics
1 marking pen
4 Parafilm strips
[2 small clear metric rulers—lab 3]
Bacteria Escherichia coli (strain #10)
For Plate 1 Bacteria + Antibiotics-commercially prepared disks
Control (plain sterile disk)
Neomycin (30 micrograms)
Penicillin (10 units)
Streptomycin (10 micrograms)
Ampicillin (10 micrograms)
Plate 2 Bacteria + Disinfectants
Control (sterile water)
Ammonia
Lysol
70% Ethanol
10% Bleach
Plate 3 Antiseptics
Control (sterile water)
Hydrogen Peroxide
Listerine mouthwash
Antibacterial soap
Regular liquid dish soap
Plate 4 Bacteria + food spices (select from a variety provided)
Control
______
______
______
______
This plate (# 4) will give you a chance to design an experiment to test some hypothesis about
the antimicrobial role of spices in food. Your team will need to select four spices. How are you
going to decide which spices to test? Before lab read the accompanying article “Darwinian
gastronomy: why we use spices” and be prepared to explain your choice of spices.
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Real time evolution 2 - 4
Procedure
1. Wash your hands with soap and water.
2. Wash bench surface with 10% bleach.
3. Label around the side or near the edge of the bottom of the four agar plates (NOT the
top):
a. On EACH plate: Room# and the name of 1 student in your group and some
symbol to recognize your plate next week;
b. Number the plates: 1,2,3,4 (1=antibiotics; 2=disinfectants; 3=antiseptics;
4=spices)
c. On the antibiotic plate (1), label 4 areas of the plate with the first letter of an
antibiotic: N, P, S, A. Put the plain sterile disk (C) in the center.
1=Antibiotic plate
N
P
C
S
A
d. Plate #2 label each of the four quarters with the first letter of the specific
disinfectant.
e. Plate #3 “ label each of the four quarters with the first letter of each antiseptic.
f. Plate #4 label each of the four quarters with the first letter of the spice to be
tested.
g. CONTROL disk with just sterile water will be in the center of plates 2,3 and 4.
4. Prepare a bacterial lawn:
a. Wash bench surface with 10% bleach in squeeze bottle on bench.
b. Insert a sterile swab into the bacterial culture in liquid nutrient broth.
c. Allow the swab to drip for a moment before taking it out of the culture tube. (The
swab should be soaked but not dripping.)
d. Carefully lift the lid of one agar plate to about 45° and swab the entire surface of
the agar including right to the edges of the dish: VERY IMPORTANT-why is it
important to have a lawn of bacteria covering the entire plate?
1. Apply bacteria evenly over the entire agar surface;
2. Rotate the plate and swab at right angles to the first application (Figure 1)
e. Cover the plate with the lid.
f. Dispose of used swab in the beaker labeled 'biohazard waste''
g. Repeat this procedure on the other agar plates.
h. Wash your hands with soap and water.
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Real time evolution 2- 5
Figure 1 Prepare a bacterial lawn by swabbing the entire surface of the agar plate
5. Plate #1 Antibiotics Carry one plate to the designated area to dispense the antibiotics.
a. Dip forceps in 70% alcohol
b. Flame using alcohol burner (TA will demonstrate)
c. Allow forceps to cool about 20 seconds.
d. Use dispenser to place correct antibiotic in each section of the plate; use
sterilized forceps to adjust position of disk to center of section.
e. Control disk: (sterilize forceps by repeating sets a and b above); transfer one
plain sterile disk to the center of the agar plate.
6. Plate #2 Disinfectants& plate #3 antiseptics (materials are on your bench)
a. Dip forceps in 70% alcohol
b. Flame forceps using alcohol burner.
c. Allow forceps to cool about 20 seconds.
d. Use sterilized forceps to add disk to each disinfectant and antiseptic liquid. Be
careful not to touch the liquid with the forceps. Soak in liquid for about 20-30
seconds.
e. Use sterile forceps to remove from liquid (allow excess liquid to drip off before
transferring disk to agar plate). Position appropriate disk in each labeled section.
f. Control disk: (sterilize forceps by repeating sets a-c above); transfer one plain
disk to soak in sterile water for about 30 seconds. With forceps hold disk over
liquid to drip off excess water before transferring disk to the center of the agar
plate.
