James Sethna > Sloppy Models > Do Parameters Matter? Fits versus measurements

Tilted ellipses with tilted data points
Parameter ensemble consistent with collective data. Often predictions can be made long before the parameters are determined.
Tilted ellipses with circular cluster of data points
Ensemble consistent with measured parameters. Here all parameters have small errors.
Tilted ellipses with vertical range of data points
Ensemble consistent with all but one parameter measured. Many contours (representing collective model behavior) are crossed if even one parameter is missed.

Do Parameters Matter? Fits versus measurements

We have discovered that multiparameter models are often sloppy: enormous ranges of parameters can be used to fit the same data, and often predictions can be made long before the parameters are even qualitatively determined. (This happens because sloppy combinations of parameters can vary over wide ranges without changing model behavior: yellow dots at right). One can view this as a good or a bad thing. On the one hand, it makes it easier to prove the model is wrong - a key goal for experimentalists. On the other hand, it makes it challenging to prove that the model is correct. Perhaps one is fitting the existing behavior, but for the wrong reason?

One can of course design special experiments to measure one parameter at a time (avoiding the collective behavior of the model as a whole). If we did measure all of the parameters (red dots at right), and the model still fit the data, then we could be far more confident that the model was really correct - that there were no important missing links or reaction pathways, that were fit by fiddling (or renormalizing) the existing model parameters.

My biology collaborators, though, usually test models not by measuring all the parameters, but by changing the system in a drastic way: knocking out a gene, overexpressing a protein, or inhibiting a reaction with a drug (as we describe below). Measuring parameters is considered boring. In fact, the models in biology are usually works in progress: new links and pathways and important features are expected. Perhaps it is an asset that a sloppy model can make predictions about a complex model that are consistent with existing behavior and understanding? Models are useful even when proven wrong, if being wrong tells us something new.

Is measuring parameters useful? In particular, suppose (as is usually the case) it is not feasible to measure every last parameter. Can one extract predictions from models when only half of the parameters are measured? How about all but one parameter? Indeed, for sloppy models, one might expect that even one missed parameter could make for drastic problems. The blue dots at right show a schematic view of a system where one parameter wasn't well measured. Here the range of the unmeasured parameter crossed many contours, representing model behavior. In a sloppy system, where the aspect ratios of the contours are a thousand to one or more, we might expect that missing even one parameter could keep one from making useful predictions.
Signal transduction model
Protein interaction network describing the reaction of PC12 cells to two growth hormones (EGF and NGF). The model has 48 unknown parameters.

The test.

We tested this idea using our systems biology model (left) for the response (active Erk versus time) of a certain cell to a particular growth hormone (EGF). If Erk goes down after 10 minutes, the cell proliferates (reproduces); if it stays up the cell differentiates (grows branches like a neuron). We wanted to determine what would happen if we fed the cell a drug (LY, red X) that would turn off the effects of two of the proteins (the "left wing" colored in gray). There were four styles of predictions we used.

  1. Human brain. Our biology colleague (who wrote down the model) had a prediction. He originally suggested that this left wing was important for turning down the fraction of Erk after ten minutes. So, after adding the drug LY, he predicted that Erk would stay active.
  2. Sloppy fit. We fit the existing experimental data (14 experiments from the literature, doing various measurements after various kinds of interventions). The model was sloppy, and the errors on the 48 parameters were huge. The resulting predictions are shown below on the left as the yellow band. Erk does not stay active at late times; our colleague guessed wrong. (That's why he wanted us to develop the model; his intuition was getting challenged by all of the feedback loops.) The remarkable thing, though, is that the error on the yellow predictions (width of the band) are so small, considering that the parameter uncertainties ranged from a minimum of a factor of fifty, to a maximum of around a million (yellow dots, right figure below).
  3. Measuring all 48 parameters. We simulated the predictions of our model, assuming that all 48 parameters were measured with an error of ±25%. (This would be a huge job.) The results are shown in red below (dashed lines showing the red region behind the yellow one). The predictions are no better than the fits, even thought the parameters are known to far higher precision (red squares at bottom right).
  4. Measuring all but one parameter. Finally, we simulated what would happen if we missed one parameter measurement. In biology, the parameters can vary over enormous ranges: we chose the unknown parameter to vary by a pretty typical unknown factor of 30 up or down (blue triangles at bottom right). The resulting predictions (blue region on bottom left) are completely useless - consistent either with a sustained or transient Erk signal (reproduction or branching). This happened whatever parameter we chose not to measure (except those in the gray left wing). Each parameter has some component along stiff directions, so measuring all of them is necessary for predicting model behavior.

Time series errors
Erk time series predictions for parameters that were fit to data (yellow), measured to ±25% (red), and all-but-one measured (blue). The range of each prediction is determined by sampling the 48-dimensional parameter space (as schematically shown in the three ellipse plots above).
Error bars on parameters
Error bars on parameters. The range of each of the 48 parameters sampled for the plot at left.

What about trying to measure most of the parameters, and fit the ones that are hard to measure? We haven't tested this, but we expect that the (experimentally challenging) parameter measurements might turn out to be unimportant. Fitting data is so powerful, that predictions might be almost as precise if one fit all the parameters (instead of just the unmeasured ones).

Are we advocating laziness? Certainly a few experiments measuring collective behavior are easier than specific experiments measuring each parameter, and boldly making predictions before parameters are known is easier than measuring until the parameters become determined. But we replace experimental rigor with computational rigor. We believe that model predictions should always be accompanied by an error analysis (like in the figure at left). Since the parameters are so often poorly determined by the data, one must not believe the results of the simulation until one also checks the range of behaviors allowed by the data - running simulations covering the huge, sloppy range in parameter space.

Our conclusion? Sloppy systems have a weird connection between parameters and model behavior. Not only can't you use the behavior of the model to determine the parameters, but conversely a partial knowledge of the parameters is useless at making predictions about model behavior. The biologists are right: measuring parameters is boring, if your system is sloppy.


More on sloppiness:

Last Modified: June 11, 2008

James P. Sethna, sethna@lassp.cornell.edu; This work supported by the Division of Materials Research of the U.S. National Science Foundation, through grant DMR-070167.

Statistical Mechanics: Entropy, Order Parameters, and Complexity, now available at Oxford University Press (USA, Europe).