From 9e856a113bcc5df8bd31e4d07871935648f4abee Mon Sep 17 00:00:00 2001
From: Ben Gyori <ben.gyori@gmail.com>
Date: Tue, 26 Mar 2024 11:31:12 -0400
Subject: [PATCH] Add an alternative, p53 model

---
 .../scenario5/atm_petrinet.json               | 569 ++++++++++++++++++
 .../scenario5/atm_regnet.json                 | 454 ++++++++++++++
 .../scenario5/p53_model.ipynb                 | 233 +++++++
 3 files changed, 1256 insertions(+)
 create mode 100644 notebooks/evaluation_2024.03/scenario5/atm_petrinet.json
 create mode 100644 notebooks/evaluation_2024.03/scenario5/atm_regnet.json
 create mode 100644 notebooks/evaluation_2024.03/scenario5/p53_model.ipynb

diff --git a/notebooks/evaluation_2024.03/scenario5/atm_petrinet.json b/notebooks/evaluation_2024.03/scenario5/atm_petrinet.json
new file mode 100644
index 00000000..377c857f
--- /dev/null
+++ b/notebooks/evaluation_2024.03/scenario5/atm_petrinet.json
@@ -0,0 +1,569 @@
+{
+ "header": {
+  "name": "Model",
+  "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json",
+  "schema_name": "petrinet",
+  "description": "Model",
+  "model_version": "0.1"
+ },
+ "properties": {},
+ "model": {
+  "states": [
+   {
+    "id": "TP53i",
+    "name": "TP53i",
+    "grounding": {
+     "identifiers": {},
+     "modifiers": {}
+    }
+   },
+   {
+    "id": "ATMa",
+    "name": "ATMa",
+    "grounding": {
+     "identifiers": {},
+     "modifiers": {}
+    }
+   },
+   {
+    "id": "TP53a",
+    "name": "TP53a",
+    "grounding": {
+     "identifiers": {},
+     "modifiers": {}
+    }
+   },
+   {
+    "id": "PPM1Di",
+    "name": "PPM1Di",
+    "grounding": {
+     "identifiers": {},
+     "modifiers": {}
+    }
+   },
+   {
+    "id": "PPM1Da",
+    "name": "PPM1Da",
+    "grounding": {
+     "identifiers": {},
+     "modifiers": {}
+    }
+   },
+   {
+    "id": "MDM2i",
+    "name": "MDM2i",
+    "grounding": {
+     "identifiers": {},
+     "modifiers": {}
+    }
+   },
+   {
+    "id": "MDM2a",
+    "name": "MDM2a",
+    "grounding": {
+     "identifiers": {},
+     "modifiers": {}
+    }
+   },
+   {
+    "id": "ATMi",
+    "name": "ATMi",
+    "grounding": {
+     "identifiers": {},
+     "modifiers": {}
+    }
+   },
+   {
+    "id": "HIPK2",
+    "name": "HIPK2",
+    "grounding": {
+     "identifiers": {},
+     "modifiers": {}
+    }
+   },
+   {
+    "id": "CDKN2A",
+    "name": "CDKN2A",
+    "grounding": {
+     "identifiers": {},
+     "modifiers": {}
+    }
+   }
+  ],
+  "transitions": [
+   {
+    "id": "t1",
+    "input": [
+     "ATMa",
+     "TP53i"
+    ],
+    "output": [
+     "ATMa"
+    ],
+    "properties": {
+     "name": "t1"
+    }
+   },
+   {
+    "id": "t2",
+    "input": [
+     "ATMa",
+     "TP53i"
+    ],
+    "output": [
+     "ATMa",
+     "TP53i",
+     "TP53a"
+    ],
+    "properties": {
+     "name": "t2"
+    }
+   },
+   {
+    "id": "t3",
+    "input": [
+     "TP53a",
+     "PPM1Di"
+    ],
+    "output": [
+     "TP53a"
+    ],
+    "properties": {
+     "name": "t3"
+    }
+   },
+   {
+    "id": "t4",
+    "input": [
+     "TP53a",
+     "PPM1Di"
+    ],
+    "output": [
+     "TP53a",
+     "PPM1Di",
+     "PPM1Da"
+    ],
+    "properties": {
+     "name": "t4"
+    }
+   },
+   {
+    "id": "t5",
+    "input": [
+     "TP53a",
+     "MDM2i"
+    ],
+    "output": [
+     "TP53a"
+    ],
+    "properties": {
+     "name": "t5"
+    }
+   },
+   {
+    "id": "t6",
+    "input": [
+     "TP53a",
+     "MDM2i"
+    ],
+    "output": [
+     "TP53a",
+     "MDM2i",
