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How to calibrate a computational model?

  • updated 1 mth ago

Calibration, or parameter estimation, consists in solving the inverse problem of finding descriptors (species, parameters or compartments) values such that the computational model outputs match some user-defined fitness criteria.

Fitness criteria

A fitness criterion is a metric of the match between outputs of the simulation and the expected behaviors that are covered by the Scoring sets and / or the data tables used as inputs. 

The "Objective function" tab of the calibration consists of a list of scoring sets and/or data tables, where having at least one item selected is mandatory.

Scoring set

A scoring set is a set of constraints that the model simulation should respect, and that may be quantitative or qualitative. For now, it is only possible to create scoring sets with Nova internal tools - reach out to your contact point to get some support in defining those for your models.

Data table

A data table usable in a calibration is typically the time series corresponding to a specie, a parameter or a compartment volume of the model. These Data tables can be mapped in order to be compatible with the calibration of a computational model, provided that they are in a compatible format. See the dedicated documentation to read more about data tables and mappings. 

The data tables transformed to be usable in the context have the following columns: 

  • obsId - mandatory. The ID of the model component that is to be compared to the data. 

  • time - mandatory. Time point, as an ISO 8601 duration

  • One of the following* - mandatory: 
    *Note that if both columns are provided then the narrow bounds prevail

    • value:  Target numerical value of the obsId at time

    • Or narrowRangeLowBound and narrowRangeHighBound

      • The score is =1 if the simulated value is within the narrow range. 

      • Default is: (value,value), hence in that case, the score will equal 1 only if the simulated value is exactly equal to the target. 

      • The value has to be within the narrow range.

  • wideRangeLowBound and wideRangeHighBound - optional. Wide range around the target values. The score is:

    •  in ]0,1[ if the simulated value is within the wide bounds but outside of the narrow bounds 

    • =0 if the simulated value is equal to one of the wide bounds, with a quadratic evolution

    • <0 if the simulated value is outside of the wide bounds

    • Default is : (narrowRangeLowBound - 1, narrowRangeHighBound + 1). In that case the cost function is equivalent to a mean squared error (f(x) = 1 - (x- x*)^2 where x* is either the narrowRangeLowBound or narrowRangeRightBound). 

  • armScope - optional. This row will only be evaluated on that protocol arm of the simulation. If null, the observation will be applied to all arms.

  • weight - optional. Weight of the observation, it will be used to compute a weighted mean for the score tied to each experimentName. Default is 1.

  • unit - optional. Unit of the values and bounds. This will be used to raise a warning in case the unit is different between the data table and the computational model used, but it will not convert the data. 

  • experimentRef - optional. A link to track where the data comes from, like the "links" field of a computational model. 

Here is an example of a table that can be uploaded to jinkō for calibration purposes: 

obsId* time* value* narrowRangeLowBound* narrowRangeHighBound* wideRangeLowBound wideRangeHighBound armScope weight unit experimentRef
String String (ISO8601 convention)  Numeric Numeric Numeric Numeric Numeric String Positive Numeric String String
observable_1 P0M 30 44 54 32.8 172.8 control 1 mg https://...
observable_1 P12M 34 44 54 32.8 172.8 control 1 mg https://...
observable_1 P0M 41 48 58 37.6 185.6 treated 1 mg https://...
observable_1 P12M 42 48 58 37.6 185.6 treated 1 mg https://...
observable_2 P0M 5 2 10 0 32 control 1 mol / L https://...
observable_2 P12M 5 2 10 0 32 control 1 mol / L https://...
observable_2 P0M 5 2 10 0 32 treated 1 mol / L https://...
observable_2 P12M 5 2 10 0 32 treated 1 mol / L https://...

 

Algorithm 

The calibration fitness function is defined as the weighted sum of the different fitness criteria. It produces a single scalar value, ranging from -∞ to 1, which the calibration algorithm aims at maximizing.

Under the hood, Jinkō uses the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm to perform such maximization.

Running a calibration

Computational model, Output set and Protocol arms

To start off, you’ll need a computational model to calibrate. This model will be used to run the simulations with varying inputs (cf next section). 

You can also add an Output Set, if there are outputs of the model you’d like to observe. Note that unlike the scorings, which may also be output as scalar results, the output set will not be used for the optimization, but only for visualization. 

You can also add Protocol arms, in order to run your model in several different circumstances (typically, different types of experiments, or different doses of a trial).

Inputs to calibrate

The inputs to calibrate are the model components that will be used for the optimization of the weighted score. You will need to select here the parameters, species or compartments for which the values (respectively initial condition or volume) are unknown. You also need to ensure that the selected inputs actually have an impact on the outpurs you want to optimize: you can use the contribution analysis tool for that. 

For each selected input, you need to define:

  • The mean and SD: the default values are taken from your CM, and will be used to define a normal distribution from which the patients for the first iteration will be drawn.

  • The bounds are used in the calibration to penalize unacceptable values of a given parameter, typically to avoid wrong behaviors of the model, such as a KM parameter becoming negative. 

  • For parameters that you need to explore on a large range, you can use a log transformation. In that case, In that case we perform a change of variables and calibrate the log of the parameter. This means in particular that the the mean and SD values are those of the log (i.e. you should put a mean of 1 if you want your parameter to be sampled around 10). 

Adding too many inputs to calibrate or very wide ranges will lead to the parameter space to explore being huge, and therefore maybe not fully explored. On the other hand, having a too small parameter space may not allow you to optimize your weighted score. It is usually recommended to have 3 to 10 inputs to calibrate, possibly by doing several iterative calibration steps.

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