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>> Hello. Welcome to Biology,
the Fabric of Life.
Biology is the study of life.
If you go back 200 years,
biology largely amounted to
documenting the phenomena one
observed in nature.
If you travel to the Yellowstone
ecosystem at the turn of the
19th century, you would have
observed bison, elk, deer,
bears, wild flowers, trees and
other organisms, and documented
what you saw.
You may even have seen and
recorded bears or wolves feeding
on those bison or elk, as well
as various interactions with
then and between the many
species observed in your
environment.
Those kinds of observations are
still significant modern biology
as it is important to monitor
the changes in nature.
Modern biology, however, goes
well beyond that.
Modern biology is concerned with
the processes that are
responsible for the phenomena we
observe, and it is these
processes with which we will be
most concerned in this course.
To understand these processes,
you will need a background of
some very basic chemistry.
This will help you understand
the biochemical processes
required for life, as well as
the energy flow in ecosystems.
Additionally, you will then be
able to understand the
relationship between DNA and an
organism's physical and
behavioral attributes and how
that genetic material is passed
from generation to generation.
You will learn about genetic
technology and its many uses.
We will examine the evolutionary
process responsible for the
extraordinary diversity of life
before exploring that diversity
and how the many organisms of
nature interact in ecosystems.
Finally, we will consider the
ecological challenges imposed on
nature by humanity.
All these couch within the
umbrella of the scientific
process responsible for the
discovery of this vast body of
knowledge.
If all goes well, the knowledge
gained in this class will make
you a better educated citizen
with an understanding of the
process and value of science,
the composition and biochemistry
of your body, the role of
important biological
technologies in your life such
as DNA fingerprinting and PCR,
and an appreciation for how the
natural world functions and the
human impact on nature.
So where you to go to the
Yellowstone ecosystem at the end
of this course in this, the 21st
century, it is my hope that you
would not just see bison,
wolves, bears, grasses, flowers,
trees as individual entities
occupying the same space in
time, but see the chemical
reactions behind the organisms,
the flow of energy through the
ecosystem, the family tree that
links them all together in a
continuum of life and feel a
responsibility to protect the
natural world of which we are a
product and upon which we are
ultimately dependent.
So, given that biology is a type
of science, naturally where I'd
like to begin with the course.
Let's look at the nature of
science and the characteristics
of good science and bad science.
So science is-- ultimately has a
dual nature to it.
It is a way of knowing that
utilizes a process to generate a
body of knowledge and that body
of knowledge then is the product
of science.
So, I'd first like to focus on
this scientific process or so
called scientific method and it
begins with, you know, just like
the other types of basically
gathering information or getting
experience in some kind of an
area.
And as you-- you learn about a
certain topic, you then ask
questions.
And so, most of the time when
you ask questions, you know, you
can find answers to those
questions.
So again, even in a scientific
setting, what you do is you
would you know again go to
scientific literature, you might
talk to your colleagues and so
on, and you would find answers
to your questions.
But occasionally, you will pose
a question in the sciences, and
I suppose other areas as well,
for which there is a no known
answer.
And so at that point then, when
you hit that wall, when you now
go into a known territory, this
is where you pose a hypothesis
in the sciences.
And so the hypothesis then is a
possible explanation to a
question that you have post.
Ideally, it is disprovable that
it-- the very least testable, so
again in the science of it we're
gonna have to test this
hypothesis, and generally
hypothesis tend to be very
specific or narrow in scope.
Now, the next step then is again
you pose this possible answer to
your question, and again, you
could just believe it outright.
That would be one way of
knowing.
But in the sciences, we have to
test our ideas.
You would have-- now have to put
this hypothesis to some kind of
a test.
And there are various ways that
you can do that, but really the
best way, kind of the gold
standard would be a controlled
study or an experiment.
And there are different elements
to a control study or an
experiment and I'm first gonna
briefly discuss these and then
we'll look at an example and I
would think it will make them a
lot more understandable.
So, the first element is we
would want this to have an
independent variable and so
ideally there is a single
independent variable in a
well-designed experiment.
