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benchmarks/Jumps/BCR_Benchmark.jmd

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using JumpProcesses, Plots, StableRNGs, Random, BenchmarkTools, ReactionNetworkImporters, StatsPlots, Catalyst
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```
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# Model Benchmark
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We will benchmark the aggregators of JumpProcesses on a B-cell receptor network (1122 species, 24388 reactions).[^1]
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# Model Benchmark
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We define a function to benchmark the model and then plot the results in a benchmark.
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```julia
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function benchmark_and_bar_plot(model, end_time, algs)
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benchmark_and_bar_plot(BCR_net, tf, algs)
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```
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### References
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[^1]: Barua D, Hlavacek WS, Lipniacki T. A Computational Model for Early Events in B Cell Antigen Receptor Signaling: Analysis of the Roles of Lyn and Fyn.
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[^2]: Loman TE, Ma Y, Ilin V, et al. Catalyst: Fast and flexible modeling of reaction networks.
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```julia, echo = false
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using SciMLBenchmarks
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SciMLBenchmarks.bench_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file])
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```

benchmarks/Jumps/EGFR_Benchmark.jmd

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using JumpProcesses, Plots, StableRNGs, Random, BenchmarkTools, ReactionNetworkImporters, StatsPlots, Catalyst
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```
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# Model Benchmark
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We will benchmark the aggregators of JumpProcesses on a epidermal growth factor receptor signaling network (356 species, 3749 reactions).[^1]
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# Model Benchmark
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We define a function to benchmark the model and then plot the results in a benchmark.
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```julia
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function benchmark_and_bar_plot(model, end_time, algs)
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benchmark_and_bar_plot(EGFR_net, tf, algs)
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```
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### References
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[^1]: Blinov ML, Faeder JR, Goldstein B, Hlavacek WS. A network model of early events in epidermal growth factor receptor signaling that accounts for combinatorial complexity.
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[^2]: Loman TE, Ma Y, Ilin V, et al. Catalyst: Fast and flexible modeling of reaction networks.
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```julia, echo = false
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using SciMLBenchmarks
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SciMLBenchmarks.bench_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file])
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```

benchmarks/Jumps/Fceri_gamma2_Benchmark.jmd

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using JumpProcesses, Plots, StableRNGs, Random, BenchmarkTools, ReactionNetworkImporters, StatsPlots, Catalyst
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```
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# Model Benchmark
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We will benchmark the aggregators of JumpProcesses on a human IgE receptor signaling network (3744 species, 58276 reactions).[^1, ^2]
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# Model Benchmark
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We define a function to benchmark the model and then plot the results in a benchmark.
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```julia
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function benchmark_and_bar_plot(model, end_time, algs)
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benchmark_and_bar_plot(fceri_gamma2_net, tf, algs)
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```
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### References
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[^1]: Faeder JR, Hlavacek WS, Reischl I, Blinov ML, Metzger H, Redondo A, et al. Investigation of Early Events in FcεRI-Mediated Signaling Using a Detailed Mathematical Model.
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[^2]: Loman TE, Ma Y, Ilin V, et al. Catalyst: Fast and flexible modeling of reaction networks.
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```julia, echo = false
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using SciMLBenchmarks
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SciMLBenchmarks.bench_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file])
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```

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