pwAddY


 m = pwAddY(m, *ID, *rhs, errorModelRhs, noiseType, unit, name,
            description, alternativeIDs, designerProps, classname)
 m = pwAddY(m, interactive)

 Adds an observable to a model. An observable connects a model to an
 experimental measurement.

 NEW SYNTAX SINCE PW 3.0:

 Old:
  pwAddY(m, rhs, ID, scalingParameter, errorModelRhs, noiseType, ...)
 New:
  pwAddY(m, ID, rhs, errorModelRhs, noiseType, ...)
  pwAddS(m, scalingParameter)


 

Arguments for pwAddY

 ID                ID of the observable. Should be different
                   than any other ID, e.g. of a dynamic variable X.
 rhs               Right-hand side of the observation
                   May include x, k, a, and s IDs.
 errorModelRhs     Default: 'y * 0.1 + max(y) * 0.05'
 noiseType         Currently only 'Gaussian' supported.
 unit
 name              descriptive name
 description       Description of the observation
 alternativeIDs    Cell array of alternative IDs which will be used for
                   automatic mapping when adding external data.
 designerProps     for internal use
 classname

 Alternative function call for interactive specification:
 interactive       If true, the user is prompted to enter the properties
                   into a user interface.

 

Description

 Observables Y are functions of dynamical variables X, dynamical parameters K,
 algebraic assignments A, and scaling factors S. They correspond to measurable
 quantities in an experiment. The right-hand side of the observation function
 is specified as a mathematical expression of the IDs belonging X, K, and S.

 

Scaling parameters

 In PW 2.* an implicit scaling parameter was used for each observable.
 This is not the case any more since PW 3.0.
 Now, a scaling between rhs and measurements has to be compensated by
 an S parameter introduced into the rhs directly, e.g.

  pwAddY(m, 'pS_obs', 'scale_pS_obs * (pS + 2 * pStat_pS)');
  pwAddS(m, 'scale_pS_obs', 1);

 This notation is more transparent and explicit.

 

Error model

 Each data point will be weighted during model fitting depending on
 its error bar, i.e. standard deviation. The standard deviation is
 either calculated using an error model, estimated on a spline-based
 approximation or given explicitly by the user in the data file (see pwAddData).

 A default error model is always required, e.g. in order to simulate
 synthetic data. To specify Gaussian noise with a standard deviation
 of 10% relative to y plus 5% absolute (relative to max(y) over all y):

  m = pwAddY(m, 'pS_obs', 'scale_pS_obs * (pS + 2 * pS_pS)', '0.1*y + 0.05*max(y)');

 When using max(yAllStimuli)instead of max(y), the maximum of all
 y values of all stimuli will be calculated.

 

PottersWheel species patterns

 Motivated by rule-based modeling, PottersWheel supports patterns as e.g.
 R_@ which are replaced automatically by the sum of all matching species
 if used in the right-hand side in pwAddY, pwAddZ, and pwAddA.
 Further information is available in pwTutorial_Rule_based_modeling.

 

Fitting with logarithmic data

 In Kreutz et al. 2007 it has been suggested to use log-normally distributed errors
 e.g. in Western blotting. Not taking into account systematic effects e.g.
 of time or gel lane, the measurement model reads:

  yMeas = beta0 + beta1 * yTrue^beta2 * eta with eta ~ e^N(0,sigma)

 We assume that the measured data have no offset, i.e. beta0 = 0.

 Taking the logarithm yields:
  log(yMeas) = log(beta1) + beta2 * log(yTrue) + log(eta)

 beta2 is usually 1 (linear dependency between true and measured y) leading to:
  log(yMeas) = log(beta1) + log(yTrue) + log(eta).

 Note that the scaling factor beta1 is now an additive offset parameter.

 Since log(eta) ~ N(0, sigma), for log(yMeas) a normal error model can be
 assumed, distributed as N(0, sigma).

 Therefore, if you take the logarithm of an experimentally measured
 observable A, the following observable has to be added to the model
 (assuming log-normally distributed errors with mu = 0 and sigma = 0.2):

  m = pwAddY(m, 'logStat_obs', 'log(A) + log(beta1)', '0.2');

 Simultaneously, we want to fit locally the scaling parameter which
 enters the right-hand side as the additive constant beta1. With an initial
 guess of 1 we use:

  pwAddS(m, 'beta1', 1, 'local');

 If you prefer log10, just use 1.0 / log(10.0) * log(A)
 or 0.434294481903252 * log(A).
 This corresponds to a changed basis (=log10(A)).
 Note that this also affects sigma.

 Please compare with
 C. Kreutz, M.M. Bartholome Rodriguez, T. Maiwald, M. Seidl, H.E. Blum, L. Mohr, J. Timmer.
 An error model for protein quantification. Bioinformatics 23, 2007, 2747-2753

See also

pwAddS
pwAddZ
pwAddR