Skip to ContentGo to accessibility pageKeyboard shortcuts menu
OpenStax Logo
Biology for AP® Courses

Science Practice Challenge Questions

Biology for AP® CoursesScience Practice Challenge Questions


Cells in different tissues of a fully developed human show significant variations in the length of time that they remain in the G0 phase of the cell cycle: muscle (lifetime), nerve (lifetime), adipose (years), liver (year), erythrocyte (months), bone osteoclasts (weeks), leukocyte (days), and epidermal (hours). For each of these types of tissues, propose a reason based on internal and external factors and function that might account for the differences among their longevities.


Describe the essential components and results of mitosis and the activities that occur during interphase to prepare the cell for mitosis.


Cancer comprises many different diseases with a common cause: uncontrolled cell growth. Cancer is a complex response to a host of environmental mutagens as well as the accumulation of random mutations. Since the “war on cancer” began in 1971, the death rate due to cancer has changed very little despite the discovery of several tumor suppressor genes, including p53.

  1. Briefly describe the multiple functions of p53, including the role of p53 in apoptosis.
  2. A principle of biology is that “form follows function.” The protein p53, which has multiple functions, is named for its molecular mass—approximately 53 kDa. This is not a large polymer by comparison with other proteins; for example, ATP synthase, which has only one function, has a molecular mass of approximately 550 kDa. Based on analogies to processes involved in cellular signaling, create a model(s) to explain how so many functions can be supported by a single, relatively simple structure.
    The figure is titled Mutational Signatures of P53 for Three Types of Cancer. There are three pie charts. The key on the bottom reads Green for G:C arrow T: A, peach for G: C arrow A:T, purple for G:C arrow C:G, red  for A:T arrow  C:G, light blue for A:T arrow G:C, gray for A:T arrow T:A, and aqua for Deletion/Insertion. The pie chart on the left is titled nonsmoker’s lungs and has the following values: 11% purple, 12% green, 12% aqua, 5% red, 10% light blue, 4% gray, and 47% peach. The second pie chart is labeled smoker’s lungs, and has the following values: 11% purple, 20% green, 12% aqua, 4% red, 10% light blue, 4% gray, 29% peach. The third pie chart is labeled Breast and Colorectal cancer and has the following values: 6% purple, 9% green, 13% aqua, 3% red, 11% light blue, 4% gray, 54% peach.
    Figure 10.17
  3. Mutational signatures of p53 are shown in the figure above [G.P. Pfeifer et al., Nature, 21(48), 2002] for the three types of cancer with the highest death rates in the U.S.: lung (~225,000 deaths in 2016), breast (246,000), and colorectal (381,000). These data can be obtained by sequencing the gene that encodes p53. Approximately 85% of lung cancers occur in smokers. Based on these data, calculate how many deaths due to lung cancer among nonsmokers were reported in 2016. How much does smoking increase the likelihood of death due to lung cancer?
  4. As shown under each graph, particular transversions (replacement of a pyrimidine by a purine of vice versa) or transitions (replacement of a purine or pyrimidine by the alternative purine or pyrimidine) are features of specific mutational signatures. Based on these data, identify the transversion or transition that seems to be induced by cigarette smoke.
  5. Using your answer to B above, predict possible mechanisms, that is, transversion or transition, for the different mutational signatures among lung cancers of smokers and those of other cancers, and for the very similar mutational signatures of lung cancers of nonsmokers and of breast and colorectal cancers. The partitioning of function along the length of the protein can lead to functional and nonfunctional segments. It is believed that the transversions due to smoking are caused by polyaromatic hydrocarbons. The hotspots for these mutations lie in the segment that binds to DNA. The transition hotspots are in segments that regulate apoptosis.

Many biological processes are synchronized with the 24-hour rotational period of Earth. Circadian (24-hour) periodicity is common across phyla. One of these processes is the cell cycle. The currently accepted explanation is that the low-oxygen atmosphere of early Earth had no ozone layer to filter out the solar ultraviolet radiation that damages DNA. Completing the S phase of the cell cycle at night provided a selective advantage. The internal clock controlling the cell cycle and the circadian clock became synchronized. Research has demonstrated that changes in one clock, either the circadian clock or the cell cycle clock, disrupt timing in the other. The question was, which clock controls the other?

Researchers have found that the circadian clock, which can be observed by fluorescent markers on proteins that carry the circadian signal, can be disrupted by changes in light, nutrition, or exposure to the steroid dexamethasone. Nutrition can also disrupt the cell cycle clock. Rat fibroblasts (cells constantly undergoing mitosis) were cultured on medium containing different levels of fetal bovine serum (FBS) with and without the addition of dexamethasone. Confluence is a phenomenon that occurs in tissue culture when the surface of the growth medium becomes covered with cells, and the cells stop dividing. The circadian and cell cycle periods were measured.

FBS Dexa-
a 0% no no 24±
b 10% no no 21.9±
c 15% no no 19.4±
d 10% yes no 24.2±
e *20% yes no 21.25±
f 20% yes no 29±
g 10% yes yes 24±
Table 10.5 * Subsets of samples with 20% FBS and dexamethasone were clustered around two means for each measured period.

A. Based on these data, describe the connections between the circadian period and the cell cycle period for each of the experimental conditions.

B. Based on these data, justify the claim that in cells that are actively dividing, the circadian period is set by the cell cycle period rather than the reverse.

Order a print copy

As an Amazon Associate we earn from qualifying purchases.


This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Attribution information
  • If you are redistributing all or part of this book in a print format, then you must include on every physical page the following attribution:
    Access for free at
  • If you are redistributing all or part of this book in a digital format, then you must include on every digital page view the following attribution:
    Access for free at
Citation information

© Apr 26, 2024 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.