Parameter Estimation Methods

The main function of PEtab.jl is to create parameter estimation problems and provide runtime-efficient gradient and Hessian functions for estimating unknown model parameters using suitable numerical optimization algorithms. Specifically, the parameter estimation problems considered by PEtab.jl are on the form:

\[\min_{\mathbf{x} \in \mathbb{R}^N} -\ell(\mathbf{x}), \quad \text{subject to} \\ \mathbf{lb} \leq \mathbf{x} \leq \mathbf{ub}\]

Where, since PEtab.jl works with likelihoods (see the API documentation on PEtabObservable), $-\ell(\mathbf{x})$ is a negative log-likelihood, and $\mathbf{lb}$ and $\mathbf{ub}$ are the lower and upper parameter bounds. For a good introduction to parameter estimation for ODE models in biology (which is applicable to other fields as well), see [9].

This advanced section of the documentation focuses on PEtab.jl's parameter estimation functionality, and before reading this part, we recommended the starting tutorial. Specifically, this section of the documentation covers available and recommended optimization algorithms, how to plot optimization results, and how to perform automatic model selection. First though, it covers how to perform parameter estimation. While the PEtabODEProblem contains all the necessary information for wrapping a suitable optimizer to solve the problem (see here), manually wrapping optimizers is cumbersome. Therefore, PEtab.jl provides convenient wrappers for:

• Single-start parameter estimation
• Multi-start parameter estimation
• Creating an OptimizationProblem to access the solvers in Optimization.jl

As a working example, we use the Michaelis-Menten enzyme kinetics model from the starting tutorial. Even though the code below presents the model as a ReactionSystem, everything works the same if the model is provided as an ODESystem.

using Catalyst, PEtab

# Create the dynamic model
t = default_t()
rn = @reaction_network begin
    @parameters S0 c3=1.0
    @species S(t)=S0
    c1, S + E --> SE
    c2, SE --> S + E
    c3, SE --> P + E
end
speciemap = [:E => 50.0, :SE => 0.0, :P => 0.0]

# Observables
@unpack E, S = rn
obs_sum = PEtabObservable(S + E, 3.0)
@unpack P = rn
@parameters sigma
obs_p = PEtabObservable(P, sigma)
observables = Dict("obs_p" => obs_p, "obs_sum" => obs_sum)

# Parameters to estimate
p_c1 = PEtabParameter(:c1)
p_c2 = PEtabParameter(:c2)
p_s0 = PEtabParameter(:S0)
p_sigma = PEtabParameter(:sigma)
pest = [p_c1, p_c2, p_s0, p_sigma]

# Simulate measurement data with 'true' parameters
using OrdinaryDiffEq, DataFrames
ps = [:c1 => 1.0, :c2 => 10.0, :c3 => 1.0, :S0 => 100.0]
u0 = [:S => 100.0, :E => 50.0, :SE => 0.0, :P => 0.0]
tspan = (0.0, 10.0)
oprob = ODEProblem(rn, u0, tspan, ps)
sol = solve(oprob, Rodas5P(); saveat = 0:0.5:10.0)
obs_sum = (sol[:S] + sol[:E]) .+ randn(length(sol[:E]))
obs_p = sol[:P] + .+ randn(length(sol[:P]))
df_sum = DataFrame(obs_id = "obs_sum", time = sol.t, measurement = obs_sum)
df_p = DataFrame(obs_id = "obs_p", time = sol.t, measurement = obs_p)
measurements = vcat(df_sum, df_p)

model = PEtabModel(rn, observables, measurements, pest; speciemap = speciemap)
petab_prob = PEtabODEProblem(model)

Single-Start Parameter Estimation

Single-start parameter estimation is an approach where a numerical optimization algorithm is run from a starting point x0 until it hopefully reaches a local minimum. When performing parameter estimation, the objective function generated by a PEtabODEProblem expects the parameters to be in a specific order. The most straightforward way to obtain a correctly ordered vector is via the get_x function:

get_x(prob::PEtabODEProblem; linear_scale = false)::ComponentArray

For our working example, we have:

x0 = get_x(petab_prob)

ComponentVector{Float64}(log10_c1 = 2.6989704386302837, log10_c2 = 2.6989704386302837, log10_S0 = 2.6989704386302837, log10_sigma = 2.6989704386302837)

As discussed in the starting tutorial, x0 is a ComponentArray, meaning it holds both parameter values and names. Additionally, parameters like c1 have a log10 prefix, as the parameter (by default) is estimated on the log10 scale, which typically improves performance [2, 3]. Interacting with a ComponentArray is straightforward, for example, to change c1 to 10.0 do:

x0.log10_c1 = log10(10.0)

or alteratively:

x0[:log10_c1] = log10(10.0)

Given a starting point, parameter estimation can be performed with the calibrate function:

calibrate(prob::PEtabODEProblem, x0, alg; kwargs...)::PEtabOptimisationResult

For information and recommendations on algorithms (alg), see this page. For our working example, following the recommendations, we use Optim.jl's Interior-Point Newton method (IPNewton):

using Optim
res = calibrate(petab_prob, x0, IPNewton())

PEtabOptimisationResult
---------------- Summary ---------------
min(f)                = 7.58e+01
Parameters estimated  = 4
Optimiser iterations  = 23
Runtime               = 6.0e-01s
Optimiser algorithm   = Optim_IPNewton

The result from calibrate is returned as a PEtabOptimisationResult which holds the relevant statistics from the optimization:

PEtabOptimisationResult

The result from calibrate can also be plotted. For example, to see how well the model fits the data, the fit can be plotted as:

using Plots
plot(res, petab_prob)

Information on other available plots can be found on this page. Now, even though the plot above may look good, it is important to remember that the objective function ($-\ell$ above) often has multiple local minima. To ensure the global optimum is found, a global optimization approach is needed. One effective global method is multi-start parameter estimation.