7. Plate #3 Food spices Flame forceps & cool, then apply disk-sized amount for the four
spices your team is testing. (The spices do not stick to the disks, so you are applying the
spice directly to the agar.) The area in the centre of the plate will have no spices, so will
serve as the control.
8. Seal each plate with a strip of Parafilm.
9. Put your plates in the designated tray with lid side up (so disks don’t come loose—
normally you store plates upside down so condensate doesn’t drip onto cultures). Your
plates will be incubated at 37°C for about 24 hours and then stored in the refrigerator
(about 4°C) until your next lab when you will record the results.
10. Wash bench surface with 10% bleach.
11. Wash your hands with soap and water.
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Real time evolution 2 - 6
12. Cleanup:
¾ Place your labeled agar plates, right side up, in the labeled bin on the
¾
¾
¾
¾
side counter.
Make sure all swabs are disposed of in the beaker on your bench labeled
BIOHAZARD WASTE'.
Dispose of Parafilm backing, spent matches, and any other garbage in the regular
garbage bins.
Make sure your alcohol jar and alcohol lamp have their lids replaced. Return all the
materials as you found them on your bench at the beginning of the lab.
If you rented a lab coat from the technician, please return it before leaving.
(Next Lab Record the results)
In the next lab you will determine the bacterial sensitivity: Use a small clear metric ruler to
measure the diameter of the zone of inhibition (clear area) around each disk. This zone is the
area where bacteria growth has been inhibited. For examples see
http://gold.aecom.yu.edu/id/micro/directsensi.htm In addition to the measurements, use the
following arbitrary criteria to rank the relative bacterial sensitivity:
NS= not sensitive = no zone of inhibition S= sensitive= zone < 1cm VS= very sensitive= >1cm
3% (20 points) Analysis of the results-done individually, not as a team.
Due at the beginning of Lab 4
(Remember your name & last 4 digits of your student # on the cover page)
1. Table 1 of your raw data (-2 if not included) and list the first names of the others in your team.
2. (4 points total) Graph data collected in Table 1 comparing the zone of inhibition for the 4
antimicrobials
Figure 1 antibiotics
Figure 2 disinfectants and antiseptics
Figure 3 spices
Figure 4 # colonies growing in the zone of inhibition
3. (16 points total) Discuss the meaning of the results (maximum 2 typed pages)
(1) State your original hypothesis and prediction of bacterial sensitivity to antimicrobials
in general.
(3) State which type of antimicrobial you predicted would be the MOST effective and
LEAST effective and briefly explain why.
(4) Discuss the meaning of any colonies growing in a zone of inhibition—did your plates
have any? How did the number of colonies vary for the different antimicrobial
substances? (If none of your plates had colonies growing in the zone of inhibition,
discuss what these colonies would mean.)
(8) Based on YOUR data which antibiotic, disinfectant, antiseptic, and food spice are the
MOST effective and which are the LEAST effective to kill E. coli? Discuss the
implications for your findings for each type of antimicrobial.
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Real time evolution 2- 7
Table 1 Escherichia coli sensitivity to antibiotics, disinfectants, antiseptics, and food spices.
Zone of inhibition Relative sensitivity
#f colonies growing
(cm)
NS
S VS
in the Zone
Plate 1 Antibiotics
Neomycin
Penicillin
Streptomycin
Ampicillin
Control (water)
Plate 2 Disinfectants
Ammonia
Lysol
70% Ethanol
10% Bleach
Control (water)
Plate 3 Antiseptics
Hydrogen Peroxide
Listerine
Antibacterial soap
Control (water)
Plate 4 Spices
BIO152 2006
Real time evolution 2 - 8
Exercise 2 Sample size and sampling error
(adapted from M. Rutledge 2001 Sampling Error Bioscene 27 (1) 3-6.)
Case study
Flora bicolour is a recently described plant native to the area. The plant’s name reflects the fact
that it exists in two varieties: one produces white flowers and one red. All known populations
contain both varieties of the plant. Little is known of the plant’s biology, including the percentage
of each variety in populations.
Recently a large population of Flora bicolour was discovered locally on an accessible site. This
population affords the opportunity to seek answers to some basic questions about the species
including determining the percentage of each variety in the population. Answering this question
may lead to an understanding of the phenotypic variation within the species and the selective
forces acting upon the species.
This exercise will simulate a plant population with coloured beads in a paper bag.
You will conduct four studies each using a different sample size to answer the question:
“What is the percentage of the white-flowered variety in the plant population?” Record your
results in Table 2 and Figure 2 (plus acetate sheets in lab to compile class data).
By the end of this exercise you should understand the effect that the size of the sample has in
accurately predicting the characteristics of the entire population. This relationship of sample size
to population is called sampling error.
Procedure (work in teams of half benches—about 4 students)
One person on the team will write your team data on the class Figure. Each team of 4 students
will be assigned two questions by your TA to complete and hand in by the end of lab. Your team
will receive one mark for this exercise.
Each team will be given mixture of red and white beads in a paper bag. The total number of
beads is 100. Note the letter on the outside of the bag to use when you record your results for
the class. Each group will be given a bag with the same total number of beads and the same
number of red and white beads (representing the two varieties of Flora bicolour). Each pair will
do THREE trials. Each trial will test a different sample size (4, 30, 60 beads)
1.
2.
3.
4.
5.
Hold the paper bag closed and vigorously shake to mix the beads.
Take out beads one at a time and place that bead in a plastic cup.
Take out the designated sample size (4, 10, or 30 beads)
Determine the percentage of white beads (flowers) for each sample size (4, 10, 30)
Repeat steps 1 through 4 for the other sample sizes.
6. Record estimated percentage of white beads for the whole population based on the
percentages for each of your sample sizes in the appropriate space of the class data
table next to your bag’s letter (similar to Table 2).
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Real time evolution 2- 9
Table 2 Frequency estimates of white beads generated by varying sample
sizes
Team’s
letter
Sample size n=4
#white #red
%white
Sample size n=30
#white #red
%white
Sample size n=60
#white #red
%white
7. Record your estimate on the class graph –record your TEAM# above the percentage
white for each of the three sample sizes (n = 4, 10, 30) (similar to Figure 2 below)
______________________________________________
0 10 20 30 40 50 60 70 80 90 100%
n= 4
______________________________________________
0 10 20 30 40 50 60 70 80 90 100%
n= 30
______________________________________________
0 10 20 30 40 50 60 70 80 90 100%
n= 60
Figure 2 Frequency distribution of estimates of white beads (flower) using varying sample
sizes
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Results & discussion
By the end of this exercise you should see the inherent variability seen in estimates from the
three sampling procedures. Your team will be asked to hand in your answers to TWO of the
following questions (assigned in lab by your TA):
1. Define sample size and sampling error.
2. Discuss the relationship between sample size and sampling error.
3. How does the estimate of white bead frequencies vary with sample size (compare the
distribution patterns generated by each sample size).
4. Describe further experiment(s) you could do to improve the estimate of the percentage
of white beads.
5. What is the percentage of white beads in this population?____ Defend your answer:
Exercise 3 Deconstruct the science—hypothesis testing
Objectives by the end of this exercise you should be able to
1.
2.
3.
4.
5.
List and recognize the steps of the scientific process.
Generate hypotheses from observations.
Given a hypothesis, state the null hypothesis.
Formulate predictions from hypotheses.
Recognize and Identify the components of a controlled experiment:
a. variables (standardized, independent, dependent),
b. controls
c. sample size and replication
6. Critique a simple experiment.
Procedure
Before lab read the background information about the scientific method (Appendix 2 and
Freeman text section1.4 Doing biology). Also before lab read the news story below. Your group
must “deconstruct’ the experiment described in the article. One person in the group should
complete the TEAM answer sheet handed out in lab.
