DataDrivenEnzymeRateEqs
Documentation for DataDrivenEnzymeRateEqs.
DataDrivenEnzymeRateEqs.calculate_all_parameter_removal_codes
DataDrivenEnzymeRateEqs.data_driven_rate_equation_selection
DataDrivenEnzymeRateEqs.fit_rate_equation
DataDrivenEnzymeRateEqs.forward_selection_next_param_removal_codes
DataDrivenEnzymeRateEqs.loss_rate_equation
DataDrivenEnzymeRateEqs.param_rescaling
DataDrivenEnzymeRateEqs.param_subset_select
DataDrivenEnzymeRateEqs.reverse_selection_next_param_removal_codes
DataDrivenEnzymeRateEqs.test_rate_equation
DataDrivenEnzymeRateEqs.calculate_all_parameter_removal_codes
— MethodGenerate all possibles codes for ways that mirror params for a and i states of MWC enzyme can be removed from the rate equation
DataDrivenEnzymeRateEqs.data_driven_rate_equation_selection
— Methoddatadrivenrateequationselection( generalrateequation::Function, data::DataFrame, metabnames::Tuple{Symbol,Vararg{Symbol}}, paramnames::Tuple{Symbol,Vararg{Symbol}}, rangenumberparams::Tuple{Int,Int}, forwardmodelselection::Bool, )
This function is used to perform data-driven rate equation selection using a general rate equation and data. The function will select the best rate equation by iteratively removing parameters from the general rate equation and finding an equation that yield best test scores on data not used for fitting.
Arguments
general_rate_equation::Function
: Function that takes a NamedTuple of metabolite concentrations (withmetab_names
keys) and parameters (withparam_names
keys) and returns an enzyme rate.data::DataFrame
: DataFrame containing the data with columnRate
and columns for eachmetab_names
where each row is one measurement. It also needs to have a columnsource
that contains a string that identifies the source of the data. This is used to calculate the weights for each figure in the publication.metab_names::Tuple
: Tuple of metabolite names that correspond to the metabolites ofrate_equation
and column names indata
.param_names::Tuple
: Tuple of parameter names that correspond to the parameters ofrate_equation
.range_number_params::Tuple{Int,Int}
: A tuple of integers representing the range of the number of parameters of generalrateequation to search over.forward_model_selection::Bool
: A boolean indicating whether to use forward model selection (true) or reverse model selection (false).
Returns nothing, but saves a csv file for each num_params
with the results of the training for each combination of parameters tested and a csv file with test results for top 10% of the best results with each number of parameters tested.
DataDrivenEnzymeRateEqs.fit_rate_equation
— Methodfit_rate_equation(
+Home · DataDrivenEnzymeRateEqs.jl DataDrivenEnzymeRateEqs
Documentation for DataDrivenEnzymeRateEqs.
DataDrivenEnzymeRateEqs.calculate_all_parameter_removal_codes
DataDrivenEnzymeRateEqs.data_driven_rate_equation_selection
DataDrivenEnzymeRateEqs.fit_rate_equation
DataDrivenEnzymeRateEqs.forward_selection_next_param_removal_codes
DataDrivenEnzymeRateEqs.loss_rate_equation
DataDrivenEnzymeRateEqs.param_rescaling
DataDrivenEnzymeRateEqs.param_subset_select
DataDrivenEnzymeRateEqs.reverse_selection_next_param_removal_codes
DataDrivenEnzymeRateEqs.test_rate_equation
DataDrivenEnzymeRateEqs.calculate_all_parameter_removal_codes
— MethodGenerate all possibles codes for ways that mirror params for a and i states of MWC enzyme can be removed from the rate equation
sourceDataDrivenEnzymeRateEqs.data_driven_rate_equation_selection
— Methoddatadrivenrateequationselection( generalrateequation::Function, data::DataFrame, metabnames::Tuple{Symbol,Vararg{Symbol}}, paramnames::Tuple{Symbol,Vararg{Symbol}}, rangenumberparams::Tuple{Int,Int}, forwardmodelselection::Bool, )
This function is used to perform data-driven rate equation selection using a general rate equation and data. The function will select the best rate equation by iteratively removing parameters from the general rate equation and finding an equation that yield best test scores on data not used for fitting.
