biotechnology

biotechnology involves the use of living systems, organisms, or parts of organisms to manipulate natural processes in order to develop products, systems, or environments to benefit people (could make products like foods, pharmaceuticals, or compost; systems like waste management or water purification; or environments like hydroponics); also includes genetic or biomedical engineering

includes (not limited to)

• manipulating DNA

• recombinant DNA technology

• PCR & cloning

• fermentation

• production or recombinant proteins (insulin)

genetic manipulation is not new in organisms

• has been done for thousands of years

  • plant and animal breeding

mainly used in multiple fields of research

• biology, chemistry, genetics, physics

• research labs

• pharmaceutical products

• agricultural products

• medical instruments

products created using biotechnology

• insulin for diabetes

  • 1982 Eli Lilly (humulin)

• proteases

  • many applications (stain removal from clothing)

• antibodies

  • fighting diseases

• recombinant plants

  • disease resistance

  • cold/drought resistance

• genetically modified animal cels

  • stem cells

  • animals making human proteins

research takes place at biotechnology companies

• For-profit

• Develop a wide variety of products

  • Pharmaceutical, agricultural, industrial, research instruments, reagents

• Provide services to other researchers

  • DNA sequencing facilities

• Employ many types of workers

  • Laboratory, manufacturing, business-related

• Patent their findings

• University and Government Research Labs

  • Not “for-profit”

  • Often “Pure science” = meant to further knowledge in the field

  • Some “applied science” = use science for practical purpose

  • Many collaborate to look at large problems

    • Viruses (HIV), Cancer

  • Funded by granting from:

    • Private industry, government, foundations (ex. Cancer foundations)

  • Share their findings

examples of bioengineered products

• Products used in rDNA technology

  • DNA ligase, restriction enzymes, Taq polymerase, vaccines (mRNA covid vaccine)

• Genetically Modified Organisms (GMOs)

• Recombinant human proteins

  • Human gene spliced into another organism, using a vector

    • E. coli, yeast, other mammals

  • Produce large amounts of human protein

Example: t-PA (tissue plasminogen activator)

• Protein involved in breaking down blood clots and clearing clogged blood vessels

Human insulin

• Treatment of Type I and II diabetes

bioethics in biotechnology

• Many of the products and treatments developed using biotechnology are controversial

  • Force debates about morals, right and wrong

  • GMO’s, stem cells, manipulating the human genome, genetic testing, “designer genes”

  • Bioethics focuses on the debates regarding what is moral

  • Government agencies help to regulate the types of research being done

  • Important to understand the science behind the products to be able to form your OWN opinion about the ethics of a product

• Most researchers are not involved in setting science policy

• Ethics also comes into play in the research setting

  • Honesty and Accuracy in Data collection and reporting

  • Safety

  • Where do you draw the moral line for your job?

scientific method

observation - using senses to gather information

scientific method - process used to answer questions about the world

problem/question - created off of observation

hypothesis - testable explanation of a question/problem

experiment - procedure that tests the hypothesis

charts/tables/graphs - used to organize data gathered during experiment

• line graph - quantitative (numerical) data

• bar graphs - qualitative (descriptive) data

conclusion - explains if the hypothesis was supported or not

constant - parts of the experiment that stay the same

variables - factors that could change in an experiment

independent variable - part being manipulated to test for any effects

dependent variable - part being measured; changes based on the independent variable’s effect

control group - experimental group that isn’t affected by independent variable; used for comparison

scientific method

• ask a question based off of observations, information, research, etc.

  • do background research

• construct a testable hypothesis

  • attempts to answer a question

  • written as a statement

  • hypothesis doesn’t get proven, only supported or rejected

• test hypothesis by doing an experiment

  • quantitative (numerical) data when possible

  • repeat at least 3 times

  • control groups

    • positive control - gives predictable results

    • negative control - lacks what is being tested, gives negative result

  • independent variables - conditions you manipulate (can be more than 1)

  • dependent variables - conditions you measure (can be more than 1)

  • controlled variables/constants - conditions you don’t change

• analyze data and make a conclusion → state if prediction was confirmed or not (can’t prove hypothesis)

  • based on observations &analysis of data

  • includes consideration of error

  • suggest possible ideas for further experimentation/applications of your findings

• report results

try to be accurate about measurements, timing, data collection

make careful observations

• photograph data?

• keep samples (if allowed)?

organize data

• spreadsheets, graphs, tables, etc.

forming a conclusion

does the numerical evidence support the original hypothesis?

look for trends in data among test groups

• what if one of the tests is very different from others?

• is there a significant difference between 2 different groups?

• determine “best” condition and make recommendation

• is further experimentation required?

• what could be done differently?

hypothesis - inferred explanation of an observation/research finding; based on existing scientific knowledge

theory - well-substantiated and comprehensive set of ideas that explains a phenomenon in nature; based on large amounts of data and observations collected over time; can be tested and refined through additional research and allow scientists to make predictions

scientific law - an expression of a mathematical or descriptive relationship observed in nature

1. what is the best definition of the term theory, as it is used in science?

   1. a theory is a guess or hunch about something that has occurred in nature.

   2. a theory is a comprehensive set of ideas explaining a phenomenon in nature

   3. a theory is based on verifiable laws and can be proven true

   4. a theory is a hypothesis that uses laws and observation to make an assumption

2. while speaking to a colleague, a scientist makes the following statement: “i propose that bald eagle eggs in northern maine will have thinner shells than those from birds in southern alaska due to increased levels of pesticides in the water. “ this statement is a

   1. theory

   2. law

   3. conclusion

   4. hypothesis

3. scientfic theories can be tested

   1. true

   2. false

4. complete the following sentence so that it is correct: scientific theories are based on

   1. general assumptions of how systems work

   2. mathematical principles that can be proven true

   3. large amounts of data collected over time

   4. observations from within only one scientific discipline

5. why are scientific theories an important part of research and understanding?

   1. they allow scientists to make predictions

   2. they allow scientists to make assumptions

   3. they determine the work that future scientists can do

   4. they determine the subjects that scientists research

6. scientific theories do not change once they have been written down

   1. true

   2. false

7. which statement below correctly identifies the difference between laws and theories?

   1. laws describe phenomena, while theories explain why phenomena exist

   2. laws are a statement of fact, while theories are a statement of opinion

   3. laws explain why phenomena exist, while theories explain how

   4. laws are a prediction of phenomena, while theories are an explanation

8. why do scientist develop a hypothesis before conducting research?

   1. it gives them direction on how to interpret the results of their research

   2. it helps to predict outcomes and define the parameters of the research

   3. hypotheses give the researcher an outcome to shape their work around.

   4. hypothesis help a researcher decide which observation to record and ignore.

9. scientific theories are always broad and cover many concepts

   1. true

   2. false

10. choose the sentence below that is a correct description of scientific theory

    1. scientific theories in one discipline can influence theories in other disciplines

    2. theories in one scientific discipline do not affect theories in other disciplines

    3. new scientific theories are always original and do not connect to those that came before

    4. creativity and insight are not important parts of developing new scientific theories

1. b

2. d

3. a

4. c

5. a

6. b

7. a

8. b

9. a

10. a

measurement stuff

Metric Conversions

The Great Man King Henry’s Daughter Drank Chocolate Milk Until Nine P.m. - tera, giga, mega, kilo, hecto, deca, deci, centi, milli, micro, nano, pico

tera- - T, 10^12

giga- - G, 10^9

mega- - M, 10^6

kilo- - k, 10^3

hecto- - h, 10^2

deca- - da, 10^1

deci- - d, 10^-1

centi- - c, 10^-2

milli- - m, 10^-3

micro- - μ, 10^-6

nano- - n, 10^-9

pico- - p, 10^-12

Microscopes

40x = 2 boxes = 4 mm = 4000 μm

100x = 2 boxes/2.5 = 0.8 boxes = 1.6 mm = 1600 μm

400x = 2 boxes/10 = 0.2 boxes = 0.4 mm = 400 μm

take estimate of sample size (ex. 1/3)

multiply field of vision (ex. 400 μm or 400x) by sample size (1/3) = about 133.3 μm