An independent variable would be
really that thing that you're
testing or the thing that you're
going to manipulate in your test
and see what the effects are.
The dependent variable or
variables will be those things
affected by your independent
variable, and normally you can
put an experiment into this kind
of a phrase, what is the effect
of blank on blank?
And when you do that, the first
blank then would be the
independent variable and the
second blank would be the
dependent variable, and again
we're looking on an example of
this later.
Okay. So again, in addition to
having independent and dependent
variables then, you also then
will need to have at least 2
groups, one that would be the
experimental group and the
experimental group is going to
be exposed to your independent
variable, and then a control
group in which you withhold the
experimental variable.
Now the importance of that,
again, we'll see in a few
minutes, but basically it's then
to have a comparison so that you
can see what happens when you
have your independent variable
and what happens if that is not
present.
Now sometimes in the control
group, you can't always withhold
the independent variable.
So for example, if you're
looking at the effect of
temperature on some kind of
system, you really can't
withhold temperature.
There's always gonna be some
temperature, maybe very cold or
very hot, but temperature you
know there's always some degree
of temperature.
So in those kinds of situations,
you more or less arbitrarily
pick, you know, one to be the
experimental group and one to be
the control group or you'll pick
the norm to be the control group
and the thing that's kind of
abnormal to be your experimental
group.
And then last but not least,
you'd have a number of control
variables and these will be all
factors that would be held
constant or consistent between
your experimental group and your
control group.
So, let's go ahead and consider
an example.
Let's say that you've done some
research, you're interested in
disease and you've-- in human
health, and you have learned
that there's a part of the
country where there's a cluster
of birth disorders and let's say
that this is near an ecological
superfund dumping site and
there's a particular pesticide
that's associated with this dump
and it's suspected that this
pesticide my have something to
do with this increase in birth
disorders that you're seeing in
this area.
And so you wanna put this to a
test.
Your hypothesis is then that
this pesticide is causing
developmental disorders.
So, you want to put this to a
test.
So you decide that you're going
to use some kind of an animal
model for this and you decide to
use something fairly simple and
easy to work with which would be
chick development, so you're
gonna work with eggs.
So what you do then is take a
bunch of eggs, let's say you
take a hundred eggs and eggs
have a little air cell at one
end of the egg.
And so what you do then in
trying to use sterile technique,
you'd punch a little hole on
that eggshell and then you're
going to inject your pesticide
into that air space at the end
of the egg and then let that
pesticide diffuse into the egg.
Now, let's say then, let's
settle a complication here.
Let's say that the pesticide
also does not dissolve very well
in water, and so you have to
dilute that in some weak alcohol
solution.
So you're going to be injecting
then a combination of pesticide
and alcohol.
So, you inject those into the
air cell of the egg, you patch
the hole and that can be done
with wax or even Elmer's Glue,
but you'd hopefully be using
some sterile technique here.
And then, again, that will
eventually diffuse into the egg
itself and you're gonna see what
effect this has on development.
So then the eggs then would be
put into an incubator and
allowed to develop and you'll
monitor that development and
look to see how many developed
normally and how many either die
during development or end up
being deformed in some way.
So in this example then, the
independent variable would be if
we phrase it this way, "What is
the effect of blank on blank?"
We're interested in what is the
effect of pesticide on-- or
dependent variable, which in
this case will be development.
So pesticide will be the
independent variable,
development will be the
independent variable.
And again we would have 2 groups
here, experimental group and the
control group.
And so the group that I
described just a minute ago,
that would be our experimental
group.
The experimental group then
would be exposed to the
pesticides, so you take a
hundred eggs, you're injecting
pesticide and alcohol, that
would be your experimental
group.
So, the question then becomes
really with your control group
is how are you going to define
your control group?
'Cause ideally, remember, we
want a single independent
variable.
So, one option you could have
would be to take a hundred eggs
and just incubate those hundred
eggs without doing anything to
them.
But we have to decide then,
would that be the best kind of
control group.
