Ingeneue Tutorial #1

The Segment Polarity Model

This tutorial is intended to get you started with our segment polarity network model. We used this network as our test case to develop Ingeneue. Before doing this tutorial, you will want to look at the paper we published on this model, as the tutorial essentially tells you how to recreate many of our results in that paper:

G. von Dassow, E. Meir, E. Munro, G. Odell. (2000). The segment polarity network is a robust developmental module. Nature 406: 188-92

Files for this tutorial are located in the networks/segmentpolarity directory.

The tutorial takes you through the following steps:

Tutorial 2 shows you how to construct your own network files, walking you through the example of a simple genetic switch.

Tutorial 3 shows you how to modify & create your own affectors.

Starting up and running a model

1. Start Ingeneue. Ingeneue should appear looking something like that shown below:


2. Ingeneue uses text files as inputs. These files can be found in the networks/segmentpolarity folder in Ingeneue. There are 3 types of files: ".net" files define genetic networks; ".params" files contain sets of parameters for a particular network; ".iter" files contain instructions for automatically running models within Ingeneue. Before loading a network, take a quick look at one of the network files.

3. Using your favorite text editor, open the file "" ("spg" = segment polarity genes). Skim through this file to see the basic structure. We'll come back to parts of this file later on in more detail. For now, just notice that the top section defines all the different components of the model – mRNA's, proteins, protein complexes and so on. These components are the "Nodes" of the network. Next comes a section specifying how each Node changes over time. The items in this section correspond to equations that will be summed into one overall differential equation per Node. We call these equation fragments "Affectors". The last two major sections give the values for each parameter in the model and the initial conditions.

4. Select 'Open' from the 'File' menu in Ingeneue, navigate to the networks/segmentpolarity directory in Ingeneue, and load the "" file you were just looking at. In addition to the two windows that appeared at start-up, Ingeneue opens a new window titled "Cell View". Cell View contains a column of pictures of the field of cells that will be used in this model. Each picture is labelled with one of the Nodes in the model, and will show the concentration of that Node in each cell. Cell View only shows a subset of the Nodes, and you can choose which ones in the input file or while the program is running (see below). To make things run fast the model is just a two-segment strip of cells and only two cells high. The initial conditions don't show up in the Cell View until you reset or run the model. Also, the main control window now contains a diagram of the network, showing which Nodes influence each other directly. Clicking on each Node brings up a Node Inspector window that allows you to specify whether that Node is shown in the Cell View or not, and, when the model is running, the Node Inspector allows you to see the numerical value of a Node in an individual cell.


5. Run the model by clicking on the 'Run' button in the upper right corner. Pretty boring, huh? Obviously the parameter values specified in the input file aren't too conducive to stripe patterning. The next section shows you how to load a file with sets of parameters (that we pre-selected) which cause the model to make stripes.

Using pre-collected parameter sets

6. Select 'Open' from the 'File' menu and load the file "spg.params". Ingeneue will then bring up a new Parameters window that contains a plot that looks a little like an incompetent spider's web. This plot shows you one or more parameter sets. Note: you may need to expand the window to see all the controls and to see the wheel plot clearly. Each spoke of the wheel is the axis for a single parameter, with the lowest possible value that parameter could take on at the inner circle and the highest value at the outer circle. A parameter's value is plotted as a point on its spoke in between the inner and outer circles. There are 48 parameters in this model so there are 48 spokes. A single parameter set is then a polygon with a vertex on each spoke at the position of each parameter's value in that set. Don't worry about reading the graph precisely; the point is that the different polygons are vastly different from one another in shape, and thus parameter sets are widely distributed in space.


7. To get a feeling for how the parameter sets vary, scan through the parameter sets by using the arrow buttons at the bottom of the Parameters window. Again, the point to recognize is that, although as you will soon discover all these parameter sets confer similar behavior on the model, they specify widely different values for each parameter.

8. Pick one of the parameter sets and load it into the model by clicking on the 'Load Cam' button in the Parameters window.

9. Click on the 'Run' button to run the model with this new set of parameters. Much better, yes?


10. Repeat steps 8 and 9 for as many of the other parameter sets as you can. Notice how all these parameter sets make qualitatively similar patterns with respect to engrailed (en), wingless (wg), and hedgehog (hh). At the same time, notice how different the detailed behavior of each parameter set is, especially during the initial period, despite all making qualitatively the same pattern. If you want to sit back and relax and watch the program test all the parameter sets in turn, load the file "tester.iter" and choose 'Run’ from the 'Iterator Controller' menu. So far you have seen a single instance of a parameter set that does not form segments and several instances of parameter sets that do form segments. What else can the model do? The next section explores that question by running the model with randomly generated parameter sets.