Arguments
general_rate_equation::Function
: Function that takes a NamedTuple of metabolite concentrations (with metab_names
keys) and parameters (with param_names
keys) and returns an enzyme rate.data::DataFrame
: DataFrame containing the data with column Rate
and columns for each metab_names
where each row is one measurement. It also needs to have a column source
that contains a string that identifies the source of the data. This is used to calculate the weights for each figure in the publication.metab_names::Tuple
: Tuple of metabolite names that correspond to the metabolites of rate_equation
and column names in data
.param_names::Tuple
: Tuple of parameter names that correspond to the parameters of rate_equation
.range_number_params::Tuple{Int,Int}
: A tuple of integers representing the range of the number of parameters of generalrateequation to search over.forward_model_selection::Bool
: A boolean indicating whether to use forward model selection (true) or reverse model selection (false).
Returns nothing, but saves a csv file for each num_params
with the results of the training for each combination of parameters tested and a csv file with test results for top 10% of the best results with each number of parameters tested.
sourceDataDrivenEnzymeRateEqs.fit_rate_equation
— Methodfit_rate_equation(
rate_equation::Function,
data::DataFrame,
metab_names::Tuple,
@@ -11,4 +11,4 @@
source = ["Figure 1", "Figure 1", "Figure 2"]
)
rate_equation(metabs, params) = params.Vmax * metabs.S / (1 + metabs.S / params.K_S)
-fit_rate_equation(rate_equation, data, (:A,), (:Vmax, :K_S))
sourceDataDrivenEnzymeRateEqs.forward_selection_next_param_removal_codes
— MethodCalculate nt_param_removal_codes
with num_params
including non-zero term combinations for codes (excluding alpha terms) in each previous_param_removal_codes
that has num_params-1
sourceDataDrivenEnzymeRateEqs.loss_rate_equation
— MethodLoss function used for fitting that calculate log of ratio of rate equation predicting of rate and rate data
sourceDataDrivenEnzymeRateEqs.param_rescaling
— MethodRescaling of fitting parameters from [0, 10] scale that optimizer uses to actual values
sourceDataDrivenEnzymeRateEqs.param_subset_select
— MethodFunction to convert parameter vector to vector where some params are equal to 0, Inf or each other based on ntparamremoval_code
sourceDataDrivenEnzymeRateEqs.reverse_selection_next_param_removal_codes
— MethodCalculate param_removal_codes
with num_params
including zero term combinations for codes (excluding alpha terms) in each previous_param_removal_codes
that has num_params+1
sourceDataDrivenEnzymeRateEqs.test_rate_equation
— MethodFunction to calculate loss for a given rate_equation
and nt_fitted_params
on data
that was not used for training
sourceSettings
This document was generated with Documenter.jl version 1.2.1 on Thursday 29 February 2024. Using Julia version 1.10.1.
+fit_rate_equation(rate_equation, data, (:A,), (:Vmax, :K_S))
DataDrivenEnzymeRateEqs.forward_selection_next_param_removal_codes
— MethodCalculate nt_param_removal_codes
with num_params
including non-zero term combinations for codes (excluding alpha terms) in each previous_param_removal_codes
that has num_params-1
DataDrivenEnzymeRateEqs.loss_rate_equation
— MethodLoss function used for fitting that calculate log of ratio of rate equation predicting of rate and rate data
DataDrivenEnzymeRateEqs.param_rescaling
— MethodRescaling of fitting parameters from [0, 10] scale that optimizer uses to actual values
DataDrivenEnzymeRateEqs.param_subset_select
— MethodFunction to convert parameter vector to vector where some params are equal to 0, Inf or each other based on ntparamremoval_code
DataDrivenEnzymeRateEqs.reverse_selection_next_param_removal_codes
— MethodCalculate param_removal_codes
with num_params
including zero term combinations for codes (excluding alpha terms) in each previous_param_removal_codes
that has num_params+1
DataDrivenEnzymeRateEqs.test_rate_equation
— MethodFunction to calculate loss for a given rate_equation
and nt_fitted_params
on data
that was not used for training