Jenelle Wallace, Megan Elmore, Nicole Zu


As amateur (but enthusiastic) bakers, we decided on the idea of modeling yeast growth and fermentation. After a bit of research, we decided that the idea was much more complicated than we originally predicted. We knew that yeast undergo anaerobic respiration and produce carbon dioxide as a byproduct, so we figured that we would measure this with a CO2 sensor. At first, however, we thought that too many variables were involved—the rate of metabolism, the rate of yeast reproduction, the temperature, and the concentration of glucose. We struggled with this for awhile, thinking we might need to figure out a way to measure glucose (we considered buying a glucose monitor for diabetics) and wondering how to measure the rate of population growth for our yeast cells. Luckily, after discussing the problem at length, we realized that our thinking was too broad—since we were planning to use Active Dry yeast for the test, we were really only concerned with the period of time in which the yeast cells were reawakening from dormancy. With a little more research, we found that we could discount population growth, since yeast cells typically only double about every hour and a half (see for reference). We also realized that yeast undergo two phases when becoming activated—the first phase involves a fast increase in metabolic rate and the second involves the synthesis of relevant enzymes and is much slower (Source: We decided to focus on the first phase and model carbon dioxide production only as a function of metabolic rate, assuming that all respiration was anaerobic.

In the end, the process of narrowing down exactly what we wanted to model was more difficult than expected, but the need for making simplifying assumptions, at least at first, when modeling a biological system was a good lesson.

Initially, writing the NetLogo code for our program was not too difficult. We decided that we wanted to write a program to predict the CO2 concentration in a bottle containing blooming yeast based on the metabolic rate of the yeast cells. In order to do this, the program would have to perform a linear regression once in every specified time perio

d, and then adjust the model’s value of the metabolic rate to fit with the real-world conditions. Megan completed the code for the regression while Jenelle wrote methods to set up our yeast-in-a-jar model.

Meanwhile, Nicole applied her superior electrical engineering skills to the problem of how to connect the PASCO CO2 sensor to the GoGo board and thus feed the data into NetLogo. Unfortunately, this turned out to be by far the most difficult part of the project. We figured out how the CO2 sensor worked by using it with the PASCO interface, but we had major difficulties connecting it to the GoGo board. First, wetook apart one of the PASCO connectors and tried to solder the wires onto pins that could be plugged into the GoGo board. This failed miserably. With Marcelo’s help and a little research of our own, we realized that because the CO2 sensor is not a simple resistor like many of the other sensors, we needed to have the analog input feed directly into the GoGo board. This meant removing the connection to the 33K reference resistor that is embedded in the GoGo board ports (the figure at right is from the GoGo board manual and shows the circuit setup)—we had to take the cap off of one of the components next to the sensor port.

Unfortunately, this epiphany was still not enough to get the sensor working. After extensive testing and frustration with the multim

eter, ELVIS adaptor, power source, and breadboard, we figured out that the problem was that no power was going through the sensor. With Jimmy and Paolo’s


help (Paolo actually called a friend who worked for PASCO to get some advice), we figured out that the sensor needed +12 V, – 12 V, and 5 V power sources simultaneously. Finally, when we connected the sensor to the GoGo board, we got meaningful output!!!

The model was not finished yet, however. We realized that the sensor was noisier than we expected, so we had to write some extra code to average out the readings over short time periods so that the readings wouldn’t be too jumpy. The final model shows graphs of the metabolic rate (rate of CO2 production) and the total amount of CO2 present over time.

Our model could potentially serve a role in many simple biology experiments. Students could measure the effe


cts of changing different variables (such as glucose concentration and temperature of the water) on the rate of yeast metabolism. Experiments with variablesaffecting plant growth and photosynthesis could also be performed, with our bifocal model changing values to accommodate various conditions.





Watch our video explanation of how the model works: