Summarize
How was the development of cell theory
26.
Analyze
What structural features suggest that eukary-
27.
Synthesize
If vesicles are almost constantly pinching off
from the ER to carry proteins to the Golgi apparatus,
why does the ER not shrink and finally disappear?
28.
Compare and Contrast
You know that both vesicles
and vacuoles are hollow compartments used for storage.
How do they differ in function?
29.
Infer
When cells release ligands, they are sent through
the bloodstream to every area of the body. Why do you
think that only certain types of cells will respond to a
particular ligand?
30.
Provide Examples
What are two ways in which exocy-
tosis might help a cell maintain homeostasis?
4B
31.
Compare
How is facilitated diffusion similar to both
65
2752
4
38
1989
Interpreting Visuals
Use the diagram to answer the next three questions.
Apply
If the independent variable in this study is age,
what is the operational definition of the dependent
variable?
36.
Analyze
What do the data show about the relationship
between age and number of mitochondria?
37.
Infer
What might the relationship between age and
number of mitochondria indicate about the increase in
ROS levels?
32.
Apply
What process is occurring in the diagram,
and how do you know?
4B
33.
Predict
If the transport proteins that carry amino acids
into this cell stopped working, how might the process
shown be affected?
4B
34.
Infer
What might you conclude about the membrane
structure of the final vesicle and the cell membrane?
Write an Analogy
The cell membrane regulates what
can enter and exit a cell. In eukaryotes, it encloses a
complex group of organelles that carry out special jobs.
Make an analogy to describe the cell membrane and the
variety of organelles and processes that take place inside
it. Explain any limitations of your analogy.
3E, 4B
39.
Connect
On the chapter opener, you saw a picture of
macrophages eating up bacteria. Identify the ways in
which the cytoskeleton helps the macrophage carry out
this job.
Analyzing Data
Form an Operational Definition
35.
Apply
If the independent variable in this study is age,
what is the operational definition of the dependent
variable?
36.
Analyze
What do the data show about the relationship
between age and number of mitochondria?
37.
Infer
What might the relationship between age and
number of mitochondria indicate about the increase in
ROS levels?
Making Connections
38.
Write an Analogy
The cell membrane regulates what
can enter and exit a cell. In eukaryotes, it encloses a
complex group of organelles that carry out special jobs.
Make an analogy to describe the cell membrane and the
variety of organelles and processes that take place inside
it. Explain any limitations of your analogy.
3E, 4B
39.
Connect
On the chapter opener, you saw a picture of
macrophages eating up bacteria. Identify the ways in
which the cytoskeleton helps the macrophage carry out
this job.
Patient
Age
Mitochondria per Muscle Cell
1
47
2026
2
89
2987
3
65
2752
4
38
1989
94
Unit 2:
Cells The science of cell biology began in the seventeenth century with the discovery of cells by Hooke and van Leuwenhoek. This discovery came shortly after, and indeed it required, one of the most important single technological innovations in seventeenth century physical science, namely, the development of practical vision-enhancing instruments based on the refractive properties of glass. The development of telescopes drove discovery and understanding in astronomy; it is significant that Galileo was an active developer of telescopes as well as an astronomer. Similarly the development of microscopes drove, somewhat later in the same century, the discovery of cells; Hooke and van Leuwenhoek, like Galileo, were hands-on developers of their instruments. Thus, the studies that resulted in the secure understanding, first clearly set forth by Schwann in the early nineteenth century, that cells are the basic units of all life, began with a reduction to practice of some early ideas about physical optics. It is my thesis that, like the discovery of cells, most major subsequent developments in cell biology continue to be driven by technological innovations and improvements whose origins lie in diverse and intellectually distant areas of science. This continuing relationship between technology and discovery means that cell biologists in the next 50 years will have to be conversant with the fundamental concepts over a broad intellectual landscape ranging from physics through chemistry to genetics, but especially with the mathematical and computational ideas and methods that are dominating technology development. This is a particular challenge for education because the quantitative skills of most of our current students are underdeveloped, leaving them ill-equipped to deal with the technologies that will drive innovation in their scientific lifetimes. A few twentieth century examples should suffice to illustrate how closely progress in cell biology continues to be tied to technological development of discoveries and ideas quite far afield.
These fantastic advances were made with analog technologies resulting in images that were recorded as photographic images and analyzed mainly by inspection. Increasingly, technology advances in chemistry, imaging, biochemistry, genetics, and indeed in cell biology rely on quantitative and computational methods and analysis. Many, if not quite all, of the great advances and opportunities in the future will involve a mixture of advances in hardware and software, with more and more of the effort in the latter category. A few examples illustrate these trends: Digital image capture and computation methods introduced already ≥20 years ago have made possible reconstruction of cellular structures in three dimensions from stacks of images obtained from optical and electron microscopes. Also, the diffraction limit of optical resolution by light microscopy has been exceeded by diverse but related methods that use fast digital image capture and computation to localize fluorescent molecules to a resolution of ca. 10 nm in three dimensions (Betzig et al., 2006 ; Rust et al., 2006 ; Baddeley et al., 2007 ). Single-molecule and single-cell imaging has been made practical with the result that the variability among apparently identical cells can be studied in situ. Such studies have already resulted in discoveries about the role of noise in gene expression (Elowitz et al., 2002 ). The introduction of laser technology not only for illumination but also for measuring forces has allowed the study of very basic issues in cell biology, such as the nature and magnitude of forces during muscle contraction (reviewed in Tyska and Warshaw, 2002 ). Close adjacency of molecules in vivo can be detected and measured by fluorescence resonance energy transfer among suitable engineered GFP variants (reviewed in Pollok and Heim, 1999 ). Genome-scale technologies (DNA microarrays, comparative genome hybridization, genome-wide gene knockouts, or RNA inference knockdowns, morphometrics) require sophisticated statistical analysis for thorough and rigorous interpretation. Quantitative and computational analysis is no longer optional for cell biologists: obtaining insight by simply looking at images is becoming less and less common. As resolution becomes better, signals tend to become weaker relative to the noise, often requiring considerable statistical and quantitative analysis even when the measurements can be made in commercially available instruments. It is unlikely that the cell biologists of the future can function effectively with just the 1 year of undergraduate physics and 1 year of undergraduate calculus required of Ph.D. candidates in most cell biology graduate programs. If major progress in the future is not to be limited to just a few of our students, we should act now to expect more quantitative thinking, and to provide more quantitative and computational content in our curricula.
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