Use the space below to record your own information:
What was their original observation?
What was the hypothesis?
What was the null hypothesis?
What do the authors predict to see if their hypothesis is correct?
Describe their experimental design: find answers to each of the following in the article. (if some
components of the design are not describe in the article, indicate not available (NA)).
Sample size
Control(s)
Independent variable
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Real time evolution 2- 11
Dependent variable
Standardized variables
Replication
What were the results of the experiment?
What did the authors conclude? Do you agree (why/why not)?
Cornell News http://www.news.cornell.edu/releases/March98/spice.hrs.html
WEDNESDAY, MARCH 4 accessed July 17, 2006
Food
bacteria-spice
survey
shows
why
some
cultures
like
it
hot
Don't expect cayenne in Copenhagen, say Cornell biologists who demonstrated cultural
coevolution of antimicrobial spice use with food-spoilage microbes in torrid climates.
The same chemical compounds that protect the spiciest spice plants from their natural enemies
are at work today in foods from parts of the world where -- before refrigeration -- food-spoilage
microbes were an even more serious threat to human health and survival than they are today,
Jennifer Billing and Paul W. Sherman report in the March 1998 issue of the journal Quarterly
Review of Biology.
"The proximate reason for spice use obviously is to enhance food palatability," says Sherman,
an evolutionary biologist and professor of neurobiology and behavior at Cornell. "But why do
spices taste good? Traits that are beneficial are transmitted both culturally and genetically, and
that includes taste receptors in our mouths and our taste for certain flavors. People who enjoyed
food with antibacterial spices probably were healthier, especially in hot climates. They lived
longer and left more offspring. And they taught their offspring and others: 'This is how to cook a
mastodon.' We believe the ultimate reason for using spices is to kill food-borne bacteria and
fungi."
Sherman credits Billing, a Cornell undergraduate student of biology at the time of the research,
with compiling many of the data required to make the microbe-spice connection: More than
4,570 recipes from 93 cookbooks representing traditional, meat-based cuisines of 36 countries;
the temperature and precipitation levels of each country; the horticultural ranges of 43 spice
plants; and the antibacterial properties of each spice.
Garlic, onion, allspice and oregano, for example, were found to be the best all-around bacteria
killers (they kill everything), followed by thyme, cinnamon, tarragon and cumin (any of which kill
up to 80 percent of bacteria). Capsicums, including chilies and other hot peppers, are in the
middle of the antimicrobial pack (killing or inhibiting up to 75 percent of bacteria), while pepper
of the white or black variety inhibits 25 percent of bacteria, as do ginger, anise seed, celery
seed and the juices of lemons and limes.
The Cornell researchers report in the article, "Countries with hotter climates used spices more
frequently than countries with cooler climates. Indeed, in hot countries nearly every meat-based
recipe calls for at least one spice, and most include many spices, especially the potent spices,
whereas in cooler counties substantial fractions of dishes are prepared without spices, or with
just a few." As a result, the estimated fraction of food-spoilage bacteria inhibited by the spices in
each recipe is greater in hot than in cold climates.
Accordingly, countries like Thailand, the Philippines, India and Malaysia are at the top of the hot
climate-hot food list, while Sweden, Finland and Norway are at the bottom. The United States
BIO152 2006
Real time evolution 2 - 12
and China are somewhere in the middle, although the Cornell researchers studied these two
countries' cuisines by region and found significant latitude-related correlations. Which helps
explain why crawfish “etoufŽe” is spicier than New England clam chowder.
The biologists did consider several alternative explanations for spice use and discounted all but
one. The problem with the "eat-to-sweat" hypothesis -- that people in steamy places eat spicy
food to cool down with perspiration -- is that not all spices make people sweat, Sherman says,
"and there are better ways to cool down -- like moving into the shade." The idea that people use
spices to disguise the taste of spoiled food, he says, "ignores the health dangers of ingesting
spoiled food." And people probably aren't eating spices for their nutritive value, the biologist
says, because the same macronutrients are available in similar amounts in common vegetables,
which are eaten in much greater quantities.
