diff --git a/doc/index.rst b/doc/index.rst index ab645524..f5ee2553 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -6,7 +6,7 @@ :caption: PEtab v1 PEtab v1 specs - v1/tutorial/tutorial + Tutorial v1/software_support @@ -15,6 +15,7 @@ :caption: PEtab v2 PEtab v2 draft + Tutorial .. toctree:: :maxdepth: 2 diff --git a/doc/v1/tutorial/tutorial.rst b/doc/v1/tutorial/tutorial.rst index c517ac44..d8f23013 100644 --- a/doc/v1/tutorial/tutorial.rst +++ b/doc/v1/tutorial/tutorial.rst @@ -1,6 +1,6 @@ -============== -PEtab tutorial -============== +================== +PEtab 1.0 tutorial +================== .. _Boehm et al. (2014): https://dx.doi.org/10.1021/pr5006923 diff --git a/doc/v2/tutorial/Boehm_JProteomeRes2014.yaml b/doc/v2/tutorial/Boehm_JProteomeRes2014.yaml new file mode 100644 index 00000000..f111fcf5 --- /dev/null +++ b/doc/v2/tutorial/Boehm_JProteomeRes2014.yaml @@ -0,0 +1,15 @@ +format_version: 2.0.0 +model_files: + model: + location: model_Boehm_JProteomeRes2014.xml + language: sbml +parameter_files: + - parameters.tsv +experiment_files: + - experiments.tsv +condition_files: + - experimental_conditions.tsv +observable_files: + - observables.tsv +measurement_files: + - measurement_data.tsv diff --git a/doc/v2/tutorial/experimental_conditions.tsv b/doc/v2/tutorial/experimental_conditions.tsv new file mode 100644 index 00000000..6f67aa36 --- /dev/null +++ b/doc/v2/tutorial/experimental_conditions.tsv @@ -0,0 +1,2 @@ +conditionId targetId targetValue +epo_bolus Epo_concentration 1.25E-07 diff --git a/doc/v2/tutorial/experiments.tsv b/doc/v2/tutorial/experiments.tsv new file mode 100644 index 00000000..6bcb6543 --- /dev/null +++ b/doc/v2/tutorial/experiments.tsv @@ -0,0 +1,2 @@ +experimentId time conditionId +epo_stimulation 0.0 epo_bolus diff --git a/doc/v2/tutorial/gfx/copasi_simulation.png b/doc/v2/tutorial/gfx/copasi_simulation.png new file mode 100644 index 00000000..c5db8ee3 Binary files /dev/null and b/doc/v2/tutorial/gfx/copasi_simulation.png differ diff --git a/doc/v2/tutorial/gfx/tutorial_data.png b/doc/v2/tutorial/gfx/tutorial_data.png new file mode 100644 index 00000000..ccd5f408 Binary files /dev/null and b/doc/v2/tutorial/gfx/tutorial_data.png differ diff --git a/doc/v2/tutorial/gfx/tutorial_model.png b/doc/v2/tutorial/gfx/tutorial_model.png new file mode 100644 index 00000000..26d8a420 Binary files /dev/null and b/doc/v2/tutorial/gfx/tutorial_model.png differ diff --git a/doc/v2/tutorial/measurement_data.tsv b/doc/v2/tutorial/measurement_data.tsv new file mode 100644 index 00000000..48e6bb76 --- /dev/null +++ b/doc/v2/tutorial/measurement_data.tsv @@ -0,0 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+</celldesigner:extension> +</annotation> +<listOfReactants> +<speciesReference metaid="CDMT00027" species="nucpBpB"> +<annotation> +<celldesigner:extension> +<celldesigner:alias>sa7</celldesigner:alias> +</celldesigner:extension> +</annotation> +</speciesReference> +</listOfReactants> +<listOfProducts> +<speciesReference metaid="CDMT00028" species="STAT5B" stoichiometry="2"> +<annotation> +<celldesigner:extension> +<celldesigner:alias>sa1</celldesigner:alias> +</celldesigner:extension> +</annotation> +</speciesReference> +</listOfProducts> +<kineticLaw metaid="CDMT00029"> +<math xmlns="http://www.w3.org/1998/Math/MathML"> +<apply> +<times/> +<ci> nuc </ci> +<ci> k_exp_homo </ci> +<ci> nucpBpB </ci> +</apply> +</math> +</kineticLaw> +</reaction> +</listOfReactions> +</model> +</sbml> diff --git a/doc/v2/tutorial/observables.tsv b/doc/v2/tutorial/observables.tsv new file mode 100644 index 00000000..3497f046 --- /dev/null +++ b/doc/v2/tutorial/observables.tsv @@ -0,0 +1,4 @@ +observableId observableFormula noiseFormula noisePlaceholders noiseDistribution +pSTAT5A_rel (100 * pApB + 200 * pApA * specC17) / (pApB + STAT5A * specC17 + 2 * pApA * specC17) pSTAT5A_rel_sigma pSTAT5A_rel_sigma normal +pSTAT5B_rel -(100 * pApB - 200 * pBpB * (specC17 - 1)) / ((STAT5B * (specC17 - 1) - pApB) + 2 * pBpB * (specC17 - 1)) pSTAT5B_rel_sigma pSTAT5B_rel_sigma normal +rSTAT5A_rel (100 * pApB + 100 * STAT5A * specC17 + 200 * pApA * specC17) / (2 * pApB + STAT5A * specC17 + 2 * pApA * specC17 - STAT5B * (specC17 - 1) - 2 * pBpB * (specC17 - 1)) rSTAT5A_rel_sigma rSTAT5A_rel_sigma normal diff --git a/doc/v2/tutorial/parameters.tsv b/doc/v2/tutorial/parameters.tsv new file mode 100644 index 00000000..38c9060d --- /dev/null +++ b/doc/v2/tutorial/parameters.tsv @@ -0,0 +1,12 @@ +parameterId parameterName lowerBound upperBound nominalValue estimate +Epo_degradation_BaF3 EPO_{degradation,BaF3} 1E-05 100000 true +k_exp_hetero k_{exp,hetero} 1E-05 100000 true +k_exp_homo k_{exp,homo} 1E-05 100000 true +k_imp_hetero k_{imp,hetero} 1E-05 100000 true +k_imp_homo k_{imp,homo} 1E-05 100000 true +k_phos k_{phos} 1E-05 100000 true +ratio ratio 0.693 false +sd_pSTAT5A_rel \sigma_{pSTAT5A,rel} 1E-05 100000 true +sd_pSTAT5B_rel \sigma_{pSTAT5B,rel} 1E-05 100000 true +sd_rSTAT5A_rel \sigma_{rSTAT5A,rel} 1E-05 100000 true +specC17 specC17 0.107 false diff --git a/doc/v2/tutorial/tutorial.rst b/doc/v2/tutorial/tutorial.rst new file mode 100644 index 00000000..4bb4f952 --- /dev/null +++ b/doc/v2/tutorial/tutorial.rst @@ -0,0 +1,352 @@ +================== +PEtab 2.0 tutorial +================== + +.. _Boehm et al. (2014): https://dx.doi.org/10.1021/pr5006923 + + +Overview +++++++++ + +In the following, we demonstrate how to set up a parameter estimation +problem in PEtab based on a realistic application example. To this end, +we consider the model and experimental data by +`Boehm et al. (2014)`_. The +model describes the dynamics of phosphorylation and dimerization of the +transcription factors STAT5A and STAT5B. A visualization and the +corresponding reactions of the model are provided below, although the +details of the model are not relevant for the purpose of this tutorial. +For more details, we refer to the original publication. + +We will start with the model, and then proceed to link the model to +experimental data by defining experimental conditions, observation +functions, and measurements. After this, we will define the parameters +to be estimated, and finally group all files in a YAML file to define +the PEtab problem. + +1. The model +++++++++++++ + +PEtab assumes that an SBML file of the model exists. Here, we use the +SBML model provided in the original publication, which is also available +on Biomodels (https://www.ebi.ac.uk/biomodels/BIOMD0000000591). For +illustration purposes we slightly modified the SBML model and shortened +some parts of the PEtab files. The full PEtab problem introduced in this +tutorial is available `online <https://github.com/PEtab-dev/PEtab/tree/main/doc/v2/tutorial>`_. + +.. figure:: gfx/tutorial_model.png + :width: 4.9846in + :height: 5.5634in + + Visualization of the model used as example in this tutorial. + The model describes the dynamics of phosphorylation and dimerization of the + transcription factors STAT5A and STAT5B. + +.. table:: Reactions included in the example model. + + == ========================== ============================================= + ID Reaction Rate law + == ========================== ============================================= + R1 2 STAT5A → pApA cyt \* BaF3_Epo \* STAT5A^2 \* k_phos + R2 STAT5A + STAT5B → pApB cyt \* BaF3_Epo \* STAT5A \* STAT5B \* k_phos + R3 2 STAT5B → pBpB cyt \* BaF3_Epo \* STAT5B^2 \* k_phos + R4 pApA → nucpApA cyt \* k_imp_homo \* pApA + R5 pApB → nucpApB cyt \* k_imp_hetero \* pApB + R6 pBpB → nucpBpB cyt \* k_imp_homo \* pBpB + R7 nucpApA → 2 STAT5A nuc \* k_exp_homo \* nucpApA + R8 nucpApB → STAT5A + STAT5B nuc \* k_exp_hetero \* nucpApB + R9 nucpBpB → 2 STAT5B nuc \* k_exp_homo \* nucpBpB + == ========================== ============================================= + +2. Linking model and measurements ++++++++++++++++++++++++++++++++++ + +The model by `Boehm et al. (2014)`_ was calibrated on measurements on +phosphorylation levels of STAT5A and STAT5B as well as relative STAT5A +abundance for different timepoints between 0 - 240 minutes after +stimulation with erythropoietin (Epo): + +.. figure:: gfx/tutorial_data.png + :width: 6.2681in + :height: 2.0835in + + Measurements considered for model calibration in our example. + +To define a parameter estimation problem in PEtab, we need to map +measurements to the model state. To this end, we need to 1) specify the +experimental conditions the measurements were generated from, 2) specify +observation functions and error models, and 3) specify the measurements +themselves. For this, we need to define observation functions as well as +experimental conditions under which a measurement was performed. + +2.1 Specifying experimental conditions +-------------------------------------- + +All measurements were collected under the same experimental condition, +which is a stimulation with Epo. In PEtab, we can define *experiments*, +which are characterized by specific *conditions* (here: discrete changes) +that are applied to the model at certain time points. + +In the problem considered here, the relevant the model parameter is +``Epo_concentration``, the initial concentration of Epo, which we want to set +to a value of 1.25E-7. Since in this example we include data from +only one single experiment, it would not be necessary to specify the condition +parameter here, but instead the value could have been also set in the model or +in the parameter table. However, the benefit of specifying this change as an +experiment is that it allows us to easily add measurements from other +experiments performed with different Epo concentrations later on. + +We define a single experiment in the PEtab experiment table, +a tab-separated values (TSV) file\ [#f1]_: + +.. table:: Experiment table ``experiments.tsv``. + + =============== ==== ================= + experimentId time conditionId + =============== ==== ================= + epo_stimulation 0.0 epo_bolus + =============== ==== ================= + +This means that in the experiment we call `epo_stimulation`, +at time point 0.0, the condition ``epo_bolus`` is applied to the model. +The condition itself is defined in the condition table, another TSV file, +below. + +The condition table specifies the discrete changes to model parameters or +model state that are applied when the respective condition is activated. +In our example, we only have one condition with a single change +that sets the Epo concentration to 1.25E-7: + +.. table:: Conditions table ``experimental_conditions.tsv``. + + =============== ============================ ================= + conditionId targetId targetValue + =============== ============================ ================= + epo_bolus Epo_concentration 1.25E-7 + =============== ============================ ================= + +In more complex scenarios, multiple conditions could be defined here, and +`targetValue` could contain more complex expressions. + +2.2 Specifying the observation model +------------------------------------ + +To link the model state to the measurements shown above, we specify observation +functions. Additionally, a noise model is be introduced to account for +the measurement errors. In PEtab, this is encoded in the observable +table: + +.. list-table:: Observable table ``observables.tsv``. + :header-rows: 1 + + * - observableId + - observableName + - ... + * - pSTAT5A_rel + - Rel. STAT5A phosphorylation [%] + - ... + * - pSTAT5B_rel + - Rel. STAT5B