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Merge pull request #10 from akutuva21/master
Migrating to ruleworld
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.github/workflows/validate.yml

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name: Validate RuleHub Metadata
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on:
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pull_request:
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push:
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branches:
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- master
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- main
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jobs:
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validate:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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- name: Setup Node
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uses: actions/setup-node@v4
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with:
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node-version: '20'
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- name: Validate metadata
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run: node scripts/validate-metadata.js
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- name: Regenerate manifest
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run: node scripts/generate-manifest.js --root . --output manifest.generated.json
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- name: Check manifest is up to date
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run: diff -u manifest.json manifest.generated.json

AddingModels.md

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2. Name is determined by either first author or the contributor, followed by the year of publication or contribution. If there is already a model in either section with that name, add another descriptive keyword (must start with a letter not a number) to the name, e.g., Blinov2006EGFR.
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3. Make a directory with the name of the model in the corresponding section.
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4. Put all model files and (optionally) associated scripts in this directory.
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5. Create a README.md file in the directory based on the template provided in the corresponding model section (Published or Contributed).
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6. If there are multiple model files/scripts, provide a table in the README.md file that gives a brief description of each.
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7. (optional) Provide additional annotation in each model file based on the annotation keywords provided in ...
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5. Create a metadata.yaml file in the directory following the field names documented in metadata-schema.yaml.
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6. Create a README.md file in the directory based on the template provided in the corresponding model section (Published or Contributed).
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7. If there are multiple model files/scripts, provide a table in the README.md file that gives a brief description of each.
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8. If there are multiple BNGL model files in a single directory, include a collection section in metadata.yaml describing the variant type and count.
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9. Run node scripts/validate-metadata.js and node scripts/generate-manifest.js --root . --output manifest.json before opening a pull request.
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10. (optional) Provide additional annotation in each model file based on the annotation keywords provided in ...
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## Guidelines for journal articles linking to RuleHub
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# akt signaling
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Signaling rates
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## Provenance
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This BNGL example was generated in BNG Playground and curated by Achyudhan.
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No external literature citation is associated with this model unless one is added manually.
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## Compatibility
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- BNG2 compatible: yes
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- Simulation methods: ode
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- Imported from: ai-generated
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## Files
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- akt-signaling.bngl
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## Tags
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akt, signaling, growthfactor, rtk, pi3k, mtorc2, mtorc1, s6k
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begin model
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begin parameters
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# Signaling rates
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k_bind 1e-3 # RTK-GF binding
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k_unbind 1e-4 # GF dissociation
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k_rtk_act 0.5 # Autophosphorylation
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k_pi3k 0.2 # PI3K recruitment/activation
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k_akt_t308 0.5 # PDK1-mediated T308 phos
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k_akt_s473 0.3 # mTORC2-mediated S473 phos
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k_mtorc1 0.4 # Akt-mediated mTORC1 activation
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k_s6k_act 0.2 # mTORC1-mediated S6K phos
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# Negative feedback / resetting
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k_reset 0.1 # General phosphatase/deactivation
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k_fb 0.05 # S6K-mediated inhibition of RTK signaling
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# Total concentrations
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GF_tot 100
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RTK_tot 50
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PI3K_tot 40
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AKT_tot 100
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mTORC2_tot 20
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mTORC1_tot 50
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S6K_tot 30
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end parameters
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begin molecule types
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GrowthFactor(r)
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RTK(l,state~U~P,fb_site~open~closed)
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PI3K(state~off~on)
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# Akt has two critical phosphorylation sites
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AKT(t308~U~P,s473~U~P)
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mTORC2(state~on)
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mTORC1(state~off~on)