So, again, we want a single
variable but we did a lot of
things to those eggs, you know,
we poked a hole on the egg.
Does poking a hole in an egg
affect development?
I don't know.
Do we want to test that?
No. We want to test the effect
of pesticide in development.
So, you would need to poke a
hole on the egg.
What about alcohol?
Does alcohol affect development
of the chick?
I don't know.
That's not what we want to test
here.
We want to test the effect of
pesticide on development.
So again, you would need to
inject alcohol into the egg.
You need to patch the hole.
So in other words, you want to
treat your control group exactly
the same as your experimental
group except for one factor, and
that one factor will be our
independent variable which is a
certain concentration of
pesticide.
So again, with our control group
then, we would want to poke a
hole.
We would want to inject alcohol
without pesticide in this case
into the egg, and eventually
that would diffuse into the rest
of the egg.
We patch the hole, and that
would now be our control group.
So, all of the things that we
hold constant between the
experimental group and the
control group, we call it
controlled variables.
So in addition to treating them
the same way, we would want
other variables to be consistent
as well.
So in other words, would it
matter if we use different
breeds of chickens?
I don't know.
It might. So, we want to use the
same breed of chickens.
Would it matter if we use
different types of incubators?
I don't know.
It might. So, we would want to
use exactly the same incubator,
perhaps even rotate them within
the same incubator if possible.
So, everything you could
possibly think of to be
consistent between the
experimental and control group,
you want to be consistent except
for one single factor, which
would be the independent
variable.
Okay. Now, once you've run your
experiment then, the next step
in the scientific process then
is to objectively, and by that I
mean statistically evaluate the
data and then come back and
evaluate your hypothesis.
So whenever possible, again, we
try to design experiments that
we can statistically evaluate
because again that takes out any
kind of prejudice that you might
have in that evaluation.
Now, I'm gonna come back to that
statistical evaluation in just a
second, but one thing I do want
to mention is that
experimentation does not
always-- or a controlled study
is not always the best way to
test an idea.
Sometimes, you just have to make
more observation.
So, an example of this for--
that I can give you is if you go
back to the, you know, 1960's
when Jane Goodall and her
associates began to observe
primate behavior.
At that time we thought that,
you know, primates were
exclusively herbivores, you
know, kind of "peaceful
herbivores" if you will.
But when biologists and
physiologists and zoologists
began to look at their diet,
they realized that the diet that
was being observed was fairly
long protein and it really
couldn't account for the biomass
of the animals, let alone their
ability to reproduce.
And so it's believed that they
had to be getting additional
protein in their diet.
So, one of the hypotheses at the
time was that they may have been
eating or kind of competing
hypothesis that they were eating
some fruits or vegetables that
were high in protein and others
felt that they were eating meat.
And so, anyway, there's no real
controlled study for that
hypothesis.
So in that kind of a situation,
what you do is you just go back
out in the field and make more
observations, and what was
eventually observed was that,
indeed, occasionally chimpanzees
do hunt and basically they hunt
monkeys and just hunt them down,
run them down and rip them apart
and eat them.
So, you know, the peaceful
herbivore actually had a
carnivorous side to them as it
turned out.
So again, a controlled study
doesn't always lend itself to
the information that you're
trying to pursue or a particular
hypothesis.
So sometimes controlled studies,
some times you just have to make
more observations.
Now, getting back then to
statistical evaluation.
When we look at data sets, say
an experimental control group or
just any data sets, there's
always gonna be a difference
between them.
So example, if I were to compare
the height of one class to the
height of another, they're not
gonna have exactly the same
average height.
There'd be a slight difference
there.
So, what we have to consider
then as scientists is not, you
know, just that there are
differences, but we have to ask
ourselves or the observed
differences due to chance, just
kind of a sampling artifact or
is there a cause for the
difference.
So again, if I'm looking at 2
different classes with a
slightly different height, is
that just the way it happened?
Is that just the way the
sampling went that time, or is
there a cause for one class to
be taller than another?
In other words, if a class had
predominantly males compared to
females, it would-- there would
be a cause there 'cause males
tend to be taller than females.
So this is one of the dilemmas
we have when we look at data
sets.
There's always gonna be a
difference, and so what's the
importance of that difference?
Is there a cause or is it just
due to chance?
So, we have to first ask
ourselves, you know, can chance
duplicate the differences we see
in our experimental or control
groups.
And then if yes, how frequently
are we willing to tolerate just
a chance duplication?
In other words, how much
competence can we have in the
results if just randomly we
could get the same kind of
result?
So, what I mean by this is let's
say we go back to our example
here with the eggs.
Let's say in our experimental
group that 85 of the 100 eggs
develop normally and let's say
in our control group, 92of the
eggs developed normally.
So, that would give us a
difference then of 7 chickens,
if you will, or 7 chicks that
developed normally.
So by looking at this single
example you'd say, "Well, gosh,
you know, those that were
exposed to the pesticide, you
know, more were abnormal so the
pesticide must be causing the
difference."
Well, and that would-- that
would possibly be true, but we
would want to examine this a
little bit further again and
from a side effect perspective.
So one of the questions we would
have then, what if we just
randomly assigned this to 2
different groups?
Could I get the same kind of a
difference, and so let's say
then that you just randomly took
a group of a hundred eggs, 50 of
which had been exposed to
pesticide and 50 of which had
only been exposed to the
alcohol.
How many of them would have
developed normally?
And then if you took a second
group of a hundred, again, kind
of randomly assigned 50 that
were exposed to pesticide and 50
that were not, how many of those
will develop normally and what
kind of a difference would you
get and how frequently would it
be equal to or greater than 7?
So in other words, if we just
randomly assigned them to 2
groups instead of an
experimental-control group,
could we get the same results?
And if we did, how frequently
would that be an acceptable
result to us?
And so the standard that we're
using in biological sciences is
what we call a 95 percent
confidence.
So what that means is that if we
rerun our experiment a hundred
times that we would get-- we
would be certain that there was
a cost for the difference 95
times or more per hundred.
So in other words, we have to be
confident that there was a cost
for the difference as opposed to
just chance duplicating our
results more than 95 percent of
the time.
And if that's the case, then we
say we have a 95 percent
confidence.
So again, rather than actually
rerunning the experiment
hundreds or thousands of times,
we have statistical analysis
that we can do to do this for
us.
But again, this 95 percent
confidence is our minimum
standard to say yes, there is a
cause for the difference on our
data sets.
And when we have that kind of
confidence, the 95 percent
confidence, the phrase that we
use is we say that there is a
significant difference in the 2
groups.
So in everyday language, we
constantly talk about, "Oh, it's
very significant and there is a
significant this and that."
But in the sciences, when we say
there is a significant
difference, it means we have at
least a 95 percent confidence
that there's a cost for the
difference in our groups.
And we can also breakdown our
ideas in what we call null and
an alternate hypothesis, and the
null hypothesis is a standard
hypothesis which says that the
difference in our groups is due
to chance.
The alternate hypothesis is that
the difference in our groups is
due to a cause.
And so again if we have at least
the 95 percent confidence in our
results, then we would reject
the null hypothesis and accept
the alternate hypothesis that
there is a cause for the
difference.
So, null hypothesis versus
alternate hypothesis.
Okay, now getting back to our
process then, one way of
objectively evaluated and
physically evaluated our data
then, we can either accept our
hypothesis, reject our
hypothesis or we may modify the
hypothesis and retest again.
So, as we look at the scientific
process then, the next step then
would be to communicate results
and we can do this formally in
per review journals or
scientific conferences, also of
course scientists just
informally communicate with one
another all the time.
But the important is to
communicate because, again,
we're looking now at an area
where there is no answer.
No one's ever perhaps
investigated this idea before.
And in our example, if I were to
present data, I'm sure I'd find
out there are a lot of problems
with that example, you know.
So, in other words, would a one
time exposure to the pesticide
be the same as chronic exposure?
No. It wouldn't, you know, so
that's not a great thing.
