GitHub

EGFR Network Benchmark

using JumpProcesses, Plots, StableRNGs, BenchmarkTools, ReactionNetworkImporters,
      StatsPlots, Catalyst

We will benchmark the aggregators of JumpProcesses on a epidermal growth factor receptor signaling network (356 species, 3749 reactions)[1], modeled using Catalyst.jl[2].

Let's first look at how the model equilibriates to make sure we get consistent results for all the methods. Let's plot the dimer concentration as a function of time using each of the different stochastic simulation algorithms.

tf = 12.0
rng = StableRNG(53124)
algs = [NRM(), CCNRM(), DirectCR(), RSSACR()]
egfr_net = complete(loadrxnetwork(BNGNetwork(), joinpath(@__DIR__, "Data/egfr_net.net")))
egfr_u0 = Catalyst.get_u0_map(egfr_net)
egfr_parameters = Catalyst.get_parameter_map(egfr_net)

plt = plot(title = "Dimer concentrations")
for alg in algs
    jprob = JumpProblem(
        egfr_net, egfr_u0, (0.0, tf), egfr_parameters;
        aggregator = alg, u0_eltype = Int64
    )
    sol = solve(jprob, SSAStepper(), saveat = tf/200)
    plot!(plt, sol, idxs = :Dimers, label = "$alg")
end
plot!(plt)
Scanning blocks...done
Parsing parameters...done
Creating parameters...done
Parsing species...done
Creating variables...done
Setting up expression bindings...done
Parsing groups...done
Parsing functions...done
Parsing and adding reactions...done

These results seem pretty reasonable - it seems like we're getting the same dimer concentration curve for each method.

Model Benchmark

We define a function to benchmark the model and then plot the results in a benchmark. The goal is to see how the SSAs perform relative to each other.

function benchmark_and_bar_plot(model, end_time, algs)
    times = Vector{Float64}()
    alg_names = ["$s"[15:(end - 2)] for s in algs]
    u0 = Catalyst.get_u0_map(model)
    parameters = Catalyst.get_parameter_map(model)

    benchmarks = Vector{BenchmarkTools.Trial}(undef, length(algs))
    for (i, alg) in enumerate(algs)
        alg_name = alg_names[i]
        println("Benchmarking $alg_name")
        jprob = JumpProblem(
            model, u0, (0.0, end_time), parameters;
            aggregator = alg, rng, save_positions = (false, false), u0_eltype = Int64
        )

        b = @benchmarkable solve($jprob; saveat = $end_time) samples=5 seconds=7200
        bm = run(b)
        push!(times, median(bm).time/1e9)
    end

    bar(alg_names, times, xlabel = "Algorithm", ylabel = "Average Time (s)",
        title = "SSA Runtime for EGFR network", legend = false)
end
benchmark_and_bar_plot (generic function with 1 method)

Now we benchmark the EGFR network on the four algorithms and plot the results.

tf = 12.0
rng = StableRNG(53124)
algs = [NRM(), CCNRM(), DirectCR(), RSSACR()]

plt = benchmark_and_bar_plot(egfr_net, tf, algs)
plt
Benchmarking NRM
Benchmarking CCNRM
Benchmarking DirectCR
Benchmarking RSSACR

References

Appendix

These benchmarks are a part of the SciMLBenchmarks.jl repository, found at: https://github.com/SciML/SciMLBenchmarks.jl. For more information on high-performance scientific machine learning, check out the SciML Open Source Software Organization https://sciml.ai.

To locally run this benchmark, do the following commands:

using SciMLBenchmarks
SciMLBenchmarks.weave_file("benchmarks/Jumps","EGFR_Benchmark.jmd")

Computer Information:

Julia Version 1.10.12
Commit d93beab124c (2026-08-15 10:29 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 128 × AMD EPYC 7502 32-Core Processor
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver2)
Threads: 128 default, 0 interactive, 64 GC (on 128 virtual cores)
Environment:
  JULIA_NUM_THREADS = auto

Package Information:

Status `~/github-runners/amdci3-1/_work/SciMLBenchmarks.jl/SciMLBenchmarks.jl/benchmarks/Jumps/Project.toml`
  [6e4b80f9] BenchmarkTools v1.8.0
  [479239e8] Catalyst v16.4.0
  [8f4d0f93] Conda v1.10.3
  [a93c6f00] DataFrames v1.8.2
  [0c46a032] DifferentialEquations v8.1.1
⌃ [31c24e10] Distributions v0.25.127
  [523d8e89] Gillespie v0.2.0
⌃ [86223c79] Graphs v1.14.0
  [faf0f6d7] JumpProblemLibrary v2.0.3
⌃ [ccbc3e58] JumpProcesses v9.32.0
⌃ [961ee093] ModelingToolkit v11.41.0
  [1dea7af3] OrdinaryDiffEq v7.8.1
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⌃ [31c91b34] SciMLBenchmarks v0.1.3
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⌃ [c3572dad] Sundials v6.6.0
  [2efcf032] SymbolicIndexingInterface v0.3.55
  [37e2e46d] LinearAlgebra
  [9a3f8284] Random
  [10745b16] Statistics v1.10.0
Info Packages marked with ⌃ have new versions available and may be upgradable.