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S6K(state~U~P)
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end molecule types
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begin seed species
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GrowthFactor(r) GF_tot
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RTK(l,state~U,fb_site~open) RTK_tot
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PI3K(state~off) PI3K_tot
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AKT(t308~U,s473~U) AKT_tot
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mTORC2(state~on) mTORC2_tot
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mTORC1(state~off) mTORC1_tot
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S6K(state~U) S6K_tot
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end seed species
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begin observables
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# KEY BIOLOGICAL OUTPUTS
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Molecules Active_RTK RTK(state~P) # Receptor tyrosine kinase activation
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Molecules Active_PI3K PI3K(state~on) # PI3K recruitment to membrane
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Molecules pAkt_T308 AKT(t308~P) # PDK1-mediated phosphorylation
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Molecules pAkt_S473 AKT(s473~P) # mTORC2-mediated phosphorylation
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Molecules Double_pAkt AKT(t308~P,s473~P) # Fully active Akt
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Molecules Active_mTORC1 mTORC1(state~on) # Downstream growth effector
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end observables
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begin reaction rules
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## RECEPTOR ACTIVATION & FEEDBACK
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# Growth factor binds to open RTK
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GrowthFactor(r) + RTK(l,fb_site~open) <-> GrowthFactor(r!1).RTK(l!1,fb_site~open) k_bind,k_unbind
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# Ligand-bound RTK becomes active
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GrowthFactor(r!1).RTK(l!1,state~U) -> GrowthFactor(r!1).RTK(l!1,state~P) k_rtk_act
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## PI3K / AKT PATHWAY
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# Active RTK recruits and activates PI3K
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RTK(state~P) + PI3K(state~off) -> RTK(state~P) + PI3K(state~on) k_pi3k
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# Active PI3K facilitates Akt phosphorylation at T308 (PDK1 proxy)
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PI3K(state~on) + AKT(t308~U) -> PI3K(state~on) + AKT(t308~P) k_akt_t308
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# Constitutive mTORC2 phosphorylates Akt at S473
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mTORC2() + AKT(s473~U) -> mTORC2() + AKT(s473~P) k_akt_s473
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## MTOR SIGNALING & FEEDBACK
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# Double-phosphorylated Akt activates mTORC1
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AKT(t308~P,s473~P) + mTORC1(state~off) -> AKT(t308~P,s473~P) + mTORC1(state~on) k_mtorc1
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# mTORC1 activates S6K,which mediates feedback
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mTORC1(state~on) + S6K(state~U) -> mTORC1(state~on) + S6K(state~P) k_s6k_act
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# Negative feedback: pS6K inhibits RTK ligand binding by "closing" the feedback site
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S6K(state~P) + RTK(fb_site~open) -> S6K(state~P) + RTK(fb_site~closed) k_fb
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## RESETTING MECHANISMS
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RTK(state~P) -> RTK(state~U) k_reset
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PI3K(state~on) -> PI3K(state~off) k_reset
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AKT(t308~P) -> AKT(t308~U) k_reset
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AKT(s473~P) -> AKT(s473~U) k_reset
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mTORC1(state~on) -> mTORC1(state~off) k_reset
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S6K(state~P) -> S6K(state~U) k_reset
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RTK(fb_site~closed) -> RTK(fb_site~open) k_reset
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end reaction rules
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begin actions
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generate_network({overwrite=>1})
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simulate({method=>"ode",t_start=>0,t_end=>500,n_steps=>250})
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end actions
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end model
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id: "akt-signaling"
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name: "akt signaling"
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description: "Signaling rates"
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contributors:
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- name: "Achyudhan"
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tags: ["akt", "signaling", "growthfactor", "rtk", "pi3k", "mtorc2", "mtorc1", "s6k"]
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category: "signaling"
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compatibility:
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bng2_compatible: true
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simulation_methods: ["ode"]
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uses_compartments: false
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uses_energy: false
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uses_functions: false
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nfsim_compatible: false
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source:
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origin: "ai-generated"
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original_format: "bngl"
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original_repository: "bionetgen-web-simulator"
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source_path: "example-models/akt-signaling.bngl"
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playground:
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visible: false
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gallery_category: "signaling"
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featured: false
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difficulty: "advanced"
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# allosteric activation
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Binding constants
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## Provenance
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This BNGL example was generated in BNG Playground and curated by Achyudhan.
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No external literature citation is associated with this model unless one is added manually.
