Analyzing a System of Nonlinear ODEs in Biology

In summary, the conversation discusses a system of ODEs arising in biology, and the possibility of finding an analytic solution for it. The system includes two state variables multiplied together, making it a nonlinear system. The conversation suggests finding the fixed points of the system and analyzing its stability by linearizing the system about these points. The conversation also mentions the possibility of the system exhibiting chaotic behavior. Additionally, the conversation provides a method for solving the system using a 2nd order differential equation, but notes that it may require the use of Painlevé transcendents.
  • #1
agonzale
1
0
Hi,

I want to analyze a system of ODEs arising in biology of the form:

x'=a1*x*z
y'=b1*x + b2*y
z'=c1 + c2*z + c3*y*z
with x,y,z state variables and a1,b1,b2,c1,c2,c3 constant parameters.

The difference to a linear system of diffs eqs. is that two state variables are multiplied. Therefore, I think that this system falls under the nonlinear systems for which no general solution can be found analytically. Is this correct?
I could not find information on the net on systems of DEs of such a form. Can somebody send me some links about this kind of system with a multiplication of two state vars?

Thank you in advance,
Aitor
 
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  • #2
There probably isn't an analytic solution for the system, but there is some analysis you can do for this. One thing you can do is find the fixed points of the system: points (x*,y*,z*) such that (x',y',z') = (0,0,0) at these points.

0=a1*x*z
0=b1*x + b2*y
0=c1 + c2*z + c3*y*z

From eq 3, we see that we can't have z* = 0 (unless c1 = 0). The first equation then means that x* = 0, which in the second equation demands y* = 0. Then, the third equation needs z* = -c1/c2. So, if your system starts at the point (x,y,z) = (0,0,-c1/c2), it will stay there for all time. Then, you can consider the stability of this point - what happens when you perturb the system away from it? To do this, we linearize the system about the fixed point. We can write a linear system in the form

[tex] \mathbf{\dot{x}} = \mathsf{A}\mathbf{x}[/tex]

where [tex]\mathbf{x} = (x-x^\ast,y-y^\ast,z-z^\ast)^\mathcal{T}[/tex] and

[tex]\mathsf{A} = \left(
\begin{array}{ c c c}
\frac{\partial x'}{\partial x} & \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial z}\\
\frac{\partial y'}{\partial x} & \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial z}\\
\frac{\partial z'}{\partial x} & \frac{\partial z'}{\partial y} & \frac{\partial z'}{\partial z}
\end{array} \right) [/tex]

evaluated at the fixed point (x*,y*,z*). For your system,

[tex]\mathsf{A} = \left(
\begin{array}{ c c c}
0 & 0 & 0\\
b_1 & b_2 & 0\\
0 & -c_3c_1/c_2 & c_2
\end{array} \right)[/tex]

Once you have this matrix, you find its eigenvalues and eigenvectors. The eigenvalues will tell you the stability of the point, and the eigenvectors will tell you along which direction trajectories will flow. For example, for a general eigenvalue [itex]\lambda = s + i \omega[/itex] with an eigenvector along the x direction (which may not be the case for this system), for s > 0 the system is unstable in the x direction (trajectories leave the fixed point), whereas for s < 0 trajectories will flow toward the fixed point in the x direction. For s = 0, the linear theory predicts closed orbits about the fixed point, but this could turn out to be incorrect due to our neglect of higher order terms.

There are many possible types of behaviours that could occur, especially with a 3D system. For example, the system could become chaotic, like the Lorenz system. The matter is further complicated by the fact that your parameters here are all arbitrary. The stability of the fixed point will generally depend on the values your parameters take.

For a good book about analysing dynamical systems like these, see "Nonlinear Dynamics and Chaos" by Steven Strogatz.
 
  • #3
agonzale said:
I want to analyze a system of ODEs arising in biology of the form:

x'=a1*x*z
y'=b1*x + b2*y
z'=c1 + c2*z + c3*y*z
with x,y,z state variables and a1,b1,b2,c1,c2,c3 constant parameters.

Assuming that all constants [tex] a_1,\,b_1,\,b_2,\,c_1,\,c_2,\,c_3 [/tex] are different from zero your system can be solved with the help of a 2nd order differential equation.