+     "MDM2a"
+    ],
+    "properties": {
+     "name": "t6"
+    }
+   },
+   {
+    "id": "t7",
+    "input": [
+     "ATMa",
+     "ATMi"
+    ],
+    "output": [
+     "ATMa"
+    ],
+    "properties": {
+     "name": "t7"
+    }
+   },
+   {
+    "id": "t8",
+    "input": [
+     "ATMa",
+     "ATMi"
+    ],
+    "output": [
+     "ATMa",
+     "ATMi",
+     "ATMa"
+    ],
+    "properties": {
+     "name": "t8"
+    }
+   },
+   {
+    "id": "t9",
+    "input": [
+     "PPM1Da",
+     "TP53a"
+    ],
+    "output": [
+     "PPM1Da"
+    ],
+    "properties": {
+     "name": "t9"
+    }
+   },
+   {
+    "id": "t10",
+    "input": [
+     "PPM1Da",
+     "TP53a"
+    ],
+    "output": [
+     "PPM1Da",
+     "TP53a",
+     "TP53i"
+    ],
+    "properties": {
+     "name": "t10"
+    }
+   },
+   {
+    "id": "t11",
+    "input": [
+     "PPM1Da",
+     "ATMa"
+    ],
+    "output": [
+     "PPM1Da"
+    ],
+    "properties": {
+     "name": "t11"
+    }
+   },
+   {
+    "id": "t12",
+    "input": [
+     "PPM1Da",
+     "ATMa"
+    ],
+    "output": [
+     "PPM1Da",
+     "ATMa",
+     "ATMi"
+    ],
+    "properties": {
+     "name": "t12"
+    }
+   },
+   {
+    "id": "t13",
+    "input": [
+     "MDM2a",
+     "TP53a"
+    ],
+    "output": [
+     "MDM2a"
+    ],
+    "properties": {
+     "name": "t13"
+    }
+   },
+   {
+    "id": "t14",
+    "input": [
+     "MDM2a",
+     "TP53a"
+    ],
+    "output": [
+     "MDM2a",
+     "TP53a",
+     "TP53i"
+    ],
+    "properties": {
+     "name": "t14"
+    }
+   },
+   {
+    "id": "t15",
+    "input": [
+     "HIPK2",
+     "PPM1Da"
+    ],
+    "output": [
+     "HIPK2"
+    ],
+    "properties": {
+     "name": "t15"
+    }
+   },
+   {
+    "id": "t16",
+    "input": [
+     "HIPK2",
+     "PPM1Da"
+    ],
+    "output": [
+     "HIPK2",
+     "PPM1Da",
+     "PPM1Di"
+    ],
+    "properties": {
+     "name": "t16"
+    }
+   },
+   {
+    "id": "t17",
+    "input": [
+     "CDKN2A",
+     "MDM2a"
+    ],
+    "output": [
+     "CDKN2A"
+    ],
+    "properties": {
+     "name": "t17"
+    }
+   },
+   {
+    "id": "t18",
+    "input": [
+     "CDKN2A",
+     "MDM2a"
+    ],
+    "output": [
+     "CDKN2A",
+     "MDM2a",
+     "MDM2i"
+    ],
+    "properties": {
+     "name": "t18"
+    }
+   }
+  ]
+ },
+ "semantics": {
+  "ode": {
+   "rates": [
+    {
+     "target": "t1",
+     "expression": "ATMa*TP53i*kf_at_act_1",
+     "expression_mathml": "<apply><times/><ci>ATMa</ci><ci>TP53i</ci><ci>kf_at_act_1</ci></apply>"
+    },
+    {
+     "target": "t2",
+     "expression": "ATMa*TP53i*kf_at_act_1",
+     "expression_mathml": "<apply><times/><ci>ATMa</ci><ci>TP53i</ci><ci>kf_at_act_1</ci></apply>"
+    },
+    {
+     "target": "t3",
+     "expression": "PPM1Di*TP53a*kf_tp_act_1",
+     "expression_mathml": "<apply><times/><ci>PPM1Di</ci><ci>TP53a</ci><ci>kf_tp_act_1</ci></apply>"
+    },
+    {
+     "target": "t4",
+     "expression": "PPM1Di*TP53a*kf_tp_act_1",
+     "expression_mathml": "<apply><times/><ci>PPM1Di</ci><ci>TP53a</ci><ci>kf_tp_act_1</ci></apply>"
+    },
+    {
+     "target": "t5",
+     "expression": "MDM2i*TP53a*kf_tm_act_1",
+     "expression_mathml": "<apply><times/><ci>MDM2i</ci><ci>TP53a</ci><ci>kf_tm_act_1</ci></apply>"
+    },
+    {
+     "target": "t6",
+     "expression": "MDM2i*TP53a*kf_tm_act_1",
+     "expression_mathml": "<apply><times/><ci>MDM2i</ci><ci>TP53a</ci><ci>kf_tm_act_1</ci></apply>"
+    },
+    {
+     "target": "t7",
