Visualizing Conditional Gaussian I am looking at a graph that depicts a conditional Gaussian:

I understand what the titled red spheres mean - that the variables are somewhat are correlated with each other. 
I don't understand the significance of the blue line. I know it's related to conditional gaussian but I can't quite make sense of it. 
Could someone explain what the blue line means?

Basically the bivariate normal distribution looks like  this one:

Then the conditional distribution $f_{Y|X=x}(x,y)$  is here marked with the red line. We take the joint pdf and plug in $x=1.6$
$f_{X,Y}(x,y) =
      \frac{1}{2 \pi  \sigma_X \sigma_Y \sqrt{1-\rho^2}}
      \exp\left(
        -\frac{1}{2(1-\rho^2)}\left[
          \frac{(x-\mu_X)^2}{\sigma_X^2} +
          \frac{(y-\mu_Y)^2}{\sigma_Y^2} -
          \frac{2\rho(x-\mu_X)(y-\mu_Y)}{\sigma_X \sigma_Y}
        \right]
      \right)$
$f_{Y|X=1.6}(1.6,y) =
      \frac{1}{2 \pi  \sigma_X \sigma_Y \sqrt{1-\rho^2}}
      \exp\left(
        -\frac{1}{2(1-\rho^2)}\left[
          \frac{(1.6-\mu_X)^2}{\sigma_X^2} +
          \frac{(y-\mu_Y)^2}{\sigma_Y^2} -
          \frac{2\rho(1.6-\mu_X)(y-\mu_Y)}{\sigma_X \sigma_Y}
        \right]
      \right)$
This function does depend on the variable y only-the remaining unknowns are parameters. We can just draw the area at $x=1.6$ and omit all the other from the graph above. Then basically we get an univariate distribution of a normal distributed variable. At the graph below we see one example of $f_{Y|X=x}(x,y)$ and $f_{Y|Y=y}(x,y)$ 
Remark
The red circles (ellipses) at your graph show the different values of the joint distribution.