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## Compatibility
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- BNG2 compatible: yes
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- Simulation methods: ode
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- Imported from: ai-generated
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## Files
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- allosteric-activation.bngl
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## Tags
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allosteric, activation, enzyme, substrate, activator, product
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begin model
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begin parameters
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# Binding constants
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k_on_S 2e-3 # Substrate binding to enzyme
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k_off_S 5e-4 # Substrate dissociation
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k_on_A 1.5e-3 # Activator binding (cooperative step 1)
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k_off_A 3e-4 # Activator dissociation
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# Catalytic rates
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k_cat_basal 0.02 # Catalysis by inactive/partially-active enzyme
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k_cat_full 0.25 # Catalysis by fully activated enzyme
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# Feedback
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k_inh 0.01 # Product-mediated feedback inhibition
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end parameters
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begin molecule types
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# Enzyme has a substrate site (s),two activator sites (a1,a2),and can be inhibited (i)
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Enzyme(s,a1,a2,i~off~on)
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Substrate(b)
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Activator(b)
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Product()
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end molecule types
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begin seed species
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Enzyme(s,a1,a2,i~off) 50
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Substrate(b) 500
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Activator(b) 200
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Product() 0
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end seed species
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begin observables
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# KEY BIOLOGICAL OUTPUTS
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Molecules Free_Enzyme Enzyme(a1,a2,s,i~off) # Available enzyme
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Molecules Single_Bound Enzyme(a1!1,a2).Activator(b!1) # Partially activated
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Molecules Double_Bound Enzyme(a1!1,a2!2).Activator(b!1).Activator(b!2) # Fully activated
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Molecules Product_Count Product() # Cumulative product
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Molecules Inhibited_Enz Enzyme(i~on) # Enzyme sequestered by feedback
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end observables
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begin reaction rules
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## ALLOSTERIC COOPERATIVITY
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# First activator binding
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Enzyme(a1,i~off) + Activator(b) <-> Enzyme(a1!1,i~off).Activator(b!1) k_on_A,k_off_A
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# Second activator binding (cooperative recruitment)
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Enzyme(a1!+,a2,i~off) + Activator(b) <-> Enzyme(a1!+,a2!1,i~off).Activator(b!1) k_on_A*2,k_off_A/2
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## SUBSTRATE PROCESSING
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# Substrate binds to enzyme (any state,but faster if active)
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Enzyme(s,i~off) + Substrate(b) <-> Enzyme(s!1,i~off).Substrate(b!1) k_on_S,k_off_S
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# Basal catalysis (inactive or partially active)
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Enzyme(s!1,a2,i~off).Substrate(b!1) -> Enzyme(s,a2,i~off) + Product() k_cat_basal
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# Enhanced catalysis (both allosteric sites bound)
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Enzyme(s!1,a1!+,a2!+,i~off).Substrate(b!1) -> Enzyme(s,a1!+,a2!+,i~off) + Product() k_cat_full
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## FEEDBACK REGULATION
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# Product mediates negative feedback inhibition (e.g.,metabolic control)
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Product() + Enzyme(i~off) -> Enzyme(i~on) k_inh
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Enzyme(i~on) -> Enzyme(i~off) k_off_S
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end reaction rules
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begin actions
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generate_network({overwrite=>1})
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simulate({method=>"ode",t_end=>5000,n_steps=>300})
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end actions
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end model
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id: "allosteric-activation"
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name: "allosteric activation"
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description: "Binding constants"
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contributors:
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- name: "Achyudhan"
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tags: ["allosteric", "activation", "enzyme", "substrate", "activator", "product"]
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category: "signaling"
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compatibility:
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bng2_compatible: true
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simulation_methods: ["ode"]
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uses_compartments: false
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uses_energy: false
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uses_functions: false
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nfsim_compatible: false
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source:
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origin: "ai-generated"
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original_format: "bngl"
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original_repository: "bionetgen-web-simulator"
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source_path: "example-models/allosteric-activation.bngl"
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playground:
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visible: false
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gallery_category: "signaling"
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featured: false
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difficulty: "advanced"
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# ampk signaling
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AMPK signaling: The cellular energy sensor.
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## Provenance
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This BNGL example was generated in BNG Playground and curated by Achyudhan.
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No external literature citation is associated with this model unless one is added manually.
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## Compatibility
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- BNG2 compatible: yes
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- Simulation methods: ode
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- Imported from: ai-generated
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## Files
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- ampk-signaling.bngl
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## Tags
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ampk, signaling, amp, lkb1, ca, sik, crtc

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