phosphorylation [%] + - ... + * - rSTAT5A_rel + - Rel. STAT5A abundance [%] + - ... + +.. list-table:: Observable table ``observables.tsv`` (continued). + :header-rows: 1 + + * - ... + - observableFormula + - ... + * - ... + - 100*(2*pApA + pApB) / (2*pApA + pApB + STAT5A) + - ... + * - ... + - 100*(2*pBpB + pApB) / (2*pBpB + pApB + STAT5B) + - ... + * - ... + - 100*(STAT5A + pApB + 2*pApA) / (2 \* pApB + 2\* pApA + STAT5A + STAT5B + 2*pBpB) + - ... + +.. list-table:: Observable table ``observables.tsv`` (continued). + :header-rows: 1 + + * - ... + - noiseFormula + - noisePlaceholders + - noiseDistribution + * - ... + - pSTAT5A_rel_sigma + - pSTAT5A_rel_sigma + - normal + * - ... + - pSTAT5B_rel_sigma + - pSTAT5B_rel_sigma + - normal + * - ... + - rSTAT5A_rel_sigma + - rSTAT5A_rel_sigma + - normal + +* *observableId* specifies a unique identifier to the observables that can + be used to link them to the measurements (see below). + +* *observableName* can be used as a human readable description of the + observable. + +* *observableFormula* is a mathematical expression defining how the model + output is calculated. The formula can consist of species and parameters + defined in the SBML file. In our example, we measure e.g. the relative + phosphorylation level of STAT5A (*pSTAT5A_rel*), which is the sum of all + species containing phosphorylated STAT5A over the sum of all species + containing any form of STAT5A. + +* *noiseFormula* is used to describe the formula for the measurement + noise. Together with *noiseDistribution*, it defines the noise model. In + this example, we assume additive, normally distributed measurement noise. + In this scenario, ``{observableId}_sigma`` is the standard + deviation of the measurement noise. Because we want to estimate the + standard deviation from the data, we parameterize it here. + Furthermore, we flag these parameters as placeholders in the + *noisePlaceholders* column, which allows us to substitute them with + specific values for each measurement in the measurement table (see below). + +2.3 Specifying measurements +--------------------------- + +The experimental data is linked to the experiments via the *experimentId* +and to the observables via the *observableId*. This is defined in the +PEtab measurement file: + +.. table:: Measurement table ``measurement_data.tsv``. + + ============ ===================== =========== ==== =============== + observableId experimentId measurement time noiseParameters + ============ ===================== =========== ==== =============== + pSTAT5A_rel epo_stimulation 7.9 0 sd_pSTAT5A_rel + ... ... ... ... ... + pSTAT5A_rel epo_stimulation 15.4 240 sd_pSTAT5A_rel + pSTAT5B_rel epo_stimulation 4.6 0 sd_pSTAT5B_rel + ... ... ... ... ... + pSTAT5B_rel epo_stimulation 10.96 240 sd_pSTAT5B_rel + rSTAT5A_rel epo_stimulation 14.7 0 sd_rSTAT5A_rel + ... ... ... ... ... + rSTAT5A_rel epo_stimulation 32.2 240 sd_rSTAT5A_rel + ============ ===================== =========== ==== =============== + +* *observableId* references the *observableId* from the observable file. + +* *experimentId* references the *experimentId* from the experiment file. + +* *measurement* defines the values that are measured for the respective + observable and experiment. + +* *time* is the time point at which the measurement was performed. For + brevity, only the first and last time point of the example are shown + here (the omitted measurements are indicated by “...” in the example). + +* *noiseParameters* relates to the *noiseParameters* in the observable table. + In our example, the measurement noise is unknown. Therefore we + specify parameters here which have to be estimated (see parameters sheet + below). If the noise is known, e.g. from multiple replicates, numeric + values can be specified in this