+     "expression": "ATMa*ATMi*kf_aa_act_1",
+     "expression_mathml": "<apply><times/><ci>ATMa</ci><ci>ATMi</ci><ci>kf_aa_act_1</ci></apply>"
+    },
+    {
+     "target": "t8",
+     "expression": "ATMa*ATMi*kf_aa_act_1",
+     "expression_mathml": "<apply><times/><ci>ATMa</ci><ci>ATMi</ci><ci>kf_aa_act_1</ci></apply>"
+    },
+    {
+     "target": "t9",
+     "expression": "PPM1Da*TP53a*kf_pt_act_1",
+     "expression_mathml": "<apply><times/><ci>PPM1Da</ci><ci>TP53a</ci><ci>kf_pt_act_1</ci></apply>"
+    },
+    {
+     "target": "t10",
+     "expression": "PPM1Da*TP53a*kf_pt_act_1",
+     "expression_mathml": "<apply><times/><ci>PPM1Da</ci><ci>TP53a</ci><ci>kf_pt_act_1</ci></apply>"
+    },
+    {
+     "target": "t11",
+     "expression": "ATMa*PPM1Da*kf_pa_act_1",
+     "expression_mathml": "<apply><times/><ci>ATMa</ci><ci>PPM1Da</ci><ci>kf_pa_act_1</ci></apply>"
+    },
+    {
+     "target": "t12",
+     "expression": "ATMa*PPM1Da*kf_pa_act_1",
+     "expression_mathml": "<apply><times/><ci>ATMa</ci><ci>PPM1Da</ci><ci>kf_pa_act_1</ci></apply>"
+    },
+    {
+     "target": "t13",
+     "expression": "MDM2a*TP53a*kf_mt_act_1",
+     "expression_mathml": "<apply><times/><ci>MDM2a</ci><ci>TP53a</ci><ci>kf_mt_act_1</ci></apply>"
+    },
+    {
+     "target": "t14",
+     "expression": "MDM2a*TP53a*kf_mt_act_1",
+     "expression_mathml": "<apply><times/><ci>MDM2a</ci><ci>TP53a</ci><ci>kf_mt_act_1</ci></apply>"
+    },
+    {
+     "target": "t15",
+     "expression": "HIPK2*PPM1Da*kf_hp_act_1",
+     "expression_mathml": "<apply><times/><ci>HIPK2</ci><ci>PPM1Da</ci><ci>kf_hp_act_1</ci></apply>"
+    },
+    {
+     "target": "t16",
+     "expression": "HIPK2*PPM1Da*kf_hp_act_1",
+     "expression_mathml": "<apply><times/><ci>HIPK2</ci><ci>PPM1Da</ci><ci>kf_hp_act_1</ci></apply>"
+    },
+    {
+     "target": "t17",
+     "expression": "CDKN2A*MDM2a*kf_cm_act_1",
+     "expression_mathml": "<apply><times/><ci>CDKN2A</ci><ci>MDM2a</ci><ci>kf_cm_act_1</ci></apply>"
+    },
+    {
+     "target": "t18",
+     "expression": "CDKN2A*MDM2a*kf_cm_act_1",
+     "expression_mathml": "<apply><times/><ci>CDKN2A</ci><ci>MDM2a</ci><ci>kf_cm_act_1</ci></apply>"
+    }
+   ],
+   "initials": [
+    {
+     "target": "TP53i",
+     "expression": "TP53_0",
+     "expression_mathml": "<ci>TP53_0</ci>"
+    },
+    {
+     "target": "ATMa",
+     "expression": "ATMa_0",
+     "expression_mathml": "<ci>ATMa_0</ci>"
+    },
+    {
+     "target": "TP53a",
+     "expression": "0",
+     "expression_mathml": "<cn>0</cn>"
+    },
+    {
+     "target": "PPM1Di",
+     "expression": "PPM1D_0",
+     "expression_mathml": "<ci>PPM1D_0</ci>"
+    },
+    {
+     "target": "PPM1Da",
+     "expression": "0",
+     "expression_mathml": "<cn>0</cn>"
+    },
+    {
+     "target": "MDM2i",
+     "expression": "MDM2_0",
+     "expression_mathml": "<ci>MDM2_0</ci>"
+    },
+    {
+     "target": "MDM2a",
+     "expression": "0",
+     "expression_mathml": "<cn>0</cn>"
+    },
+    {
+     "target": "ATMi",
+     "expression": "ATM_0",
+     "expression_mathml": "<ci>ATM_0</ci>"
+    },
+    {
+     "target": "HIPK2",
+     "expression": "HIPK2_0",
+     "expression_mathml": "<ci>HIPK2_0</ci>"
+    },
+    {
+     "target": "CDKN2A",
+     "expression": "CDKN2A_0",
+     "expression_mathml": "<ci>CDKN2A_0</ci>"
+    }
+   ],
+   "parameters": [
+    {
+     "id": "kf_at_act_1",
+     "value": 1e-07
+    },
+    {
+     "id": "kf_tp_act_1",
+     "value": 1e-07
+    },
+    {
+     "id": "kf_tm_act_1",
