Finding differences amongst a system of differential equations

In summary: I just can't seem to find it.In summary, the equations in Box 2.4 (continued) are different from the equations in Box 2.3 because the ##-\beta RV## term in ##V(t)## is not negligible.
  • #1
J6204
56
2

Homework Statement


Given the following figure and the following variables and parameters, I have been able to come up with the set of differential equation below the image. My question is how does the system of equations 1 which I produced myself differ from the set of equations 2. Below I have a further explanation of this question. The image below was used to create my system of equations 1.

Homework Equations


Variables
R(t): number of susceptible T-cells
L(t): number of latently infected T-cells
E(t): number of actively infected T-cells
V(t): amount of virus

Parameters
$$\Gamma$$: rate of production of susceptible T-cells
$$\tau$$: fraction of T-cells susceptible to attack by HIV
$$\mu$$: removal rate of T-cells
$$\beta$$: rate of T-cell infection
p: fraction of infected T-cells that are latently infected
$$\alpha$$: rate that latent T-cells become activated
$$\delta$$: death rate/removal of actively infected T-cells
$$\pi$$: rate that virus is produced by actively infected T-cells
$$\sigma$$: rate of virus removal System of Equations 1
$$\frac{dR}{dt} = \Gamma \tau - \mu R - \beta VR $$
$$\frac{dL}{dt} = p \beta VR-\mu L - \alpha L$$
$$\frac{dE}{dt} = (1-p)\beta V R+ \alpha L - \delta E - \pi E$$
$$\frac{dV}{dt} = \pi E - \sigma V - \beta V R$$

System of Equations 2
$$\frac{dR}{dt} = \Gamma \tau - \mu R - \beta VR $$
$$\frac{dL}{dt} = p \beta VR-\mu L - \alpha L$$
$$\frac{dE}{dt} = (1-p)\beta V R+ \alpha L - \delta E $$
$$\frac{dV}{dt} = \pi E - \sigma V $$

The Attempt at a Solution


So clearly there is a difference between the number of infected T cells in system of equations 1 and
assigment2.png
2. System of equations 1 includes the term $$\pi E$$ while system of equations 2 does not in equation 3. Why is this?

There is a difference between the amount of virus in system of equations 1 and 2. System 1 includes the loss of term $$\beta VR$$ while the system of equations of 2 in equation 4. Why is this?
 

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  • #2
upload_2018-1-30_17-51-54.png


So set 1 comes from the above picture in Otto (A Biologist's Guide to Mathematical Modeling in Ecology and Evolution) and set 2 comes from Science.

Apparently, both the article in Science and Otto missed the ##-\beta RV## in ##V(t)## but not in ##R(t)##, but you did not. [edit] However, at the bottom in Box 2.4 (continued) they claim it is negligible.

The ##-\pi E## term does not appear in ##dE\over dt## because the infected cell produces the virus particles but stays intact. This is explained in Box 2.4 (continued). So there I side with the Science/Otto sets.

You can check your set of equations by adding them all up. That should yield the in- and outgoing solid arrows plus the ##\pi E## as mentioned.

[edit] my advice: take your time to read the whole thing :smile:
 

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  • #3
BvU said:
View attachment 219387

So set 1 comes from the above picture in Otto (A Biologist's Guide to Mathematical Modeling in Ecology and Evolution) and set 2 comes from Science.

Apparently, both the article in Science and Otto missed the ##-\beta RV## in ##V(t)## but not in ##R(t)##, but you did not. [edit] However, at the bottom in Box 2.4 (continued) they claim it is negligible.

The ##-\pi E## term does not appear in ##dE\over dt## because the infected cell produces the virus particles but stays intact. This is explained in Box 2.4 (continued). So there I side with the Science/Otto sets.

You can check your set of equations by adding them all up. That should yield the in- and outgoing solid arrows plus the ##\pi E## as mentioned.

[edit] my advice: take your time to read the whole thing :smile:

so is my equation for ##dE\over dt## correct for the figure? and what about the last equation ##dV\over dt## why is this different?
 
  • #4
BvU said:
View attachment 219387

So set 1 comes from the above picture in Otto (A Biologist's Guide to Mathematical Modeling in Ecology and Evolution) and set 2 comes from Science.

Apparently, both the article in Science and Otto missed the ##-\beta RV## in ##V(t)## but not in ##R(t)##, but you did not. [edit] However, at the bottom in Box 2.4 (continued) they claim it is negligible.

The ##-\pi E## term does not appear in ##dE\over dt## because the infected cell produces the virus particles but stays intact. This is explained in Box 2.4 (continued). So there I side with the Science/Otto sets.

You can check your set of equations by adding them all up. That should yield the in- and outgoing solid arrows plus the ##\pi E## as mentioned.

[edit] my advice: take your time to read the whole thing :smile:

I was asked to compare the equations I have made to the equations given to find difference so I guess I was right? there should be some difference?
 
  • #5
J6204 said:
so is my equation for dEdtdEdtdE\over dt correct for the figure?
No. E does not change when a virus is released.
J6204 said:
I was asked to compare the equations I have made to the equations given to find difference so I guess I was right? there should be some difference?
Was answered in an edit:
BvU said:
Apparently, both the article in Science and Otto missed the ##-\beta RV## in ##V(t)## but not in ##R(t)##, but you did not. [edit] However, at the bottom in Box 2.4 (continued) they claim it is negligible.

BvU said:
[edit] my advice: take your time to read the whole thing :smile:
 

1. What is the purpose of finding differences amongst a system of differential equations?

The purpose of finding differences amongst a system of differential equations is to analyze and understand the behavior of the system over time. By identifying the differences in the equations, we can determine how the system will change and evolve under different conditions or inputs. This is crucial in many scientific and engineering fields, such as physics, chemistry, and biology, as it allows us to make predictions and design solutions based on the behavior of the system.

2. How do you identify the differences in a system of differential equations?

The differences in a system of differential equations can be identified by examining the coefficients and variables in each equation. Generally, the differences will involve the derivatives of the variables with respect to time. For a system of linear equations, the differences will be constant, while for non-linear equations, they may vary with the variables. Additionally, the initial conditions and boundary conditions can also affect the differences in the equations.

3. Can the differences in a system of differential equations change over time?

Yes, the differences in a system of differential equations can change over time. This is because the equations may involve variables that are dependent on time, such as growth rates or decay rates. As these variables change, the differences in the equations will also change, resulting in different behaviors of the system over time.

4. How do you solve a system of differential equations with varying differences?

Solving a system of differential equations with varying differences can be challenging and often requires advanced mathematical techniques. One approach is to use numerical methods, such as Euler's method or Runge-Kutta methods, to approximate solutions. Another approach is to use analytical methods, such as separation of variables or Laplace transforms, to find exact solutions. Ultimately, the best method will depend on the specific equations and initial/boundary conditions of the system.

5. What are some real-world applications of finding differences in a system of differential equations?

Finding differences in a system of differential equations has many real-world applications, including modeling population growth, predicting chemical reactions, analyzing electrical circuits, and understanding weather patterns. It is also used in fields such as economics, epidemiology, and environmental science to make predictions and inform decision-making. Essentially, any system that can be described by differential equations can benefit from finding differences to better understand its behavior and make accurate predictions.

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