column. + +3. Defining parameters +++++++++++++++++++++++ + +The model by `Boehm et al. (2014)`_ contains nine unknown parameters that +need to be estimated from the experimental data. Additionally, it has +one known parameter that is fixed to a literature value. + +The parameter table for this is given by: + +.. table:: Parameter table ``parameters.tsv``. + + ==================== ========== ========== ============ ======== + parameterId lowerBound upperBound nominalValue estimate + ==================== ========== ========== ============ ======== + Epo_degradation_BaF3 1E-5 1E+5 \ true + k_exp_hetero 1E-5 1E+5 \ true + k_exp_homo 1E-5 1E+5 \ true + k_imp_hetero 1E-5 1E+5 \ true + k_imp_homo 1E-5 1E+5 \ true + k_phos 1E-5 1E+5 \ true + ratio \ \ 0.693 false + sd_pSTAT5A_rel 1E-5 1E+5 \ true + sd_pSTAT5B_rel 1E-5 1E+5 \ true + sd_rSTAT5A_rel 1E-5 1E+5 \ true + ==================== ========== ========== ============ ======== + +* *parameterId* references parameters defined in the SBML file or introduced in + the condition table or the measurement table. + In this example, the first seven parameters are specified in the model, + and the last three parameters are the standard deviations for the different + observables (*sd_{observableId}*) that we introduced in the measurement + table. + +* *lowerBound* and *upperBound* define the bounds for the parameters used + during estimation. These are usually biologically plausible ranges. + +* *estimate* defines whether the parameter will be estimated (``true``) + or be fixed (``false``) to the value in the *nominalValue* column. + +* *nominalValue* are known values used for simulation. The entry can be + left empty, if a value is unknown and requires estimation. + +5. YAML file +++++++++++++ + +The parameter estimation problem is fully defined by the files created +above. However, to facilitate importing the problem into +tools supporting PEtab, a YAML file is used to group the files together. +This file has the following format (``Boehm_JProteomeRes2014.yaml``): + +.. code-block:: yaml + + format_version: 2.0.0 + model_files: + model: + location: model_Boehm_JProteomeRes2014.xml + language: sbml + parameter_files: + - parameters.tsv + experiment_files: + - experiments.tsv + condition_files: + - experimental_conditions.tsv + observable_files: + - observables.tsv + measurement_files: + - measurement_data.tsv + +The first line specifies the PEtab version this file and the files referenced +adhere to. The next block specifies the model file, in this case an SBML +file. This is followed by lists of the different PEtab files created above: +parameter, experiment, condition, observable, and measurement files. +Here, each list contains only one file, but multiple files can be +referenced if needed. + +7. Further information +++++++++++++++++++++++ + +This tutorial only demonstrates a subset of PEtab functionality. +For full reference, consult the +`PEtab specification <https://petab.readthedocs.io/en/latest/v2/documentation_data_format.html>`_. +After finishing the implementation of the PEtab problem, its correctness can +be verified using the ``petablint`` tool provided by the PEtab Python library +(`usage <https://petab.readthedocs.io/projects/libpetab-python/en/latest/example/example_petablint.html>`__). +The PEtab problem can then be used as input to the supporting toolboxes +to estimate the unknown parameters or calculate parameter uncertainties. + +.. [#f1] + TSV files can be created using any standard spreadsheet application, + or for small files, text editor. + + +References +++++++++++ + +* Martin E. Boehm, Lorenz Adlung, Marcel Schilling, Susanne Roth, + Ursula Klingmüller, and Wolf D. Lehmann. + *Journal of Proteome Research* **2014** 13 (12), 5685-5694. + DOI: `10.1021/pr5006923 <https://dx.doi.org/10.1021/pr5006923>`_.