Lesson 2 – Growth and Phenotypic Response of Halobacterium in Different Environmental Conditions

Benchwork, experimentation in the lab

3 class periods, including AT LEAST 72 hours for incubation

The exploration of the genetic control of networks begins as students go into the laboratory to test how gene expression in Halobacterium changes in response to environmental conditions.  Surprisingly, students observe that the cells, typically pink in color, experience a phenotypic change when grown in the presence of a full spectrum light bulb and when grown in the absence of all light.  The cells grown in dark conditions turn nearly white, whereas cells grown in the presence of light turn a deep purple color.

Students also notice that the amount of cell growth increases when a full-spectrum light bulb is used.  After learning that bacteriorhodopsin (bR), a protein found in the Halobacterium membrane, is purple, students begin to suspect that the genes for bR might be turned on in the presence of light and turned off in the absence of light.  After gathering and graphing all of the class data using Excel, they induce that as the amount of light increases, so does the amount of bR. However, they find they do not have enough information to verify this, which sends them in search of more evidence to answer the question, “How might Halobacterium cells change the amount of bR in response to light?”

 Lesson 3 – Data Analysis to Propose Network Function

Collaboration with other experts to thoroughly analyze multiple types of available, pertinent data.

2 class periods

Networks of genes and proteins allow cells to change gene expression in response to their environment.  Just as scientists use this premise to infer or build models of cellular networks by analyzing and integrating multiple types of data, the students likewise use actual data to piece together a network in order to answer their question discovered through experimentation.  Each student analyzes one of three types of data using given background information, actual homology, metabolic or microarray data, and a worksheet to guide their analysis.  The student then joins a pre-assigned group to teach each other about their data and information. Together the group uses all of their new understandings to pencil in a possible bacteriorhodopsin network.  Because this would not be possible with only one type of data, this displays the significance of compiling multiple data sets from various experimental techniques when suggesting network relationships.  The data also gives the students the opportunity to look past complex names to see molecules as items that simply interact.  They represent these molecules as symbols, or nodes, in a network and begin to visualize how a set of proteins and genes function as a unit to give rise to a set of properties within a cell.  Specifically, they learn about growth and energy production in response to changing the environmental conditions of light and oxygen; in general, they learn about systems concepts along with the importance of both gene regulation and surviving in a variety of environments.

One of the beginning networks after looking at only microarray data.

Final, class compiled bR network.

 Lesson 4 – Analysis of Laboratory Results to Verify Network Interactions

Scientists combine previous data with their new data in order to evaluate their proposed network function. 

3 periods

Once the students begin to understand the make up of their bR network as well as the negative and positive relationships within the network, they realize the network in this stage is actually a hypothesis. They consider how to test this model, which brings them back to the lab to revisit their initial question of, “How might Halobacterium cells change the amount of bR in response to light?”  Students test the validity of their models/hypotheses by comparing predicted phenotypes of genetically manipulated strains with observed phenotypes. Their predictions come from analyzing their proposed network to guess what phenotypes would be present when different routes within their network are active.  For instance, if the model is correct, then when the bat gene is knocked out, the cells should look a certain way.  The students actually spin down cells of varying strains, with known genotypes, to view the cultures’ specific phenotypes.  To bring this back to their original question, they gather the cell pellets they created when growing wild type Halobacterium in different conditions (including light and dark conditions) and compare them to the different mutant strains.

By matching the phenotypes of the experimental and known cell pellets using a color chart, they can infer what network interactions are currently at play.  This information is used to answer their question and verifies how a certain environmental condition affects gene expression and the cellular network.  Students further their understanding by manipulating a computer simulation built by scientists in the Baliga Lab to display the actual bR network.