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# Worked example: Logistic model word problem

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FUN‑7 (EU)
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## Video transcript

- [Narrator] The population P of T of bacteria in a petry dish satisfies the logistic differential equation. The rate of change of population with respect to time is equal to two times the population times the difference between six and the population divided by 8000, where T is measured in hours and the initial population is 700 bacteria. What is the carrying capacity of the population; and what is the population's size when it's growing the fastest? So in order to even attempt to answer these questions at any point if you're inspired, definitely pause the video and try to answer it by yourself. Let's just remind ourselves what we're talking about or what they're talking about with the logistic differential equation and the carrying capacity. So in general a logistic differential equation is one where we seeing the rate of change of, and it's often referring to population, so let's just stick with population. So the rate of change of our population with respect to time is proportional to the product of the population and the difference between what's known as the carrying capacity and the population. Now why is this a model that you'll see a lot, especially why is it useful for studying things like populations. When a population is small the environment really isn't limiting it and so assuming it starts from some none zero value, this thing grows, this thing is not going to get much smaller and so our population is going to a rate of change is going to increase. And so let me just draw a little graph here to show the typical solution to a logistic differential equation. So this is our population, this is time, so when our population is low, let's say it's gonna start from some non zero value, if it was zero what would happen, well then our rate of change would just be zero and our population would never grow and that makes sense. If you have no bunnies on your island then there never will be any bunnies on your island but if you have a few bunnies well initially, their rate of change, the rate of population is gonna keep accelerating as this thing grows, it's gonna keep accelerating but then at some point your environment is going to limit how many bunnies for example or bacteria can grow in your environment. Because once the population gets close to A, this thing right over here is going to approach zero and its going to make our rate of change smaller and smaller and smaller. And so you can imagine in the limiting case as P gets very close to A, you can imagine as T approaches infinity, our rate of change is going to approach zero. So one way to think about it is, our population would acemtote towards the carrying capacity. That is A right over here, that is our carrying capacity. So there's a couple of ways of answering this first question; one way is we can actually put our logistic differential equation in this form and then we can recognize what the carrying capacity is. The other way is to think about, well what happens as T approaches infinity. As T approaches infinity, this thing approaches zero and so we can think from this logistic differential equation what P values would make this thing be zero based on this differential equation or when this thing approaches zero, what P values would this approach. So let's do it both ways. So one way, let me write it in this form right over here, so it's close, this is six minus P over 8000, can we make it in the form where we just have some number minus P. Well if we multiply this times 8000 and then we divided this by 8000 we wouldn't be changing the value of that expression so let's do that. If we divide this by 8000 you have DP DT is equal to 2P over 8000 is P over 4000 times and now let's multiply this expression by 8000. So six times 8000 is 48,000 minus P over 8000 times 8000 is minus P and there you have it. We've written it down to somewhat classic form and we can see that the carrying capacity is 48,000, I guess we say 48,000 bacteria. So this thing right over here is 48,000. Another way we could think about it is, well the carrying capacity is what happens as T approaches infinity. So as T approaches infinity, this thing right over here approaches zero, you can see the rate of change approaches zero. So when that approaches zero what does P approach? So we can just solve for this; six minus P over 8000. Well there's two situations, two Ps for which our derivative is equal to zero. There's one case when this is equal to zero, in which case our population is to zero or the other case is when this is equal to zero. So let's do that; six minus P over 8000 is equal to zero or we could say P over 8000 is equal to six and so we can say multiply both sides by 8000, P is equal to 48000. Which is exactly what we had right over there. So now let's answer the second part. What is the population's size when it's growing the fastest? So intuitively you can see when that is right over here, the rate of change is gonna grow but then as we approach the carrying capacity, the rate of change is gonna start slowing down. So your maximum rate of change, it's growing the fastest, is right about there but how do we figure out it exactly. Well you could go back to the logistic differential equation. You can see that it's really our rate of change is a function, you could view it as a function of our population right over here. And this is actually a quadratic expression, this is a concave downward quadratic expression, it would look like this, if you were graphing rate of change, let me do that, if you were to graph rate of change, DP DT as a function of population, well when the population is small, so when the population is, say 700 or that's where we're starting. I'll just speak in generalities, when your population is small DPT, your rate is small but then it increases and then some point around there the rate of change starts to decrease and it approaches zero as our population is approaching the carrying capacity. So this right over here for example, would actually be our carrying capacity. What is this maximum point right over here and there's a couple of ways you could approach it with calculus and even algebra, we have many tools for identifying this maximum point which is really just a vertex of this downward opening, this concave downward parabola. So let's just do that and let's find this vertex and the vertex is just half way between the zeros of this quadratic. So let's find the P values that make this equal to zero which we actually just figured out, the vertex is just gonna be half way between that. So when the population is zero our rate of change is zero when the population is A, which we know is 48,000, our rate of change is zero and so our maximum rate of change is gonna happen half way between those two points which is 24,000 So it is a population of 24,000. When we got that by really just saying well at one point does this quadratic, quadratic is a function of P, when does that hit a maximum point. Well that's going to be half way between the zeros, the zeros happen when P equals zero and P is equal to 48,000 so that's gonna happen when the population is 24,000. So this type of problem seems very intimidating at first, logistic differential equation, how do I actually solve this and then analyze it but the key is to; one, recognize a logistic differential equation, see what it's talking about and then maybe think of this in terms of our rate of change as a function of our population. And remember that the carrying capacity is what happens as T approaches infinity and as T approaches infinity our rate of change approaches zero. And so if that's approaching zero, what must our population be approaching.
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