Volume

graduated cylinder - measures anything greater than 10 mL; types include 10 mL, 25 mL, 100 mL, 250 mL, 500 mL, and 1 L

serological pipette - measures anything less than or equal to 10 mL; types include 1 mL, 2 mL, 5 mL, and 10 mL; used with pumps (blue and green)

micropipette - types include P-10, P-20, P-100, P-200, and P-1000

P-10 - measures 0.5 μL to 10 μL; uses small white tips

P-20 - measures 2 μL to 20 μL; uses small white tips

P-100 - measures 10 μL to 100 μL; uses medium yellow tips

P-200 - measures from 20 μL to 200 μL; uses medium yellow tips

P-1000 - measures from 200 μL to 1000 μL or 1 mL; uses large blue tips

Volume Practice

25 μL - P-100

10 μL - P-10

1 μL - P-10

1000 μL - P-1000

1 mL - P-1000

2 mL - 2 mL serological

175 μL - P-200

23.5 μL - P-100

6.5 mL - 10 mL serological

125 mL - 250 mL graduated cylinder

7 μL - P-10

2.87 mL - 5 mL serological

555μL - P-1000

Mass

standard balance - measures solid mass over 900 mg; accurate up to the hundredth (0.01) of a gram

analytical balance - measures solid mass under 900 mg; accurate up to the thousandth (0.001) of a gram

Mass Practice

2.0 g - standard balance

40 mg - analytical balance

30 g - standard balance

2.006 g - standard balance

3.5 g of salt - standard balance

6.5 mg of DNA - analytical balance

12.5 g of gelatin - standard balance

Remember

1 cm^3 = 1 mL = 1 g

percent error equation - (observed mass - expected mass)/expected mass x 100

also know procedures for all measuring instruments (balances, micropipette, etc.)

Random Practice

find the mass in grams

1000 μL - 1 g

500 μL - 0.0005 g

53 μL - 0.000053 g

13.7 μL - 0.0000137 g

solutions

mass/volume

often in grams/ml

• may use some in mg/ml or µg/ml

• always check/convert units before starting

% mass/volume

different from mass/volume

1% solution will be 1 g in 100 ml

always convert volume units to mls

molar solutions

molarity - unit used to describe the number of moles of a chemical/compounds in one liter (L) of solution, making it a unit of concentration

this means a 1.0 Molar (1.0 M) solution is equivalent to one formula weight (FW = g/mol) of a compound dissolved in 1 liter (1.0 L) or solvent (usually water)

always convert volume units to liters first

• because molarity is expressed as moles per liter (mol/L)

always convert to L

dilution of a concentrated solution

C1V1 = C2V2

C1 - starting concentration

V1 - volume of starting concentration to use in final solution

C2 - desired concentration

V2 - desired final volume

can be g/ml, molar, %, etc. as long as they match

serial dilutions

usually take 1 ml stock and put into 9 ml distilled water to make 10 ml dilute solution

excel graphing

graphs make it easier to see relationships

• between different experiments or different experimental conditions

conclusions that might be missed in a table become apparent when data are in graphic form

variables

variable - something that takes on different values

• ex. Age, height, weight, temperature, absorbance, wavelength, etc.

independent variables - value being manipulated or changed

dependent variables - observed result of the manipulated value

may be quantitative or qualitative

quantitative variable - one that can be measured

• height, weight, number of leaves, absorbance, etc. that must be expressed as a number

qualitative variable - expresses a “qualitative attribute”

• like a category - not measured

  • ex: Type of plant (bean or corn), gender (male or female), political party (Democrat or Republican), etc.