Multi-Start Parameter Estimation

Multi-start parameter estimation is an approach where n parameter estimation runs are initiated from n random starting points. The rationale is that a subset of these runs should, hopefully, converge to the global optimum. While simple, empirical benchmark studies have shown that this method performs well for ODE models in biology [2, 2].

The first step for multi-start parameter estimation is to generate n starting points. While random uniform sampling may initially seem like a good approach, random points tend to cluster. Instead, it's better to use a Quasi-Monte Carlo method, such as Latin hypercube sampling, to generate more spread-out starting points. This approach has been shown to improve performance [2]. The difference can quite clearly be seen generating 100 random points and 50 Latin hypercube-sampled points on the plane.

using Distributions, QuasiMonteCarlo, Plots
s1 = QuasiMonteCarlo.sample(100, [-1.0, -1.0], [1.0, 1.0], Uniform())
s2 = QuasiMonteCarlo.sample(100, [-1.0, -1.0], [1.0, 1.0], LatinHypercubeSample())
p1 = plot(s1[1, :], s1[2, :], title = "Uniform sampling", seriestype=:scatter)
p2 = plot(s2[1, :], s2[2, :], title = "Latin Hypercube Sampling", seriestype=:scatter)
plot(p1, p2)

For a PEtabODEProblem, Latin hypercube sampled points within the parameter bounds can be generated with the get_startguesses function:

get_startguesses(prob::PEtabODEProblem, n::Integer; kwargs...)

For our working example, we can generate 50 starting guesses with:

x0s = get_startguesses(petab_prob, 50)

In principle, x0s can now be used together with calibrate to perform multi-start parameter estimation. But, to further simplify this process, PEtab.jl provides a convenient function, calibrate_multistart, which combines start-guess generation and parameter estimation in one step:

calibrate_multistart(prob::PEtabODEProblem, alg, nmultistarts::Integer; nprocs = 1,
                     dirsave=nothing, kwargs...)::PEtabMultistartResult

Two important keyword arguments for calibrate_multistart are dirsave and nprocs. If nprocs > 1, the parameter estimation runs are performed in parallel using the pmap function from Distributed.jl with nprocs processes. Even though pmap introduces some overhead because it must load and compile the code on each process, setting nprocs > 1 often reduces runtime when the parameter estimation is expected to take longer than 5 minutes. Meanwhile, dirsave specifies an optional directory to continuously save the results from each individual run. We strongly recommend providing such a directory, as parameter estimation for larger models can take hours or even days. If something goes wrong with the computer during that time, it is, to put it mildly, frustrating to lose all the results. For our working example, we can perform 50 multistarts in parallel on two processes with:

ms_res = calibrate_multistart(petab_prob, IPNewton(), 50; nprocs = 2,
                              dirsave="path_to_save_directory")

PEtabMultistartResult
---------------- Summary ---------------
min(f)                = 7.58e+01
Parameters estimated  = 4
Number of multistarts = 50
Optimiser algorithm   = Optim_IPNewton
Results saved at path_to_save_directory

The results are returned as a PEtabMultistartResult, which, in addition to printout statistics, contains relevant information for each run:

PEtabMultistartResult

PEtabMultistartResult(dirres::String; which_run::String="1")

Finally, a common approach to evaluate the result of multi-start parameter estimation is through plotting. One widely used evaluation plot is the waterfall plot, which shows the final objective values for each run:

plot(ms_res; plot_type=:waterfall)

In the waterfall plot, each plateau corresponds to different local optima (represented by different colors). Since many runs (dots) are found on the plateau with the smallest objective value, we can be confident that the global optimum has been found. In addition to waterfall plots, more plotting options can be found on this page.

Creating an OptimizationProblem

Optimization.jl is a Julia package that provides a unified interface for over 100 optimization algorithms (see their documentation for the complete list). While Optimization.jl is undoubtedly useful, it is currently undergoing heavy updates, so at the moment we do not recommend it as the default choice for parameter estimation.

The central object in Optimization.jl is the OptimizationProblem, and PEtab.jl directly supports converting a PEtabODEProblem into an OptimizationProblem:

OptimizationProblem(prob::PEtabODEProblem; box_constraints::Bool = true)

For our working example, we can create an OptimizationProblem with:

using Optimization
opt_prob = OptimizationProblem(petab_prob)

OptimizationProblem. In-place: true
u0: ComponentVector{Float64}(log10_c1 = 1.0, log10_c2 = 2.6989704386302837, log10_S0 = 2.6989704386302837, log10_sigma = 2.6989704386302837)

Given a start-guess x0, we can then estimate the parameters using, for example, Optim.jl's ParticleSwarm() method, with:

using OptimizationOptimJL
opt_prob.u0 .= x0
res = solve(opt_prob, Optim.ParticleSwarm())

retcode: Failure
u: ComponentVector{Float64}(log10_c1 = 1.9645946199603268, log10_c2 = 3.0, log10_S0 = 1.9998090306762408, log10_sigma = 0.06291465794443092)

which returns an OptimizationSolution. For more information on options and how to interact with OptimizationSolution, see the Optimization.jl documentation.

References