However the micronutrient hypothesis -- that spices provide trace amounts of anti-oxidants or
other chemicals to aid digestion -- could be true and still not exclude the antimicrobial
explanation, Sherman says. However, this hypothesis does not explain why people in hot
climates need more micro-nutrients, he adds. The antimicrobial hypothesis does explain this.
"I believe that recipes are a record of the history of the co evolutionary race between us
and our parasites. The microbes are competing with us for the same food," Sherman
says. "Everything we do with food -- drying, cooking, smoking, salting or adding spices - is an attempt to keep from being poisoned by our microscopic competitors. They're
constantly mutating and evolving to stay ahead of us. One way we reduce food-borne
illnesses is to add another spice to the recipe. Of course that makes the food taste
different, and the people who learn to like the new taste are healthier for it."
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Appendix 1 Background on controlling microbial growth
"Control of growth" means to prevent growth of bacteria in two basic ways: (1) by killing or (2) by
inhibiting growth. Control of growth usually involves the use of physical or chemical agents.
A. Physical means
1. Heat The lethal temperature varies for different microorganisms. The time required to kill
depends on the number of organisms, species, nature of the product being heated, pH, and
temperature. Whenever heat is used to control microbial growth inevitably both time and
temperature are considered.
Sterilization (boiling, autoclaving, hot air oven) kills all microorganisms with heat;
commonly employed in canning, bottling, and other sterile packaging procedures.
Pasteurization is the use of mild heat to reduce the number of microorganisms in a
product or food.
2. Low temperature (refrigeration and freezing): Most organisms grow very little or not at all
at 0º C. Store perishable foods at low temperatures to slow rate of growth and consequent
spoilage (e.g. milk). Low temperatures are not bactericidal.
3. Drying (removal of H2O): Most microorganisms cannot grow at reduced water activity. Often
used to preserve foods (e.g. fruits, grains, etc.). Methods involve removal of water from product
by heat, evaporation, freeze-drying, addition of salt or sugar.
4. Irradiation (microwave, UV, x-ray): destroys microorganisms as described under
"sterilization". Many spoilage organisms are easily killed by irradiation. In some parts of Europe,
fruits and vegetables are irradiated to increase their shelf life up to 500 percent. The practice
has not been accepted in the U.S.
B. Chemical means
Antimicrobial agents are chemicals that kill or inhibit the growth microorganisms. Antimicrobial
agents include chemical preservatives and antiseptics, as well as drugs used in the treatment of
infectious diseases of plants and animals. Antimicrobial agents may be of natural or synthetic
origin, and they may have a static or cidal effect on microorganisms.
Some types of antimicrobial agents
1. Antiseptics: Agent that kill microorganisms (microbicidal) harmless enough to be applied to
the skin and mucous membrane; should not be taken internally (Table 2).
2. Disinfectants: Agents that kill microorganisms, but not necessarily their spores, not safe for
application to living tissues; they are used on inanimate objects such as tables, floors, utensils,
etc. (Table 2)
Note: disinfectants and antiseptics are distinguished on the basis of whether they are safe for
application to mucous membranes. Often, safety depends on the concentration of the
compound. For example, sodium hypochlorite (chlorine), as added to water is safe for drinking,
but "chlorox" (5% hypochlorite), an excellent disinfectant, is hardly safe to drink.
