EnzymeRates.jl

Identify the best enzyme rate equation from kinetic data. Given a reaction definition and experimental rate measurements at varying substrate, product, and regulator concentrations, EnzymeRates enumerates biochemically valid mechanisms, fits each to the data, and selects the simplest mechanism that generalizes by leave-one-group-out cross-validation. It has first-class support for MWC allostery and for mechanisms that mix steady-state and rapid-equilibrium elementary steps, and it derives Haldane/Wegscheider thermodynamic constraints automatically from the mechanism's cycle structure.

Installation

using Pkg
Pkg.add(url="https://github.com/DenisTitovLab/EnzymeRates.jl")

Quickstart

Define a mechanism, derive its symbolic rate equation, and evaluate it:

using EnzymeRates

m = @enzyme_mechanism begin
    substrates: S
    products:   P
    steps: begin
        E + S ⇌ E(S)
        E(S) <--> E(P)
        E(P) ⇌ E + P
    end
end

parameters(m)                              # parameter names to supply
print(rate_equation_string(m))             # the symbolic rate equation
rate_equation(m, (S=1e-4, P=1e-5),        # evaluate numerically
    (K_P_E=1e-5, K_S_E=1e-4, k_ES_to_EP=100.0, Keq=2.0, E_total=1.0))

Or identify the best mechanism directly from measured rates. Provide a data file with a group column, a Rate column, and one column per metabolite:

using EnzymeRates, OptimizationCMAEvolutionStrategy, CSV

table = CSV.File("rate_data.csv")          # ← provide your own data file
data  = (group = table.group, Rate = table.Rate, S = table.S, P = table.P)

rxn = @enzyme_reaction begin
    substrates: S[C]
    products:   P[C]
end

prob    = IdentifyRateEquationProblem(rxn, data; Keq = 2.0)
results = identify_rate_equation(prob; optimizer = CMAEvolutionStrategyOpt())

results.best                               # the identified mechanism
print(rate_equation_string(results.best))  # its rate equation

Documentation

Full documentation — tutorials for deriving, fitting, and identifying rate equations, plus the architecture and API reference — lives at the documentation site:

• Getting Started — the end-to-end define → derive → fit → identify arc.
• Deriving rate equations — RE vs steady state, the Cha/King–Altman algorithm, thermodynamic constraints, ping-pong, and MWC allostery.
• Fitting rate equations — the data format, normalized vs absolute rate, and optimizer choice.
• Identifying the best rate equation — the enumeration engine, beam search, and cross-validation model selection.
• API Reference — every exported type, macro, and function.