This package is under rapid development. Expect frequent breaking changes.
DataDrivenEnzymeRateEqs.jl is a Julia package for identifying metabolic enzyme rate equations based on in vitro kinetic data.
The user provides the following info about enzyme:
- names of substrates, products, and regulators of the enzyme
- number of metabolite binding sites and list of metabolites bound to each site
- oligomeric state of the enzyme
- in vitro kinetic data
DataDrivenEnzymeRateEqs.jl outputs the following info about enzyme:
- enzyme rate equation based on the Monod-Wyman-Changeux model that is most consistent with the user provided data (i.e., rate equation that is best at predicting in vitro kinetic data not used for fitting measured with lowest leave one out crossvalidation score)
- kinetic parameters and their uncertainty (work in progress)
using DataDrivenEnzymeRateEqs, DataFrames
data_gen_rate_equation_Keq = 1.0
data_gen_rate_equation(metabs, params) = params.Vmax * (metabs.S / params.K_S - (1 / data_gen_rate_equation_Keq) * metabs.P / params.K_P) / (1 + metabs.S / params.K_S + metabs.P / params.K_P)
param_names = (:Vmax, :K_S, :K_P)
metab_names = (:S, :P)
params = (Vmax=10.0, K_S=1e-3, K_P=5e-3)num_datapoints = 10
num_figures = 4
S_concs = Float64[]
P_concs = Float64[]
sources = String[]
for i in 1:num_figures
if i < num_figures ÷ 2
for S in range(0, rand(1:10) * params.K_S, rand(num_datapoints ÷ 2 : num_datapoints * 2))
push!(S_concs, S)
push!(P_concs, 0.0)
push!(sources, "Figure$i")
end
else
for P in range(0, rand(1:10) * params.K_P, rand(num_datapoints ÷ 2 : num_datapoints * 2))
push!(S_concs, 0.0)
push!(P_concs, P)
push!(sources, "Figure$i")
end
end
end
data = DataFrame(S=S_concs, P=P_concs, source=sources)
noise_sd = 0.2
data.Rate = [data_gen_rate_equation(row, params) * (1 + noise_sd * randn()) for row in eachrow(data)]Use the data above to identify the rate equation and check that it is same as data_gen_rate_equation
#Define enzyme
enzyme_parameters = (; substrates=[:S,], products=[:P], regulators=[], Keq=1.0, oligomeric_state=1, rate_equation_name=:mwc_derived_rate_equation)
metab_names, param_names = @derive_general_mwc_rate_eq(enzyme_parameters)
#Find the best rate equation
mwc_derived_rate_equation_no_Keq(nt_metabs, nt_params) = mwc_derived_rate_equation(nt_metabs, nt_params, enzyme_parameters.Keq)
selection_result = @time data_driven_rate_equation_selection(mwc_derived_rate_equation_no_Keq, data, metab_names, param_names)
#Display best equation with 3 parameters. Compare sym_rate_equation with data_gen_rate_equation with Vmax=1
nt_param_removal_code = filter(x -> x.num_params .== 3, selection_result.test_results).nt_param_removal_codes[1]
sym_rate_equation = display_rate_equation(mwc_derived_rate_equation, metab_names, param_names; nt_param_removal_code=nt_param_removal_code)Check the Documentation for more info
- add plotting functions
- add function for bootstrapping for calculation of kinetic parameter uncertainty based on log-normal distribution
- update rate equations based on Quasy-Steady-State approximation to choose actual kinetic constants instead of aggregate constants like K_S1_P1_P2