+     "value": 1e-07
+    },
+    {
+     "id": "kf_aa_act_1",
+     "value": 5e-07
+    },
+    {
+     "id": "kf_pt_act_1",
+     "value": 5e-07
+    },
+    {
+     "id": "kf_pa_act_1",
+     "value": 1e-05
+    },
+    {
+     "id": "kf_mt_act_1",
+     "value": 1e-07
+    },
+    {
+     "id": "kf_hp_act_1",
+     "value": 1e-07
+    },
+    {
+     "id": "kf_cm_act_1",
+     "value": 1e-07
+    },
+    {
+     "id": "ATM_0",
+     "value": 10000.0
+    },
+    {
+     "id": "TP53_0",
+     "value": 10000.0
+    },
+    {
+     "id": "PPM1D_0",
+     "value": 10000.0
+    },
+    {
+     "id": "MDM2_0",
+     "value": 10000.0
+    },
+    {
+     "id": "HIPK2_0",
+     "value": 10000.0
+    },
+    {
+     "id": "CDKN2A_0",
+     "value": 10000.0
+    },
+    {
+     "id": "ATMa_0",
+     "value": 1.0
+    }
+   ],
+   "observables": [],
+   "time": {
+    "id": "t"
+   }
+  }
+ },
+ "metadata": {
+  "annotations": {}
+ }
+}
\ No newline at end of file
diff --git a/notebooks/evaluation_2024.03/scenario5/atm_regnet.json b/notebooks/evaluation_2024.03/scenario5/atm_regnet.json
new file mode 100644
index 00000000..df8bc1f9
--- /dev/null
+++ b/notebooks/evaluation_2024.03/scenario5/atm_regnet.json
@@ -0,0 +1,454 @@
+{
+ "header": {
+  "name": "Model",
+  "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/regnet_v0.2/regnet/regnet_schema.json",
+  "schema_name": "regnet",
+  "description": "Model",
+  "model_version": "0.1"
+ },
+ "model": {
+  "vertices": [
+   {
+    "id": "TP53i",
+    "name": "TP53i",
+    "grounding": {
+     "identifiers": {},
+     "context": {}
+    },
+    "initial": "TP53_0"
+   },
+   {
+    "id": "ATMa",
+    "name": "ATMa",
+    "grounding": {
+     "identifiers": {},
+     "context": {}
+    },
+    "initial": "ATMa_0"
+   },
+   {
+    "id": "TP53a",
+    "name": "TP53a",
+    "grounding": {
+     "identifiers": {},
+     "context": {}
+    },
+    "initial": 0.0
+   },
+   {
+    "id": "PPM1Di",
+    "name": "PPM1Di",
+    "grounding": {
+     "identifiers": {},
+     "context": {}
+    },
+    "initial": "PPM1D_0"
+   },
+   {
+    "id": "PPM1Da",
+    "name": "PPM1Da",
+    "grounding": {
+     "identifiers": {},
+     "context": {}
+    },
+    "initial": 0.0
+   },
+   {
+    "id": "MDM2i",
+    "name": "MDM2i",
+    "grounding": {
+     "identifiers": {},
+     "context": {}
+    },
+    "initial": "MDM2_0"
+   },
+   {
+    "id": "MDM2a",
+    "name": "MDM2a",
+    "grounding": {
+     "identifiers": {},
+     "context": {}
+    },
+    "initial": 0.0
+   },
+   {
+    "id": "ATMi",
+    "name": "ATMi",
+    "grounding": {
+     "identifiers": {},
+     "context": {}
+    },
+    "initial": "ATM_0"
+   },
+   {
+    "id": "HIPK2",
+    "name": "HIPK2",
+    "grounding": {
+     "identifiers": {},
+     "context": {}
+    },
+    "initial": "HIPK2_0"
+   },
+   {
+    "id": "CDKN2A",
+    "name": "CDKN2A",
+    "grounding": {
+     "identifiers": {},
+     "context": {}
+    },
+    "initial": "CDKN2A_0"
+   }
+  ],
+  "edges": [
+   {
+    "id": "t1",
+    "source": "ATMa",
+    "target": "TP53i",
+    "sign": false,
+    "properties": {
+     "name": "t1",
+     "rate_constant": "kf_at_act_1"
+    }
+   },
+   {
+    "id": "t2",
+    "source": "ATMa",
+    "target": "TP53a",
+    "sign": true,
+    "properties": {
+     "name": "t2",
+     "rate_constant": "kf_at_act_1"
+    }
+   },
+   {
+    "id": "t3",
+    "source": "TP53a",
+    "target": "PPM1Di",
+    "sign": false,
+    "properties": {