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Table 21. Common antiseptics and disinfectants
Chemical
Action
Uses
Alcohols:
ethanol & isopropanol (50-70%)
Detergents
Phenolic compounds (e.g. carbolic
acid, lysol, hexylresorcinol,
hexachlorophene)
Denatures proteins and
solubilizes lipids
Disrupts cell membranes
Denatures protein and
disrupts cell membrane
Antiseptics at low concentrations;
disinfectants at high concentrations
Silver nitrate (AgNO3)
Precipitates proteins
General antiseptic and used in the
eyes of newborns
Tincture of Iodine (2% I2 in 70%
alcohol)
Inactivates proteins
Antiseptic used on skin
Chlorine (Cl2) gas
Forms hypochlorous acid
(HClO), a strong oxidizing
agent
Disinfect drinking water; general
disinfectant
Ethylene oxide gas
Alkylating agent
Disinfectant used to sterilize heatsensitive objects such as rubber and
plastics
Antiseptic used on skin
Skin antiseptics and disinfectants
3. Antibiotics: antimicrobial agents produced by microorganisms that kill or inhibit other
microorganisms. Antibiotics are low molecular-weight (non-protein) molecules produced mainly
by microorganisms that live in the soil. Among the moulds (eukaryotes in the Fungi Kingdom),
the notable antibiotic producers are Penicillium and Cephalosporium , which are the main
source of penicillin and its relatives. In Bacteria, the Actinomycetes, notably Streptomyces
species, produce a variety of types of antibiotics including streptomycin and neomycin (Table 3).
Table 32. The classes and properties of antibiotics used in this laboratory
Chemical class
Examples
Biological
source
Mode of action
Beta-lactams (penicillins and
cephalosporins)
Penicillin G
Penicillium
notatum
Inhibits steps in cell wall
(peptidoglycan) synthesis
Semisynthetic penicillin
Ampicillin
Inhibits steps in cell wall
(peptidoglycan) synthesis
Aminoglycosides
Streptomycin Streptomyces
Neomycin
griseus
Inhibits translation (protein
synthesis)
1&2
Kenneth Todar University of Wisconsin Department of Bacteriology, 2002
http://textbookofbacteriology.net/resantimicrobial.html accessed July 27, 2004
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Appendix 2: Science as a process: what is the scientific method?
Figure 3 The scientific method
A. Hypotheses
A hypothesis (plural, hypotheses) is an “educated guess” to the answer of a question being
investigated based on observations or previous research the scientist’s or others’. The null
hypothesis merely says that the hypothesis is not correct—the null hypothesis = restating the
hypothesis in the negative.
When you were given your mystery box, you were given a scientific problem to investigate: its
contents. Thus, the question was already defined for you. Recall the steps you went through to
scientifically investigate the contents of your mystery box (Prelab Exercise 0.1). These steps
began with observations that led to testable hypotheses.
How do you test a hypothesis? Generally, a hypothesis leads to very specific predictions that
are testable. For example, based on your observations (e.g. sound, feel, weight), you may have
hypothesized that your mystery box contained a roll of tape and a paper clip. You could have
tested this hypothesis directly by opening the sealed box, but this kind of direct observation is
not always possible. Instead, based on this hypothesis, you could predict what these contents
might sound like or weigh in another similar box. These predictions are then what is testable.
This is what we mean by saying that a hypothesis is testable.
B. Predictions
Do not begin an experiment without predicting the outcome. Your prediction should be based
on the particular experiment designed to test a specific hypothesis. It is easiest to phase your
prediction as an “if …then” statement:
“If the experiment [hypothesis] is true, then the results of the experiment will be …
Example 1: Hypothesis: Regular interaction with pets improves the health of the elderly.
Null hypothesis: Interaction with pets has no impact on health of the elderly.
Prediction: If regular interaction with pets improves the health of the elderly [notice this is a
restatement of the hypothesis], then the heart rate will be lower after exercise and return to
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Real time evolution 2 - 16
normal faster in elderly people who spend 20 minutes daily with their pet cat [predicted results
from the experiment].
Example 2: Hypothesis: Music lessons cause a greater increase in children’s IQ.
Null hypothesis: music lessons have no effect on children’s IQ.
Prediction: If music lessons increase children’s IQ , then students having weekly piano lessons
for a year should have a higher increase in IQ than students who do not have lessons.
Example 3: Hypothesis: Giraffes have long necks to out compete other animals for food.
Null hypothesis: Long necks do not help giraffes out compete other animals for food.
Prediction: If the main function of the giraffe’s long neck is for feeding, then giraffes should
regularly extend their necks when feeding. (See Freeman, Chapter 1 for a discussion on
hypothesis testing and why giraffes have long necks.)