The first equation defines [tex] z(t) [/tex] by
[tex] z(t)=\frac{x'(t)}{a_1\,x(t)} [/tex]
The second equation defines [tex] x(t) [/tex] by
[tex] x(t)=\frac{1}{b_1}\big(y'(t)-b_2\,y(t)\big) [/tex]

Plugging the above relations to the third equation we arrive to the messy 3nd order DE

[tex] \begin{multline*}y'''(t)=\frac{1}{- {b_2}\,y(t) + y'(t)}\,y''(t)^2+ \big(-{b_2} + {c_2} + {c_3}\,y(t) +
\frac{2\,{{b_2}}^2\,y(t)}{{b_2}\,y(t) - y'(t)}\big)\,y''(t)+\\
{b_2}\,\left( {{b_2}}^2 - {a_1}\,{c_1} \right) \,y(t) +
\left( {{b_2}}^2 + {a_1}\,{c_1} -
{b_2}\,\left( {c_2} + {c_3}\,y(t) \right) \right) \,y'(t) +
\frac{{{b_2}}^4\,{y(t)}^2}
{- {b_2}\,y(t) + y'(t)}\end{multline*}[/tex]

Now this equation is autonomous, i.e. it does not contains the time [tex] t [/tex] explicity. Thus with the transformation
[tex] y(t)=\lambda, \quad t=w(\lambda) [/tex]
the ODE becomes
[tex] \begin{multline}w'''(\lambda)=\big(\frac{2}{w'(\lambda )} + \frac{\lambda \,{b_2}}
{-1 + \lambda \,{b_2}\,w'(\lambda )}\big)\,w''(\lambda)^2 +
\big(\frac{2}{\lambda } + \left( {b_2} + {c_2} + \lambda \,{c_3}
\right) \,w'(\lambda ) +
\frac{2}{\lambda \,\left( -1 +
\lambda \,{b_2}\,w'(\lambda ) \right) }\big)\,w''(\lambda)+\\
\frac{1}{{\lambda }^3\,{b_2}} +
\frac{w'(\lambda )}{{\lambda }^2} +
\frac{{b_2}\,{w'(\lambda )}^2}{\lambda } -
\left( {a_1}\,{c_1} - {b_2}\,{c_2} -
\lambda \,{b_2}\,{c_3} \right) \,{w'(\lambda )}^3 +
\lambda \,{a_1}\,{b_2}\,{c_1}\,{w'(\lambda )}^4 +
\frac{1}{{\lambda }^3\,{b_2}\,
\left( -1 + \lambda \,{b_2}\,w'(\lambda ) \right) }\end{multline} [/tex]
Setting [tex] w'(\lambda)=u(\lambda) [/tex] the previous equation becomes a 2nd order DE for [tex] u(\lambda) [/tex].
Since this equation involves [tex] u(\lambda)^2 [/tex] it must have a solution with the help of [tex] Painlev\'e [/tex] transcendents but I don't have the time to really prove it! :approve:

I hope the above analysis helps you (and not confuse you :confused:)
 

1. What is the purpose of analyzing a system of nonlinear ODEs in biology?

The purpose of analyzing a system of nonlinear ODEs (ordinary differential equations) in biology is to understand and predict the behavior of complex biological systems. These systems can include biochemical reactions, population dynamics, and physiological processes. By studying the interactions between different variables and how they change over time, we can gain insights into the underlying mechanisms of biological systems.

2. What makes a system of ODEs nonlinear?

A system of ODEs is considered nonlinear when the equations involved involve one or more nonlinear functions. This means that the variables in the equations are raised to a power, or are multiplied or divided by each other. Nonlinear ODEs often have complex and unpredictable solutions, making them challenging to solve and analyze.

3. How are nonlinear ODEs typically solved and analyzed in biology?

Nonlinear ODEs in biology are often solved and analyzed using numerical methods, such as Euler's method or Runge-Kutta methods. These methods involve breaking down the equations into smaller, simpler steps and using iterative calculations to approximate the solution. Once the solution is obtained, it can be analyzed using techniques such as phase portraits, bifurcation diagrams, and sensitivity analysis.

4. What are some applications of analyzing nonlinear ODEs in biology?

Analyzing nonlinear ODEs in biology has a wide range of applications. It can be used to model and predict the spread of infectious diseases, understand the dynamics of cellular signaling pathways, and study the effects of environmental changes on population dynamics. It is also used in drug development and personalized medicine, as it allows researchers to simulate and test the effects of different treatments on biological systems.

5. What are some challenges in analyzing nonlinear ODEs in biology?

One of the main challenges in analyzing nonlinear ODEs in biology is that they can have multiple solutions or exhibit chaotic behavior. This makes it difficult to determine the most accurate and biologically relevant solution. Additionally, the large number of variables and parameters involved in these systems can make it challenging to model and interpret the results. It also requires a strong understanding of both mathematical and biological concepts, making interdisciplinary collaboration essential for successful analysis.

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