Question 150·Hard·Nonlinear Functions
In a sealed Petri dish, a bacterial population grows toward a maximum carrying capacity of 12,000 cells. At the moment the population is first observed, there are 3,000 cells, and 2 hours later there are 4,800 cells.
Assuming the population follows a logistic growth model, which function best models the number of cells , hours after the initial observation?
For logistic growth questions, start by recalling the standard form , where is the carrying capacity. Immediately set equal to the given carrying capacity to limit which choices are possible, then use the initial value (often at ) to solve for or to quickly eliminate options that give the wrong starting population. Use a second data point to solve for algebraically or, if answer choices are given, plug that time into the remaining models and see which one matches the data. Finally, check that the sign of the exponent makes sense for growth toward the carrying capacity (the term with in the denominator should shrink over time so that approaches ).
Hints
Use the logistic model structure
Recall that a logistic growth model with carrying capacity usually has the form . Which choices look like this, and which one is just simple exponential growth?
Use the initial value at t = 0
Plug into the general logistic form . The population is 3,000 at that time. What equation do you get for , and what does that tell you about the constant in the denominator?
Use the value at t = 2 hours
Once you know the constant in the denominator, plug and into that form to solve for the growth rate , or plug directly into the remaining answer choices to see which one gives 4,800.
Think about the sign of the exponent
In a logistic growth model, as time increases, the population approaches the carrying capacity from below. Should or appear in the denominator so that the denominator approaches 1 and the population approaches 12,000?
Desmos Guide
Enter each candidate model
In Desmos, use in place of . Type each answer choice as a separate function, for example: , , , and .
Check the initial value at t = 0
For each graph, tap on the point where (or add a table for each function and include ). Note the -values; only the model(s) that give at are consistent with the initial population.
Check the value at t = 2
For the remaining candidate(s), tap on (or include in the table) and read off the -values. The correct model will give a value close to 4,800 at .
Confirm long-term behavior
Zoom out horizontally to see what happens as gets large. The correct logistic model should level off near , while an incorrect model may either grow without bound or approach a different limiting value.
Step-by-step Explanation
Write the logistic form and use the initial population
A standard logistic growth model with carrying capacity has the form
Here the carrying capacity is 12,000 cells, so :
At the initial observation time , the population is .
Substitute and :
Solve for :
So the model must look like
Any correct choice must have numerator 12000 and denominator for some positive . This already rules out any option with a different constant (like ) in the denominator.
Use the second data point to solve for the growth rate
We also know that after 2 hours, there are 4,800 cells: .
Using the partially determined model
substitute and :
Solve for :
Take the natural log of both sides:
So the exponent in the logistic model is approximately .
Write the full model and match it to an answer choice
Now substitute and back into the logistic form:
This function starts at 3,000 cells when , reaches about 4,800 cells at , and approaches a maximum of 12,000 cells as becomes large. Among the answer choices, this expression matches option D, so the correct answer is