UNIT I – UNITY & DIVERSITY OF LIFE

1. UNIT I – UNITY & DIVERSITY OF LIFE Baby Campbell ~ Ch 1, 17, 24, 25 Big Campbell ~ Ch 1, 19, 27, 28, 31

2. Biology is . . . The Study of Life!

3. I. “THE STUDY OF . . . “ – EXPERIMENTAL DESIGN Inquiry-based

4. I. EXPERIMENTAL DESIGN, cont • Setting up a Controlled Experiment • Clearly defined purpose • Valid hypothesis • Testable statement or prediction—it must be falsifiable • If it’s wrong, then the experiment should reveal it • Do not use “I think …”, “My hypothesis is …”, etc! • Often written in “If …, then …” format but not a requirement. Avoid if possible.

5. I. EXPERIMENTAL DESIGN, cont • Setting up a Controlled Experiment • Control Group • Benchmark or standard for comparison • Allows you to know if what was changed in experimental group is responsible for effect • Positive Controls have an expected positive result—show that your test works • Negative Controls are expected to have no effect—gives you confidence that variable changed is responsible for effect observed • Experimental or Test Group(s) • Only one factor should be changed by the experimenter in each test • Independent (Manipulated) Variable • Dependent (Responding) Variable

6. I. EXPERIMENTAL DESIGN, cont • Important Considerations • Controlled variables (aka control variables, constants) must be monitored • Easy to confuse with controls! • Control = “control group” • Controlled variable = “constant” • Sample Size • Are the results statistically significant? • Potential sources of error • Repeatability

7. A student observed that wrapping thin, insulated wire several times around a nail and attaching the leads to a battery made the nail magnetic. The student hypothesized that increasing the number of wrappings around the nail increases the magnetic strength of the electromagnet. He devised an experiment to test the effects of the number of wrappings on the number of paperclips the nail can pick up. For comparison, he removed the battery from his electromagnet and observed that it did not pick up any paperclips. He also tested a permanent magnet to see if it could pick up paperclips. 34. _______________________________ Dependent variable 35. _______________________________ Independent variable 36. _______________________________ Constant (give one valid example) 37. _______________________________ Control group example 38. _______________________________ Control group example

8. Does Drug X improve student performance? • One group of students given fake pill (placebo) • Several groups given different doses of Drug X. • One group given a drug already known to increase performance. • All groups, after treatment, take a test and are scored. • What is the positive control? Negative control? • Experimental group(s)? • Controlled variables, Ind variable, dep variable?

9. I. EXPERIMENTAL DESIGN, cont • Tables • Format • Descriptive title • Each row a different independent variable • Each column a different dependent variable • Include units in labels • Derived units (e.g. averages, rates, etc.) are put in new columns on the right

10. I. EXPERIMENTAL DESIGN, cont • Graphs • Format • Descriptive title • Key • Units must be evenly spaced (line break) and labeled • Use at least half of available space • Use a ruler!!! • Graphing Dependent and independent variables: • DRY MIX • Continuous Independent Variable (e.g., time) → ____________ Graph • Discrete Independent Variable → _________________ Graph • Other kinds of data: • Part of a Whole → _________________ Graph • Histogram _____________ distribution—how often does this value or group of values occur in a data set?

11. I. EXPERIMENTAL DESIGN, cont • Graphs, cont • For Height Lab … • Mean • Median • Mode • Range • Histogram

12. I. EXPERIMENTAL DESIGN, cont • Graphs, cont • Bar graph versus histogram • Bar graphs and line graphs look at the relationship between two variables (use DRY MIX) • Histograms look at the distribution of values for one variable

13. Factor changed by experimenter; designed to test hypothesis • Type of graph used to represent data when independent variable is continuous; for example, time • Set-up used as a benchmark, standard for comparison • Calculation used to represent spread, variability of data • Variable plotted on X-axis of line graph • Calculation used to determine precision, accuracy of data mean • Factor monitored in experimental design to minimize possibility of error, increase repeatability • Type of graph used to illustrate frequency, patterns of data

14. I. EXPERIMENTAL DESIGN, cont • Graphs, cont • For Height Lab … • Mean = • Median = • Mode = • Range = • Normal Distribution?

15. Histogram vs. Density Distribution Curve

16. I. EXPERIMENTAL DESIGN, cont • Data Analysis • Null Hypothesis • “Statement of No Effect: • States that any differences in data sets are due to random errors that cannot be eliminated in experimental design/protocol • For example, • There are no significant differences between predicted and observed data. • There are no significant differences between control group data and test group data. • Alternate Hypothesis • Statistical Analysis – Supports or refutes null hypothesis: • Answers question, Can we refute the null hypothesis?

17. I. EXPERIMENTAL DESIGN, cont • Standard Deviation • Describes the spread of values in a sample; that is, data variability

18. Mass of Peaches in an Orchard Population size N Mean = 101.2 g 2nd Sampling 1st Sampling Experiment/replicate 1 Sample size n = 10 Sample mean = 103.7 g Experiment/replicate 2 Sample size n = 10 Sample mean = 100.5 g

19. Does your sample mean reflect the true mean of the population? • How can we tell if two samples came from the same or different populations?

20. I. EXPERIMENTAL DESIGN, cont • Standard Error of the Mean • Predicts the accuracy of the calculated samplemean toactual population mean (the “true” mean). • Technically, the SEM is calculated by taking several samples from a population and finding the standard deviation of the means of those samples. • We can estimate the SEM with one sample, however, using the above equation.

21. I. EXPERIMENTAL DESIGN, cont • Standard Error of the Mean • For large values of n (n=10 or more), ± 2 SEM is a 95% confidence interval • That means that there is a 95% chance that the true population mean lies within the error bars. • In order to reject the null hypothesis between two data sets, error bars cannot overlap.

22. I. EXPERIMENTAL DESIGN, cont Examine the data below showing two different experiments in which the heart rate of 10 different individuals was measured in beats/minute. • Calculate the standard deviation for each data set. Round to the nearest whole number. • Null Hypothesis: X barstudy A = 70 X barstudyB = 75 Sstudy A = 4 SstudyB = 12

23. I. EXPERIMENTAL DESIGN, cont • Is there is a significant difference between the average heart beat/minutes in the two data sets? Construct a graph to illustrate. SEMstudy A = 1 SEMstudyB = 4 Sstudy A = 4 SstudyB = 12

24. I. EXPERIMENTAL DESIGN, cont SEMstudy A = 1 SEMstudyB = 4 X barstudy A = 70 X barstudyB = 75

26. I. EXPERIMENTAL DESIGN, cont • Conclusion • Evaluate hypothesis • Was it supported, refuted, or were results inconclusive? • Interpretation of Statistical Analysis • What does the SD, SEM tell you about your results, experimental design? • Assess experimental design • Was there only one independent variable? • Were sources of error minimized? • Controlled variables/constants • Repeatable? • Theory vs Hypothesis

27. II. UNITY OF LIFE • Characteristics of Life • All living things are made of ______. • Prokaryotic • Eukaryotic

28. II. UNITY OF LIFE, cont. • Characteristics of Life, cont • Living things obtain and use energy. • Living things respond to their environment. • Living things grow and develop. • Living things maintain homeostasis. • Living things are based on a universal genetic code. • Living things reproduce. • As a group, living things evolve.

29. II. UNITY OF LIFE, cont. • Four “BIG IDEAS” in AP Biology • The process of evolution drives the diversity and unity of life. • Biological systems utilize free energy and molecular building blocks to grow, to reproduce, and to maintain dynamic homeostasis. • Living systems store, retrieve, transmit, and respond to information essential to life processes. • Biological systems interact, and these systems and their interactions possess complex properties.

30. VIRUSES

32. III. CHALLENGING THE BOUNDARIES OF LIFE • Viruses . . . Living or Non-living? • Discovery of Viruses • First isolated by Ivanowsky in 1890s from infected tobacco leaves • Crystallized by Stanley in 1935 – proved viruses were not cells • Acellular • May be described as particle or virion • Not capable of carrying out life processes without a host cell • Parasites; use host cell’s resources to replicate

33. III. BOUNDARIES, cont • Viruses, cont • Structures found in all viruses: • Viral genome • DNA or RNA. • May be single-stranded or double-stranded • Protein coat • Known as a capsid • Made up of protein subunits called capsomeres.

34. III. BOUNDARIES, cont • Viruses, cont • Structures/adaptations that may be present: • Viral envelope • Typically derived from host cell membrane • Exception is Herpes virus, synthesized from nuclear envelope of host cell • Aid in attachment. Envelope glycoproteins bind to receptor molecules on host cell • Most viruses that infect animals have envelope • Can be described as an evolutionary trade-off • Tail – Found in some viruses to aid in attachment

35. III. BOUNDARIES, cont

37. III. BOUNDARIES, cont • Viral Replication

38. III. BOUNDARIES, cont • Viruses, cont. • Bacteriophage • Infects bacteria • Bacterial Defense Mechanisms • Restriction Enzymes • CRISPR/Cas9 System • Coexistence

39. III. BOUNDARIES, contBacteriophage Replication LYTIC CYCLE 1. Lytic Cycle – Results in death of host cell.

40. III. BOUNDARIES, cont – Bacteriophage Replication LYSOGENIC CYCLE

41. IV. BOUNDARIES, contHuman Viruses

42. III. BOUNDARIES, cont – Human Viruses • Enveloped Viruses vs Non-enveloped Viruses • No envelope • With envelope

43. III. BOUNDARIES, cont – Human Viruses • DNA vs RNA Viruses • DNA Viruses • Herpes Virus

44. III. BOUNDARIES, cont – Human Viruses • DNA vs RNA Viruses • RNA Viruses • HIV

45. III. BOUNDARIES, cont • Prions • “Proteinaceous Infectious Particles” • Infectious proteins; lack nucleic acid • Cause Mad Cow Disease, Creutzfeldt-Jakob Disease • Very long incubation period • No treatment

46. IV. DIVERSITY OF LIFE • Classification • Domain • Kingdom • Phylum • Class • Order • Family • Genus • Species

47. IV. DIVERSITY, cont

48. V. PROKARYOTES

49. V. PROKARYOTES • Archaebacteria • Examples include methanogens, thermoacidophiles, halophiles

50. V. PROKARYOTES – EUBACTERIA • Eubacteria • Ubiquitous • May be pathogenic • Classification • Shape • Cocci • Bacilli • Spirilla