+     "name": "t3",
+     "rate_constant": "kf_tp_act_1"
+    }
+   },
+   {
+    "id": "t4",
+    "source": "PPM1Di",
+    "target": "PPM1Da",
+    "sign": true,
+    "properties": {
+     "name": "t4",
+     "rate_constant": "kf_tp_act_1"
+    }
+   },
+   {
+    "id": "t5",
+    "source": "TP53a",
+    "target": "MDM2i",
+    "sign": false,
+    "properties": {
+     "name": "t5",
+     "rate_constant": "kf_tm_act_1"
+    }
+   },
+   {
+    "id": "t6",
+    "source": "MDM2i",
+    "target": "MDM2a",
+    "sign": true,
+    "properties": {
+     "name": "t6",
+     "rate_constant": "kf_tm_act_1"
+    }
+   },
+   {
+    "id": "t7",
+    "source": "ATMa",
+    "target": "ATMi",
+    "sign": false,
+    "properties": {
+     "name": "t7",
+     "rate_constant": "kf_aa_act_1"
+    }
+   },
+   {
+    "id": "t8",
+    "source": "ATMi",
+    "target": "ATMa",
+    "sign": true,
+    "properties": {
+     "name": "t8",
+     "rate_constant": "kf_aa_act_1"
+    }
+   },
+   {
+    "id": "t9",
+    "source": "PPM1Da",
+    "target": "TP53a",
+    "sign": false,
+    "properties": {
+     "name": "t9",
+     "rate_constant": "kf_pt_act_1"
+    }
+   },
+   {
+    "id": "t10",
+    "source": "PPM1Da",
+    "target": "TP53i",
+    "sign": true,
+    "properties": {
+     "name": "t10",
+     "rate_constant": "kf_pt_act_1"
+    }
+   },
+   {
+    "id": "t11",
+    "source": "PPM1Da",
+    "target": "ATMa",
+    "sign": false,
+    "properties": {
+     "name": "t11",
+     "rate_constant": "kf_pa_act_1"
+    }
+   },
+   {
+    "id": "t12",
+    "source": "ATMa",
+    "target": "ATMi",
+    "sign": true,
+    "properties": {
+     "name": "t12",
+     "rate_constant": "kf_pa_act_1"
+    }
+   },
+   {
+    "id": "t13",
+    "source": "MDM2a",
+    "target": "TP53a",
+    "sign": false,
+    "properties": {
+     "name": "t13",
+     "rate_constant": "kf_mt_act_1"
+    }
+   },
+   {
+    "id": "t14",
+    "source": "MDM2a",
+    "target": "TP53i",
+    "sign": true,
+    "properties": {
+     "name": "t14",
+     "rate_constant": "kf_mt_act_1"
+    }
+   },
+   {
+    "id": "t15",
+    "source": "HIPK2",
+    "target": "PPM1Da",
+    "sign": false,
+    "properties": {
+     "name": "t15",
+     "rate_constant": "kf_hp_act_1"
+    }
+   },
+   {
+    "id": "t16",
+    "source": "HIPK2",
+    "target": "PPM1Di",
+    "sign": true,
+    "properties": {
+     "name": "t16",
+     "rate_constant": "kf_hp_act_1"
+    }
+   },
+   {
+    "id": "t17",
+    "source": "CDKN2A",
+    "target": "MDM2a",
+    "sign": false,
+    "properties": {
+     "name": "t17",
+     "rate_constant": "kf_cm_act_1"
+    }
+   },
+   {
+    "id": "t18",
+    "source": "CDKN2A",
+    "target": "MDM2i",
+    "sign": true,
+    "properties": {
+     "name": "t18",
+     "rate_constant": "kf_cm_act_1"
+    }
+   }
+  ],
+  "parameters": [
+   {
+    "id": "kf_at_act_1",
+    "value": 1e-07
+   },
+   {
+    "id": "kf_tp_act_1",
+    "value": 1e-07
+   },
+   {
+    "id": "kf_tm_act_1",
+    "value": 1e-07
+   },
+   {
+    "id": "kf_aa_act_1",
+    "value": 5e-07
+   },
+   {
+    "id": "kf_pt_act_1",
+    "value": 5e-07
+   },
+   {
+    "id": "kf_pa_act_1",
+    "value": 1e-05
+   },
+   {
+    "id": "kf_mt_act_1",
+    "value": 1e-07
+   },
+   {
+    "id": "kf_hp_act_1",
+    "value": 1e-07
+   },
+   {
+    "id": "kf_cm_act_1",
+    "value": 1e-07
+   },
+   {
+    "id": "ATM_0",
+    "value": 10000.0
+   },
+   {
+    "id": "TP53_0",
+    "value": 10000.0
+   },
+   {
+    "id": "PPM1D_0",