Example 4: Hypothesis: The presence of capsaicin (molecule which makes chili peppers hot)
will deter some predators but not others. (See Freeman, Chapter 1.)
What is the null hypothesis? Prediction:
What is the dispersal hypothesis? Prediction:
C. The Nature of Scientific Knowledge
Scientific knowledge based on generalizations and conclusions drawn from specific
observations and experiments, is a process known as inductive reasoning. An alternative way
of reasoning that we also use in science is when we begin with general principles and predict
their consequences (deductive reasoning). While deductive reasoning is an important part of
science and is an essential part of the application of scientific principles, it is not a means for
gaining new scientific knowledge. All new scientific knowledge depends on inductive reasoning.
(Why?)
However, information gathered through inductive reasoning has a level of uncertainty. In the
inductive process, generalizations are made based on specific observations. Since it is never
possible to observe every possible case or scenario in an investigation, we must rely on
observations of a sample of all possible observations. For this reason, scientific “facts” are
always regarded with a certain level of skepticism rather than as absolute truth. In fact,
statistics are a formal way of quantifying an investigator’s uncertainty when experimentally
testing a scientific hypothesis. We will use statistics to test hypotheses in the next lab.
New knowledge is actually an accumulation of evidence which support hypotheses. When we
accept a hypothesis as “true”, we accept the hypothesis on a conditional basis: evidence may
support the hypothesis, evidence does NOT prove the hypothesis. Some future technology,
experiment or other information may falsify it.
Examine the scientific inquiry method closely: a single experiment can prove a hypothesis false,
but it takes many types of investigations before a hypothesis appears to be true. When we
prove a hypothesis false, we say that we falsify or refute it. Scientists DO NOT SAY that an
investigation proves a hypothesis true, recognizing the level of uncertainty involved in inductive
reasoning. Instead, we say that the data support the hypothesis.
New scientific knowledge seems tentative and it is only after much data has been gathered from
many experiments and observations that the knowledge is generally accepted as the “facts” you
read in your text books. Many of these “facts” were once very controversial. An excellent
example was the discovery of DNA as the hereditary material by Avery in 1944. Until then, DNA
was a weak candidate for the genetic material and most geneticists favored protein as the likely
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molecule. It wasn’t until the unique structure of DNA was clearly elucidated by Watson, Crick
and Franklin in 1953 that the last of the skeptics was convinced.
We have focused here on the scientific method using hypotheses that can be proven false
through controlled experiments. Experiments may also be based on the predictive power of
observation and comparison (comparative experiment), which cannot be tested with controlled
experiments. Hypotheses in the comparative method are tested by making predictions about
patterns that should exist in nature if the hypothesis is correct; data are gathered to determine if
the patterns exist. Examples: paleontologists who study the fossil record and astronomers who
study the stars.
Both a controlled experiment and an observation-based comparative experiment depend on
observable phenomena and inductive reasoning.
By its nature, scientific knowledge is
knowledge that can be proven false. This is not a requirement for other forms of knowledge
(e.g. aesthetic, philosophical, ethical, religious, etc.)
Understanding this is critical to
understanding the limitations of scientific inquiry. There are certain things it is simply not
possible to learn through science. For example, consider the following hypothesis: “The best
music of the century was written in the 1960s”. There is no experiment that can be performed
or observations made to test and potentially falsify the hypothesis.
D. Controlled experiments: components and design
(1) Variables
(2) Controls
(3) Replication and Sample Size
Once a question or problem has been identified and a hypothesis formulated, the next step in a
scientific investigation is to conduct an experiment to test the hypothesis. There are several
important factors to keep in mind when designing a suitable experiment. These include:
identifying variables to be tested, measured and held constant, controls to be run and how
many times to replicate the experiment.
