Differential Equations - Logistic Model

In summary, the equation dP/dt = kP(1 - P/A) describes a logistical situation in which the population of a system increases linearly with time, while the carrying capacity decreases. The equation can be solved for P, and the constant C can be found by multiplying the RHS by exp(-kt).
  • #1
NSOutWest
4
0
I have the equation dP/dt = kP(1 - P/A). It is supposed to describe a logistical situatuon involving the carrying capacity of the system.

k is a constant, and A is the carrying capacity of the system. t is time and P is population as a function of time. P(0) = P0. I solved c (the integration constant) to be:

c = -ln|(P0)/(A - P0)|

I'm trying to solve the equation in terms of t.

In my calculus book, a similar equation is given with an explanation.

dP/dt = 0.1P(1 - P/300)

With an initial condition of P(0) = 50, c is found to be ln(1/5). A = 300 and k = 0.1.

I follow along well up to this point.

After solving for c, the book lists the rearranged equation as:

P(t) = 300/(1 + 5e-0.1t)

I don't understand how they went from one equation to the other, the closest I could come with the general equation was:

P(t) = (Aekt + Q)/(1 + ekt) where Q = (P0)/(A - P0)

Which would coincide with an equation of:

(300e0.1t + .2)/(1 + e0.1t)

Which, when graphed, is not equivalent to the equation given by the book.

Can anyone go over how to solve the general equation? I think I'm missing some crucial point.

Thank you!
 
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  • #2
This equation is separable - meaning that you can get all of the terms involving P on one side (including dP), all of the terms involving t on the other (including dt), and integrate both sides.

So we write the equation as

[tex]\left(\frac{1}{P} + \frac{1/A}{1-P/A}\right)\ dP = k\ dt[/tex]

Integrate both sides to get ln|P| - ln|1-P/A| = kt + c, take the exponential of both sides, and you'll get your answer
 
  • #3
That's what I've done. I'm trying to solve for P, and I can't figure out where my method diverges from the book's explanation.

I was hoping someone could show me the steps to solving for P, so I can figure out where my error lies.
 
  • #4
Well, exp(ln|P|-ln|1-P/A|) = exp(kt + c)

The right hand side simplifies to exp(kt+c) = exp(kt)*exp(c) = C exp(kt), where C is a constant that we'll determine later.

The left hand side simplifies to exp(ln|P|-ln|1-P/A|) = exp(ln|P|) exp(-ln|1-P/A|) = P/(1-P/A)

So P/(1-P/A) =C exp(kt)
P = (A-P) C exp(kt)/A
P = AC exp(kt) - (PC/A)exp(kt)
P + (PC/A) exp(kt) = AC exp(kt)
P(1 + C/A exp(kt)) = AC exp(kt)
P = AC exp(kt)/(1+C/A exp(kt))

Multiplying the numerator and denominator of the RHS by exp(-kt)

P = AC/(exp(-kt) + C/A)
 

FAQ: Differential Equations - Logistic Model

What is a logistic model in differential equations?

A logistic model in differential equations is a mathematical function that describes how a population grows or declines over time. It is commonly used to model population growth when there are limiting factors, such as limited resources or competition.

How is a logistic model different from other differential equation models?

A logistic model differs from other differential equation models in that it includes a carrying capacity, which represents the maximum population size that can be sustained in a given environment. This makes it a more realistic model for populations that experience growth limitations.

What are the key components of a logistic model?

The key components of a logistic model are the initial population size, the growth rate, and the carrying capacity. These factors, along with the time variable, are used to create a differential equation that describes the population's growth over time.

How is a logistic model used in real-world applications?

A logistic model is commonly used in fields such as biology, ecology, and economics to predict and understand population dynamics. It can also be used to analyze data and make predictions about future population growth or decline.

Can a logistic model accurately predict population growth in all situations?

No, a logistic model may not accurately predict population growth in all situations. It is best suited for populations that experience growth limitations, and its predictions may not hold true if there are significant changes in the environment or other external factors that affect population dynamics.

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