  • qualitative variable can have a name denoted by a number, like laboratory groups or tube numbers (1,2,3,4,5,6) but this does not make it a quantitative variable.

quantitative variables can also be called:

• discrete variable - variable with possible scores of discrete points on the scale

  • home can have three or six children, but not 4.53 children

• continuous variable - where scale is continuous and not made of discrete steps

  • response time could be 1.64 seconds, or it could be 1.64237123922121 seconds.

• practicalities of measurement prevent most measured variables from being truly continuous

graphs

two axes - horizontal and vertical

• horizontal axis - x-axis or abscissa

• vertical axis - y-axis or ordinate

values of the independent variable are measured on the x-axis

values of the dependent variable are on the y-axis

3 types of graphs - bar, scatter, line

• two for the quantitative independent variable

  • line graphs

  • scatter plots

  • difference - how they plot along the x-axis and are trends (scatter) or data point differences (line) being observed

• one for qualitative

  • bar graphs

line graph -  distributes category or value data along the horizontal x-axis and distributes all value data evenly along the vertical y-axis

• Demonstrates relationship between 2 variables

scatterplots - gives a visual display of 2 variables in a data set

• has two value axes showing one set of numerical data on the x-axis and another on the y-axis

bar graph - when independent variable is qualitative and represents separate groups

histogram - type of bar graph

• horizontal scale represents classes of data values

• vertical scale represents frequencies

  • bars drawn without gaps between

• often used by researchers to help them quickly "see" how points in a large data set are distributed over the range of data

• x-axis indicates names of the values of the independent variable

graphing in excel

can be used for statistics and graphing

workbook - new page in Excel

most broad to narrow - tabs, ribbons, groups

cell - individual spot

range - grabbing a bunch of cells

worksheets - inside the workbook

wrap text - displays the text in multiple lines rather than one long line

merge & center - combines cells into one cell

dotted line - boundaries of the printed page

use = to start writing a formula

copying a formula - click the cell and get a square (bottom right corner of a cell), then drag the cell all the way down

autosum - adds all the numbers in the selected range

use arrows to move

• tab - moves right

• shift + tab - moves left

• enter - moves down

• shift + enter - moves up

statistics

measures of center

measures of center - representative or average value that indicates where the middle of the data is located

mean

center of the set of values

found by adding all values and dividing by total number of values

usually the best indicator of the middle, unless a large number of high and low points

more accurately called arithmetic mean

• ∑ = sum of

• N = number of values in a population

• n = number of values in a sample

• x = variable used to represent individual data sets (here each value in sample)

median

if data set contains one outlier, this can dramatically change the mean

median is less sensitive to this effect

median - measure of center that is the middle value when the original data are arranged in order from low to high

finding median

• sort the values - arrange in order

  • if the number of values is odd, the median is the number located in the exact middle

  • if the number of values is even, the median is the mean of the 2 numbers in the middle

mean vs median

mean is dramatically effected by outliers

median is not

• median is good for data sets that include a small number of outliers

• ex. - u.s. census - median household income =$36,078

  • small number of household with very high incomes

mode

mode - value that occurs most frequently

bimodal - when there are 2 values that have the same, greatest frequency

multimodal - when there are more than 2 values that have the same, greatest frequency

midrange

skews

conclusion

no measure of center is best for all data sets - each has advantages and disadvantages

measures of variation

measures of variation - measure of the amount that the data values vary among themselves (variability)

tells how far data points are spread out from the middle

range

range - difference between the max value and min value (high # - low #)

isn’t as helpful as some other measures of variation

standard deviation

standard deviation - measure of variation of values about the mean

• how far do values vary from the center?

1. compute the mean

2. subtract the mean from each individual value to get a list of deviations

3. square each value

4. add all the squares

5. divide by n - 1

6. find square root of the result

importance

• values clustered closely together yield a low standard deviation

  • means data set is very consistent

• lower the standard deviation, the more consistent the data set

conclusion

allows to see how varied the sample is and determine whether a certain value lies within the range of the other “normal” measures

t-tests

t-tests - used to determine if there is a statistically significant difference between 2 data sets

• start making a null hypothesis

  • assuming that there is no difference between the 2 groups

• use Excel to calculate the p-value

  • are probabilities - tell the probability that the data sets are the same

• determine if there is a significant difference

  • p < 0.05 - reject the null hypothesis

    • there is a statistically significant difference

  • p > 0.05 - accept the null hypothesis

    • there is no statistically significant difference

null hypothesis and hypothesis might not be the same thing

• null hypothesis will always be that there is no difference

critical probability is usually 0.05 (5%)

• reflects the facts that biology experiments are expected to produce varied results

• means that there is only a 5% probability that the differences between the two groups are due to chance

if p > 0.05 - two sets are the same; if p < 0.05 - two sets are statistically different.

number of repeats should be as large as possible (> 10 if possible) for the t-test to work

tails

one tail - greater than or less than

• one change in one direction

• movie theater - measure heart rate only after movie

two tails - greater than and less than

• change in two directions

• movie theater - measure heart rate before and after movie

type

type 1 - paired

• same football, different gas

type 3 - independent, small variation, unequal in sample

• different football, different gas

type 2 - independent, large variation, equal (uncommon)

biological literature

biological literature - any printed or electronic document written with the intent of communicating biological information

three types - primary, secondary, tertiary

primary literature

contains original research results reported by scientist

reports the results of research which has never been previously published

authors report their own research

includes new findings discovered by the authors’ research

recognized because it always has “materials & methods” and “data” sections (may not be labeled)

examples

• journal articles

• conference proceedings

• dissertations/theses

• patents

• symposia publications

• research posters

peer review

peer review - before research results are published in a scientific journal, they need to pass a rigorous review process by other scientists

experts in the field of the results examine the results checking for originality accuracy, integrity, etc.

• must-have for primary sources

secondary literature

after a research article is published, the information contained in the article may be summarized and analyzed in books and review articles by other authors

• rather than presenting new info, they provide a compilation or evaluation of previously published research

• not too much analysis

authors will organize previously published literature into a comprehensive view

includes references to primary literature

authors may suggest new conclusions or directions for future research

recognized because it lacks a “Materials and Methods” section

often has a long bibliography and includes data reproduced from the primary literature

intended to summarize the available information and influence the direction of future research

metanalysis - analyzing data about data to bring a new conclusion

examples

• review articles, textbooks, books, and articles that interpret or review research works

• any journal with “review” in the title (ex. Annual Review of Microbiology or The Quarterly Review of Biology)

• other journals that publish review articles (ex. Trends in Cell Biology or Immunology Today)

tertiary literature

aim to provide a broad overview of a topic, or data, already proven facts, and definition, often presented in a convenient form

does not have a “materials & methods” section

examples

• fact books

• guides and handbooks

• digests

• almanacs

• many websites

primary literature extended

anything besides primary

• good places to look for background info

• helps understand a topic better

• gives an overview

• helps identify important ideas & terminology

• gives a list of articles/authors/topics for more information to work with

parts of a primary research article

• title

  • title of article

• by-line

  • authors’ names

• abstract

  • summarizes that article

• introduction

  • presents the research question

  • explains the context of the research

  • discusses previous work that was done on the topic

• materials & methods

  • discusses how the research was conducted

  • explains materials & procedures

• results

  • presents results, often in table and/or chart format

  • shows statistical calculations performed on the data

• discussion/conclusion

  • author explains how the results of their research have addressed their research question

  • suggest further research

• acknowledgments

  • thank yous

• references

  • publications that the author cited

• date of receipt/publication

  • date article is submitted to journal and finally approved

how to read a scientific article

don’t read all at once

• read the abstract

  • gives an overview of the paper

• skim the article

  • look at section headings and any figures/tables, taking time to read captions

• read the entire article, section by section

  • write down questions

  • highlight important concepts

  • focus on the introduction, results, & conclusion