(1) Variables
Variables are things that might be expected to vary in an experiment. There are those factors
the investigator wishes to manipulate in order to test their effect. These are known as
independent variables. As a result of changing something about an independent variable,
there may be some effect. Variables that are expected to change in response to independent
variables are called dependent variables. In order to be certain that any change is actually
due to changes in the independent variables and not to other factors, it is important to keep
standardized variables constant.
a. The independent variable is the variable the investigator wishes to test and is
deliberately varied.
For example, in today’s lab you were looking at the effectiveness of different antibiotics
on limiting bacterial growth. The independent variable are the four antibiotics (the
resulting zone of inhibition of E. coli is the dependent variable)
What is the independent variable in today’s lab testing E. coli sensitivity to the
1. Disinfectants
2. Food spices
It is not always possible or necessary for an investigator to directly manipulate an
independent variable in order to test its effect. So, for example, perhaps you thought
that the phase of the moon might affect mouse reproduction and could be an important
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independent variable in a thorough study of their reproductive biology. Even if the phase
of the moon can not be experimentally manipulated, it can still vary and be recorded as
an independent variable.
►Can you think of other independent variables like this that can’t be experimentally
manipulated?
b. Since more than one factor can be an independent variable that might affect bacteria
resistanc/sensitivity, it is typical to test only one independent variable at a time.
►Why is testing one variable at a time important?
►What must be done with the other possible independent variables you mentioned while
you investigate the effects on bacterial growth?
c. The dependent variable is what is expected to vary in response to the experimenter’s
manipulations, it is what will be measured or counted during the investigation.
For example, as stated above in the study of the effect different antibiotics on bacterial
growth, next lab you will be measure the zone of inhibition (killed bacteria or bacteria that
did not grow in the presence of the specific antibiotic). What is the dependent variable in the
experiment today with E. coli and various food spices?
(2) Controls
Control treatments are another necessary part of a well designed experiment. A control
treatment is a treatment in which the independent variable is either eliminated or set at a
standard value. The results of the control treatment are compared to the results of the
experimental treatments.
For example, in the experiment where bean plant seed production is measured after spraying
pesticides at various frequencies, it is important to include unsprayed plants as a control
treatment. However, for an experiment to test activity level of lizards at various temperatures, it
would not be possible to include a “no temperature” control treatment. Instead, the investigator
chooses a standard temperature (perhaps the average field temperature) as a basis of running
speed.
► Indicate an appropriate control treatment for each of the following examples:
a. An investigator wants to determine the dose of penicillin that is most effective at
combating strep throat infections.
b. Antibacterial soap is investigated to determine its effect during hand washing.
(3) Replication: repetition and sample size
Replication is another important part of good experimental design. A scientist must repeat
(replicate) an experiment many times, keeping the conditions as identical as possible, in order
to draw conclusions from the experimental results. Each time an experiment is done, the results
may be slightly different, because biological systems are inherently variable. (For example,
lizards don’t always run at exactly the same speed (do you?), so that any experiment comparing
lizard running speed may give slightly different running speed values from earlier experiments
even if the independent and standardized variables are exactly the same. Thus, when we
replicate an experiment, we can get a measure of the average value and also an idea of how
much variation there is among replicates. These values (average = mean, variation = variance)
are important statistical parameters that can be useful in hypothesis testing.
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Sample size is another aspect of replication. One way to replicate an experiment, is to repeat it
over and over. Another way is to perform it on a large sample size simultaneously. In the
investigation of insecticide effect on bean seed production, it would be risky to conclude much
from a sample of six plants: two each with each of three levels of insecticide. If one plant died in
any of the groups, it would be hard to know if it was due to the insecticide level or to some other
factor. The most convincing results come from experiments done with both replication and with
adequate sample size.
Observation-based comparative experiments
Some experiments do not have a control, but rather an investigator compares two or more
groups to look for similarities and differences. Example: You are a chicken farmer and wanted to
know the ideal number of chickens per room so that the chickens laid the most number of eggs.
You could set up an experiment with different numbers of chickens in identical rooms (same
light, temperature, air circulation, etc.) and compare the resulting number of eggs.
Notice in this example the farmer does not have a control unless she set up the experiment to
compare the density greater and less than current standard number of chickens kept in each
room.
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