And the full manifest:

Status `~/github-runners/amdci3-1/_work/SciMLBenchmarks.jl/SciMLBenchmarks.jl/benchmarks/Jumps/Manifest.toml`
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  [d7ba0133] Git v1.5.0
⌃ [86223c79] Graphs v1.14.0
⌅ [eafb193a] Highlights v0.5.3
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  [3587e190] InverseFunctions v0.1.17
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⌃ [7771a370] ModelingToolkitBase v1.68.2
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  [975044d2] Xorg_xcb_util_keysyms_jll v0.4.1+0
  [0d47668e] Xorg_xcb_util_renderutil_jll v0.3.10+0
  [c22f9ab0] Xorg_xcb_util_wm_jll v0.4.2+0
  [35661453] Xorg_xkbcomp_jll v1.4.7+0
  [33bec58e] Xorg_xkeyboard_config_jll v2.47.0+2
  [c5fb5394] Xorg_xtrans_jll v1.6.0+0
  [8f1865be] ZeroMQ_jll v4.3.6+0
  [3161d3a3] Zstd_jll v1.5.7+1
  [35ca27e7] eudev_jll v3.2.14+0
⌅ [214eeab7] fzf_jll v0.61.1+0
  [a4ae2306] libaom_jll v3.14.1+0
  [0ac62f75] libass_jll v0.17.5+0
  [1183f4f0] libdecor_jll v0.2.2+0
  [8e53e030] libdrm_jll v2.4.134+0
  [2db6ffa8] libevdev_jll v1.13.4+0
  [f638f0a6] libfdk_aac_jll v2.0.4+0
  [36db933b] libinput_jll v1.28.1+0
  [b53b4c65] libpng_jll v1.6.58+0
  [a9144af2] libsodium_jll v1.0.21+0
  [9a156e7d] libva_jll v2.23.0+0
  [f27f6e37] libvorbis_jll v1.3.8+0
  [009596ad] mtdev_jll v1.1.7+0
  [1317d2d5] oneTBB_jll v2022.3.0+0
⌅ [1270edf5] x264_jll v10164.0.1+0
  [dfaa095f] x265_jll v4.1.0+0
  [d8fb68d0] xkbcommon_jll v1.13.0+0
  [0dad84c5] ArgTools v1.1.1
  [56f22d72] Artifacts
  [2a0f44e3] Base64
  [ade2ca70] Dates
  [8ba89e20] Distributed
  [f43a241f] Downloads v1.6.0
  [7b1f6079] FileWatching
  [9fa8497b] Future
  [b77e0a4c] InteractiveUtils
  [4af54fe1] LazyArtifacts
  [b27032c2] LibCURL v0.6.4
  [76f85450] LibGit2
  [8f399da3] Libdl
  [37e2e46d] LinearAlgebra
  [56ddb016] Logging
  [d6f4376e] Markdown
  [a63ad114] Mmap
  [ca575930] NetworkOptions v1.2.0
  [44cfe95a] Pkg v1.10.0
  [de0858da] Printf
  [9abbd945] Profile
  [3fa0cd96] REPL
  [9a3f8284] Random
  [ea8e919c] SHA v0.7.0
  [9e88b42a] Serialization
  [1a1011a3] SharedArrays
  [6462fe0b] Sockets
  [2f01184e] SparseArrays v1.10.0
  [10745b16] Statistics v1.10.0
  [4607b0f0] SuiteSparse
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test
  [cf7118a7] UUIDs
  [4ec0a83e] Unicode
  [e66e0078] CompilerSupportLibraries_jll v1.1.2+1
  [deac9b47] LibCURL_jll v8.4.0+0
  [e37daf67] LibGit2_jll v1.6.4+0
  [29816b5a] LibSSH2_jll v1.11.0+1
  [c8ffd9c3] MbedTLS_jll v2.28.1010+0
  [14a3606d] MozillaCACerts_jll v2025.12.2
  [4536629a] OpenBLAS_jll v0.3.23+5
  [05823500] OpenLibm_jll v0.8.5+0
  [efcefdf7] PCRE2_jll v10.42.0+1
  [bea87d4a] SuiteSparse_jll v7.2.1+1
  [83775a58] Zlib_jll v1.2.13+1
  [8e850b90] libblastrampoline_jll v5.11.0+0
  [8e850ede] nghttp2_jll v1.52.0+1
  [3f19e933] p7zip_jll v17.6.1+0
Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`
  • 1Blinov ML, Faeder JR, Goldstein B, Hlavacek WS. A network model of early events in epidermal growth factor receptor signaling that accounts for combinatorial complexity.
  • 2Loman TE, Ma Y, Ilin V, et al. Catalyst: Fast and flexible modeling of reaction networks.