Also, would be using a chicken
as a substitute for human
tissues be a good substitute?
You know, probably not.
So there are a lot of problems
actually with our little example
and I'm sure I would find those
out if I went public with my
findings.
So it's very good and important
to get not only communicate and
say, "Hey, this is what I've
done, but also then get the
feedback and then others can try
to duplicate your results.
So that's another aspect of
this, is that others have to be
able to get the same results
that you do for it to have
scientific validity.
So when we look at the products
if science then, certainly the
data that we generate is one of
the products.
The hypothesis that we proposed
and test would also be a
product.
And again, I think the public
has this idea that hypotheses
are just kind of "educated
guesses" and they may start out
that way.
So hypotheses start out as being
unverified.
But once they've been tested and
then retested and retested and
retested, they become verified
hypotheses and verified
hypotheses are factual.
Likewise with theories, and
again I think most people in the
public think that a theory just,
you know, is very nebulous,
hasn't really been tested.
But that-- and that can be true
initially, but once you retest
and retest and retest theories,
they become verified theories as
well.
And again, we look at them as
being factual.
So, a theory then is built from
related hypothesis using
inductive logic, we'll talk of
this in a second.
It usually addresses the
mechanism behind the phenomenon
that you observe.
It tends to be predictive using
deductive reasoning and a
verified theory again we can
consider it to be factual.
So-- so again, once ideas have
been tested and retested, they
gain confidence and eventually
we look at them as being
factual.
So when we look at the products
of science then, again, data,
hypotheses, theories, and then
also principles and laws are
sometimes used.
We don't use those a lot in the
biological sciences, but
basically principles and laws
are just statements of
observations that have been made
and, again, usually are
considered factual.
So an example would be the law
of conservation of matter.
The law of conservation of
matter simply states that matter
is neither created nor destroyed
in chemical reactions.
So, it doesn't really address
the mechanism behind that.
It's just merely a statement of
observed phenomena and again is
generally considered to be
factual.
So again, in the general
public's mind, I think they
think that hypotheses are very
unverified and that laws are
very verified, but again, we can
look at hypotheses, theories,
and the principles and laws as
all being verified and
essentially factual.
So, we use basically 2 types of
logic then in building our
ideas.
One is inductive reasoning or
logic and here is where we-- we
take specific examples and build
to broad conclusions and this is
how hypotheses are used to build
theories.
So for example, with the
discovery of the microscope, you
know, as people began to look at
different types of organisms,
they always found that they were
composed of-- of cells or the
products of cells.
And so over time, after
repeatedly seeing this over and
over and over again, this
generally-- eventually led to
the general theory, the cell
theory which is that organisms
are composed of cells and their
products and that cells give
rise to other cells.
So again, you're building from
very specific observations to a
broad idea.
That's inductive reasoning.
And then, deductive reasoning or
logic, you take a general
concept to make specific
predictions.
So, you'll take-- and this is
how we use theory to predict
hypothesis and generally this
can be phrased as an "if then"
kind of statement.
So, if organisms are composed of
cells and their products then,
even though I mean ever have
looked at a banana leaf, if I
look at a banana leaf, I would
predict that it would be
composed of cells.
So-- and I would find that to be
true if I did so.
So that would be deductive
reasoning or logic.
So as we look at characteristics
or-- or qualities of good
science, one of the
characteristics is that science
is mechanistic.
In other words, we have to be
able to rest the mechanisms
behind what we observe and I
think we kind of intuitively do
science all the time, and an
example I often use is when my
oldest daughter who's a grown
woman now but when she was
child, she had been to her
grandmother's house, her
grandmother had-- is a Jehovah's
Witness and had been talking
about how God did different
things and so my daughter was
eating dinner one night as the
sun was setting and she was
saying that grandma says God
does this and grandma says God
does that, and then she was
looking at the sunset and said,
"Hey daddy, how does-- what
makes the sun go down?"
And I said, "Well, you know, God
does it."
And she said, "No daddy, I mean
really?"