+    "value": 10000.0
+   },
+   {
+    "id": "MDM2_0",
+    "value": 10000.0
+   },
+   {
+    "id": "HIPK2_0",
+    "value": 10000.0
+   },
+   {
+    "id": "CDKN2A_0",
+    "value": 10000.0
+   },
+   {
+    "id": "ATMa_0",
+    "value": 1.0
+   }
+  ]
+ },
+ "semantics": {
+  "ode": {
+   "rates": [
+    {
+     "target": "t1",
+     "expression": "ATMa*TP53i*kf_at_act_1",
+     "expression_mathml": "<apply><times/><ci>ATMa</ci><ci>TP53i</ci><ci>kf_at_act_1</ci></apply>"
+    },
+    {
+     "target": "t2",
+     "expression": "ATMa*TP53i*kf_at_act_1",
+     "expression_mathml": "<apply><times/><ci>ATMa</ci><ci>TP53i</ci><ci>kf_at_act_1</ci></apply>"
+    },
+    {
+     "target": "t3",
+     "expression": "PPM1Di*TP53a*kf_tp_act_1",
+     "expression_mathml": "<apply><times/><ci>PPM1Di</ci><ci>TP53a</ci><ci>kf_tp_act_1</ci></apply>"
+    },
+    {
+     "target": "t4",
+     "expression": "PPM1Di*TP53a*kf_tp_act_1",
+     "expression_mathml": "<apply><times/><ci>PPM1Di</ci><ci>TP53a</ci><ci>kf_tp_act_1</ci></apply>"
+    },
+    {
+     "target": "t5",
+     "expression": "MDM2i*TP53a*kf_tm_act_1",
+     "expression_mathml": "<apply><times/><ci>MDM2i</ci><ci>TP53a</ci><ci>kf_tm_act_1</ci></apply>"
+    },
+    {
+     "target": "t6",
+     "expression": "MDM2i*TP53a*kf_tm_act_1",
+     "expression_mathml": "<apply><times/><ci>MDM2i</ci><ci>TP53a</ci><ci>kf_tm_act_1</ci></apply>"
+    },
+    {
+     "target": "t7",
+     "expression": "ATMa*ATMi*kf_aa_act_1",
+     "expression_mathml": "<apply><times/><ci>ATMa</ci><ci>ATMi</ci><ci>kf_aa_act_1</ci></apply>"
+    },
+    {
+     "target": "t8",
+     "expression": "ATMa*ATMi*kf_aa_act_1",
+     "expression_mathml": "<apply><times/><ci>ATMa</ci><ci>ATMi</ci><ci>kf_aa_act_1</ci></apply>"
+    },
+    {
+     "target": "t9",
+     "expression": "PPM1Da*TP53a*kf_pt_act_1",
+     "expression_mathml": "<apply><times/><ci>PPM1Da</ci><ci>TP53a</ci><ci>kf_pt_act_1</ci></apply>"
+    },
+    {
+     "target": "t10",
+     "expression": "PPM1Da*TP53a*kf_pt_act_1",
+     "expression_mathml": "<apply><times/><ci>PPM1Da</ci><ci>TP53a</ci><ci>kf_pt_act_1</ci></apply>"
+    },
+    {
+     "target": "t11",
+     "expression": "ATMa*PPM1Da*kf_pa_act_1",
+     "expression_mathml": "<apply><times/><ci>ATMa</ci><ci>PPM1Da</ci><ci>kf_pa_act_1</ci></apply>"
+    },
+    {
+     "target": "t12",
+     "expression": "ATMa*PPM1Da*kf_pa_act_1",
+     "expression_mathml": "<apply><times/><ci>ATMa</ci><ci>PPM1Da</ci><ci>kf_pa_act_1</ci></apply>"
+    },
+    {
+     "target": "t13",
+     "expression": "MDM2a*TP53a*kf_mt_act_1",
+     "expression_mathml": "<apply><times/><ci>MDM2a</ci><ci>TP53a</ci><ci>kf_mt_act_1</ci></apply>"
+    },
+    {
+     "target": "t14",
+     "expression": "MDM2a*TP53a*kf_mt_act_1",
+     "expression_mathml": "<apply><times/><ci>MDM2a</ci><ci>TP53a</ci><ci>kf_mt_act_1</ci></apply>"
+    },
+    {
+     "target": "t15",
+     "expression": "HIPK2*PPM1Da*kf_hp_act_1",
+     "expression_mathml": "<apply><times/><ci>HIPK2</ci><ci>PPM1Da</ci><ci>kf_hp_act_1</ci></apply>"
+    },
+    {
+     "target": "t16",
+     "expression": "HIPK2*PPM1Da*kf_hp_act_1",
+     "expression_mathml": "<apply><times/><ci>HIPK2</ci><ci>PPM1Da</ci><ci>kf_hp_act_1</ci></apply>"
+    },
+    {
+     "target": "t17",
+     "expression": "CDKN2A*MDM2a*kf_cm_act_1",
+     "expression_mathml": "<apply><times/><ci>CDKN2A</ci><ci>MDM2a</ci><ci>kf_cm_act_1</ci></apply>"
+    },
+    {
+     "target": "t18",
+     "expression": "CDKN2A*MDM2a*kf_cm_act_1",