Sampling random parameter sets
To run a model automatically in Ingeneue you use a software module called an Iterator. An Iterator performs some predefined set of actions over and over again. One of the simplest Iterators picks random sets of parameters for a model and runs it, usually using a pattern recognition module to score the behavior of the model with each parameter set. The framework we have for Iterators right now is a bit more complex than it ought to be, and this tutorial won't try to explain them, just have you use a simple one.

11. Select 'Open' from the 'File' menu and choose the file "randomsampler.iter". This should open the Iterator control panel.


12. Click on the 'Run' button. Ingeneue cycles through one randomly-picked parameter set after another, running each 200 minutes. Here are several things to pay attention to:

13. Some runs are slower than others, but all cover roughly 200 minutes (time is shown in the upper left of the Cell View). The speed difference reflects differences in the "stiffness" of the equations, which depends on the particular parameter values in the current set. This speed difference in the simulation has absolutely nothing to do with the speed at which the pattern could be formed in a real embryo, just with how fast the computer can solve the equations.

14.Lots of runs do boring things like turn all genes off or turn something on everywhere. Many others look promising but then collapse. See if you can get a feel for what tends to happen as the initial pattern collapses. Look at the network diagram to see if you can intuit what's going on. Once you have tried this. Go to the Network viewer Panel and choose 'Turn Active Display On' from the Active Display menu. How does your intuition hold up? You can also select which cell to view the network in from the same menu.


15. There are many other stable patterns besides the segment polarity pattern that the network can adopt, even though they all start from the same initial conditions. You should see some of them going by, and every once in a while (about 1% of the time) you may see the model make the correct pattern.

Note: for the segment polarity model we impose repeating boundary conditions, meaning that on each edge the cell grid wraps around so that, for instance, the cells on the east edge are actually neighbors of the cells on the west edge. The file "" specifies an 8x2 cell grid, which seems to be about right for a human to see clearly the nature of the patterns the model makes. However, as long as we impose repeating boundary conditions, and as long as the model is entirely deterministic (i.e. no random noise afflicts the Nodes), a 4x1 cell grid is equivalent but four times faster, which of course makes searches much more bearable for the human. For example, once you have a feel for the variety of patterns the model makes, you might want to get a feel for how frequent some of those patterns are. The file "" uses a 4x1 grid, but is otherwise exactly the same as "". Load "", then "randomsampler.iter" (you may have to restart the program if it emits a stream of error messages), and then choose 'Run Iterator' again from the 'Run' menu.

16. When you are tired of watching this, select 'Stop' from the 'Interator control' menu. Until now you have been using either working parameter sets that we found, or random sets. The next section shows you how to set parameter values yourself to get a better feel for how each individual parameter might affect the behavior of the model.

Changing parameter values by hand

There are two different ways to change parameter values of an Ingeneue model. You can change them in the input file. You can also change them through the Ingeneue interface by selecting ‘Network Parameters’ from the ‘Viewers’ menu, which shows all the parameters, their initial conditions and values in a table. Changing parameters is about as straightforward as it can be.

17. Start with a parameter set that makes the right pattern by loading the network file "".

18. Run the model to see what pattern these parameters produce, taking note of the initial transients, and how well it matches the target pattern.

19. Open "" in a text editor and scroll to a section labeled "&ParameterValues". You'll see a bunch of lines that look like this:

&K_WGen 0.1687772 0.0010 1.0 Logarithmic                          

The tag with the ampersand is the name of the parameter. The first number is the set value of that parameter. This is the only number we'll work with in this tutorial. The next two are the lower and upper bounds for that parameter. The "Logarithmic" specifier means that this parameter is plotted on a log scale in the Parameters graph, and that during random sampling it varies on a log scale.

20. The manual and other documents contain fuller descriptions of the general classes of parameters used in Ingeneue models.

21. Think about what parameter you might want to change to achieve a particular effect on the behavior of the model (or just pick randomly). Then replace the current value of the parameter – the first number on the parameter definition line – with another value between the minimum and maximum values. The minimum and maximum are merely guidelines, and may be violated if you wish. For example, for parameters starting with "K_", a value well above 1.0 (i.e. 10 or so) usually means that the regulatory connection governed by that parameter is too weak to do anything.