And so, what she was asking for
was a mechanism to explain what
she was seeing and so, you know,
I got out an orange and put a
dot on it and a flashlight and
showed her how the earth spins
and how when you approach the
light, that's sunset and you go
away from the light, that's
sundown and I didn't think much
of it, and then when she was
around 12, we were-- we were,
you know, driving along and the
sun was setting very rapidly in
the distance and she said, "Hey
daddy, look how fast the earth
is spinning."
And so, I was very impressed by
that that she remembered that.
But the point is, she was look
at a mechanism.
She was interested in the
mechanism behind what she was
seeing, and again, we kind of
intuitively do that.
Now, we also have to work in a
natural world.
So we have to generate data and
evaluate data.
So that is something then that
is in the natural world.
Its magic is not a scientific
answer because we can't evaluate
that in any-- any way, shape, or
form and so, again, this is one
of the places where, you know,
science and religion I think
collide 'cause we can't go to
that "God did it" kind of an
answer.
We have to examine and answer.
Ideas have to be testable.
So in other words, I just can't
decide to believe something
'cause I wanna believe it, or
you know, or that it's logical.
That's not good enough.
We have to put it to a test,
generate the data, evaluate and
then come back to our
hypothesis.
Along the same line then,
science is evidentiary.
So again, it has to generate a
product that can be evaluated.
So I might have a dream that a
certain drug is-- will cure
cancer.
That doesn't make it true.
So again, I would have to put it
to the test, generate the data
and see if that is true or not.
So again, we have to be based on
evidence and we just can't kind
of a priori go to a conclusion
and accept a conclusion as fact.
We also have to be objective,
and so again kind of the
antithesis of objectivity would
be faith.
So we cannot have faith in our
results or faith in ideas.
Our ideas are only as good as
the next test.
So we have to be as objective as
possible.
So, kind of related to this I
will be talking about in a
supplement to this unit about
anecdotes and testimonials, and
I'll come back to that, and also
placebo effects.
So that's something I'll talk
about in a supplement to this
particular module, and as
related to that then what are
called blind studies and
double-blind studies and the
importance then in doing these
kinds of studies to account for
the placebo effect and ensure
objectivity.
Now, continuing this idea then
of-- of the characteristics of
good science then is that
science-- scientific ideas are
tested and also have to be
repeatable.
So again, if I do a certain test
on a hypothesis and I get a
certain result, you should be
able to do the same test and get
the same result and as a
graduate student, you know,
that's what happens a lot of
times.
Our major professors will just
bring us something and say,
"Hey, see if you can do this.
See if you can get the same kind
of results."
And then another aspect of
quality science or a truth about
science, I guess, is that there
are no absolute truths because
again as we're going into areas
where no one has ever
investigated before, there's no
one to say, yeah, that' right.
So it becomes the collective
intellect that really helps us
determine the truth and the
repeated testing that helps us
determine that truth.
So as we look at the public's
skepticism of science and I
think there are a lot of reasons
for this and certainly one of
those is kind of a lack of
understanding of the scientific
process.
I think that, again, that is a
very important idea.
Just understand the process so
you'll know why scientists think
what they think and again, for
some people, they get frustrated
by the fact that scientists
sometimes change their minds.
But again, that is good science
in that if you get new
information, you always have to
go back and reassess your
hypothesis, your idea.
Also, I think in the public's
mind it's being recent-- that a
recent conclusion or if it's
logical that that is good
science.
But that's not always good
science.
Again, you may have a good idea,
maybe a recent idea, maybe
logical, but you have to put it
to a test.
Also, I think in the public's
mind that being open to ideas
means that you accept ideas
and-- and scientists are open to
challenges constantly but we
have to put our ideas to a test.
So we cannot just a priori
accept ideas without that test.
And then of course, certainly,
in the public there is a clash
of religion and science and so,
again, it's nice to want to
integrate the two but really,
faith and science are completely
different things but it doesn't
mean that a single individual
can't practice both.
So, I hope you'll be open then
to the importance of science in
your lives 'cause we need all of
the great minds that we can get.
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