+     "expression_mathml": "<apply><times/><ci>CDKN2A</ci><ci>MDM2a</ci><ci>kf_cm_act_1</ci></apply>"
+    }
+   ],
+   "observables": [],
+   "time": {
+    "id": "t"
+   }
+  }
+ },
+ "metadata": {
+  "annotations": {}
+ }
+}
\ No newline at end of file
diff --git a/notebooks/evaluation_2024.03/scenario5/p53_model.ipynb b/notebooks/evaluation_2024.03/scenario5/p53_model.ipynb
new file mode 100644
index 00000000..5f9ba771
--- /dev/null
+++ b/notebooks/evaluation_2024.03/scenario5/p53_model.ipynb
@@ -0,0 +1,233 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5bfc8e1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "\n",
+    "import sympy\n",
+    "import numpy\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from mira.metamodel import *\n",
+    "from mira.modeling import Model\n",
+    "from mira.modeling.ode import OdeModel, simulate_ode_model\n",
+    "from mira.modeling.amr.regnet import template_model_to_regnet_json\n",
+    "from mira.modeling.amr.petrinet import template_model_to_petrinet_json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e0fa72e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c = lambda x: Concept(name=x)\n",
+    "\n",
+    "templates = [\n",
+    "    ControlledConversion(controller=c('ATMa'), subject=c('TP53i'), outcome=c('TP53a')).with_mass_action_rate_law('kf_at_act_1'),\n",
+    "    ControlledConversion(controller=c('TP53a'), subject=c('PPM1Di'), outcome=c('PPM1Da')).with_mass_action_rate_law('kf_tp_act_1'),\n",
+    "    ControlledConversion(controller=c('TP53a'), subject=c('MDM2i'), outcome=c('MDM2a')).with_mass_action_rate_law('kf_tm_act_1'),\n",
+    "    ControlledConversion(controller=c('ATMa'), subject=c('ATMi'), outcome=c('ATMa')).with_mass_action_rate_law('kf_aa_act_1'),\n",
+    "    ControlledConversion(controller=c('PPM1Da'), subject=c('TP53a'), outcome=c('TP53i')).with_mass_action_rate_law('kf_pt_act_1'),\n",
+    "    ControlledConversion(controller=c('PPM1Da'), subject=c('ATMa'), outcome=c('ATMi')).with_mass_action_rate_law('kf_pa_act_1'),\n",
+    "    ControlledConversion(controller=c('MDM2a'), subject=c('TP53a'), outcome=c('TP53i')).with_mass_action_rate_law('kf_mt_act_1'),\n",
+    "    ControlledConversion(controller=c('HIPK2'), subject=c('PPM1Da'), outcome=c('PPM1Di')).with_mass_action_rate_law('kf_hp_act_1'),\n",
+    "    ControlledConversion(controller=c('CDKN2A'), subject=c('MDM2a'), outcome=c('MDM2i')).with_mass_action_rate_law('kf_cm_act_1'),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cb45425f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "templates = list(itertools.chain(*[conversion_to_deg_prod(t) for t in templates]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "075b49eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = {\n",
+    "    'kf_at_act_1': Parameter(name='kf_at_act_1', value=1e-07),\n",
+    "    'kf_tp_act_1': Parameter(name='kf_tp_act_1', value=1e-07),\n",
+    "    'kf_tm_act_1': Parameter(name='kf_tm_act_1', value=1e-07),\n",
+    "    'kf_aa_act_1': Parameter(name='kf_aa_act_1', value=5e-07),\n",
+    "    'kf_pt_act_1': Parameter(name='kf_pt_act_1', value=5e-07),\n",
+    "    'kf_pa_act_1': Parameter(name='kf_pa_act_1', value=1e-05),\n",
+    "    'kf_mt_act_1': Parameter(name='kf_mt_act_1', value=1e-07),\n",
+    "    'kf_hp_act_1': Parameter(name='kf_hp_act_1', value=1e-07),\n",
+    "    'kf_cm_act_1': Parameter(name='kf_cm_act_1', value=1e-07),\n",
+    "    'ATM_0': Parameter(name='ATM_0', value=10000.0),\n",