22. Choose 'Save As' in your text editor and save the file under a new name, to avoid overwriting the original.

23. Load this new file into Ingeneue and run it.

Repeat 21 - 23 a bunch of times until you are sick of the little colored hexagons. You can also start with another initial parameter set by using one of the other "" files. Depending on the parameter set you start with you may find it very hard to change the behavior of the model, whereas with another starting set you may find it very easy to nudge it into doing something quite different.

Changing initial conditions

The "initial conditions" for the model is the pre-pattern that the model starts running from – the initial concentrations of every Node in every Cell. It is the pattern displayed at time 0 in your runs above (or after resetting the model). As with changing parameter values, there are two ways to change the initial conditions in an Ingeneue model. You can change them in the network file, or you can change them through the interface. While changing through the network file is more powerful, in this case it is easier to change them through the interface. In this section, you explore the effect of simple changes to initial conditions.

24. Load a network file (one of "") that makes nice stripes.

25. In the Cell View window, double-click on one of the cells for a Node whose initial value you want to change. For instance, if you want to change the initial concentration of engrailed mRNA in the top-left cell, double-click on the top left cell in the row labelled "en".

Note: to correspond with the numbering of arrays in Java and similar programming languages, the first (that is, top left) Cell in the cell grid displayed in Cell View is numbered 0.

26. In the Inspector window which appears, there is a number called InitValue. Concentrations for most Nodes are scaled between 0 and 1, so change the InitValue to any number you want between 0 and 1.

27. Click on the 'Set' button in the Inspector window.

28. Reset the model. You should see the pre-pattern reflecting the change you just made.

29. Run the model.

30. If you want to change the initial value for a Node that is not shown in the Cell View, click on the Node in the network diagram and then click once (only once) on the cell where you want to change its value in any of the rows in the Cell View. You will now get the appropriate Inspector and can change the initial value as above. Or, click on the Node in the network diagram, then check the box to show the Node in the Cell View, and then double-click the appropriate cell in the new row.

31. Play around with different initial conditions. Start with one of the "" files, and then modify the initial conditions to explore what effect they have. How far can you get from the crisp initial condition that we used and still get good segments? What other patterns can you get with different initial conditions? You might want to modify the initial conditions in "", then use the "randomsampler.iter" to conduct a random search.

A test of sensitivity to random noise

Next you'll test how sensitive the model is to random variations in the initial concentrations of engrailed and wingless. Of course, the model needs parameters, so we use the ones collected in "spg.params"; thus you'll really be testing the sensitivity of the model with each of those parameter sets.

32. Load the file "". Click on the 'Reset' button. You should see a 4x3 grid of cells with engrailed and wingless stripes, but with random fluctuations in the level of each of them in each cell.

33. Load the same file again, choose 'Reset', and note that it now exhibits a different random variation in the initial prepattern. The program is set up so that the initial prepattern is randomized slightly each time the model is loaded, not each reset. This means that you can test a battery of parameter sets against the same noisy variant.

34. To do that, load the file "spg.params". Flip through the parameter sets, press 'Load Cam', and run the model. Depending on the initial prepattern, you should see that some sets allow the model to "correct" the initial sloppiness, but others fail in various ways.

35. To get a new sloppy pattern, re-load ""; you should not need to reload the battery of parameter sets. Flip through them again, applying each one, and running the model. Repeat for several re-loads of "".

36. You can use the file "tester.iter" to run through all the parameter sets in "spg.params". Load "", then load "spg.params", then load "tester.iter", and choose 'Run' from the ‘Iterator Control’ window. To get a new sloppy pattern, reload "", and re-run the Iterator.

37. The Iterator specified in "tester.iter" will run the model with each parameter set for 1000 min, and assign it a score based on whether it makes decent stripes of engrailed, wingless and hedgehog. If you are really diligent and repeat this step several times, you can follow the output in the console window to keep track of which parameter sets tolerate which insults. You will find that some parameter sets are consistently able to correct against random variation in the initial pattern, but that some are more fragile to this kind of perturbation.

38. This exercise uses a bit of a cumbersome way to test this sort of thing, but it does it using very simple tools. Ingeneue includes some more powerful facilities for doing this sort of thing, and the design of the program makes it easy to add iterator objects that conduct very sophisticated tests that involve running the simulation over and over again while varying parameters or initial conditions.

That's the end of this tutorial. The next tutorial takes you through building a very simple model with Ingeneue. After that, you can look in the manual for more details. If you are a programmer, there is also a fair bit of documentation contained within the source code, including descriptions of each of the Affectors (pieces of equations). And of course, you can always email one of us. Enjoy.

Go to Tutorial 2