+    "    'TP53_0': Parameter(name='TP53_0', value=10000.0),\n",
+    "    'PPM1D_0': Parameter(name='PPM1D_0', value=10000.0),\n",
+    "    'MDM2_0': Parameter(name='MDM2_0', value=10000.0),\n",
+    "    'HIPK2_0': Parameter(name='HIPK2_0', value=10000.0),\n",
+    "    'CDKN2A_0': Parameter(name='CDKN2A_0', value=10000.0),\n",
+    "    'ATMa_0': Parameter(name='ATMa_0', value=1.0)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f4758b1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "initials = {\n",
+    "    'ATMi_0': Initial(concept=Concept(name='ATMi'), expression=sympy.Symbol('ATM_0')),\n",
+    "    'TP53i_0': Initial(concept=Concept(name='TP53i'), expression=sympy.Symbol('TP53_0')),\n",
+    "    'PPM1Di_0': Initial(concept=Concept(name='PPM1Di'), expression=sympy.Symbol('PPM1D_0')),\n",
+    "    'MDM2i_0': Initial(concept=Concept(name='MDM2i'), expression=sympy.Symbol('MDM2_0')),\n",
+    "    'HIPK2_0': Initial(concept=Concept(name='HIPK2'), expression=sympy.Symbol('HIPK2_0')),\n",
+    "    'CDKN2A_0': Initial(concept=Concept(name='CDKN2A'), expression=sympy.Symbol('CDKN2A_0')),\n",
+    "    'ATMa_0': Initial(concept=Concept(name='ATMa'), expression=sympy.Symbol('ATMa_0')),\n",
+    "    'TP53a_0': Initial(concept=Concept(name='TP53a'), expression=0),\n",
+    "    'PPM1Da_0': Initial(concept=Concept(name='PPM1Da'), expression=0),\n",
+    "    'MDM2a_0': Initial(concept=Concept(name='MDM2a'), expression=0),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "efa7154d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tm = TemplateModel(templates=templates,\n",
+    "                   parameters=parameters,\n",
+    "                   initials=initials)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c62c2252",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}- p_{0, 0} y_{0, 0} y_{1, 0} + p_{4, 0} y_{2, 0} y_{4, 0} + p_{6, 0} y_{2, 0} y_{6, 0}\\\\p_{3, 0} y_{1, 0} y_{7, 0} - p_{5, 0} y_{1, 0} y_{4, 0}\\\\p_{0, 0} y_{0, 0} y_{1, 0} - p_{4, 0} y_{2, 0} y_{4, 0} - p_{6, 0} y_{2, 0} y_{6, 0}\\\\- p_{1, 0} y_{2, 0} y_{3, 0} + p_{7, 0} y_{4, 0} y_{8, 0}\\\\p_{1, 0} y_{2, 0} y_{3, 0} - p_{7, 0} y_{4, 0} y_{8, 0}\\\\- p_{2, 0} y_{2, 0} y_{5, 0} + p_{8, 0} y_{6, 0} y_{9, 0}\\\\p_{2, 0} y_{2, 0} y_{5, 0} - p_{8, 0} y_{6, 0} y_{9, 0}\\\\- p_{3, 0} y_{1, 0} y_{7, 0} + p_{5, 0} y_{1, 0} y_{4, 0}\\\\0\\\\0\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[-p[0, 0]*y[0, 0]*y[1, 0] + p[4, 0]*y[2, 0]*y[4, 0] + p[6, 0]*y[2, 0]*y[6, 0]],\n",
+       "[                           p[3, 0]*y[1, 0]*y[7, 0] - p[5, 0]*y[1, 0]*y[4, 0]],\n",
+       "[ p[0, 0]*y[0, 0]*y[1, 0] - p[4, 0]*y[2, 0]*y[4, 0] - p[6, 0]*y[2, 0]*y[6, 0]],\n",
+       "[                          -p[1, 0]*y[2, 0]*y[3, 0] + p[7, 0]*y[4, 0]*y[8, 0]],\n",
+       "[                           p[1, 0]*y[2, 0]*y[3, 0] - p[7, 0]*y[4, 0]*y[8, 0]],\n",
+       "[                          -p[2, 0]*y[2, 0]*y[5, 0] + p[8, 0]*y[6, 0]*y[9, 0]],\n",
+       "[                           p[2, 0]*y[2, 0]*y[5, 0] - p[8, 0]*y[6, 0]*y[9, 0]],\n",
+       "[                          -p[3, 0]*y[1, 0]*y[7, 0] + p[5, 0]*y[1, 0]*y[4, 0]],\n",
+       "[                                                                           0],\n",
+       "[                                                                           0]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "om = OdeModel(Model(tm), initialized=True)\n",
+    "om.kinetics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "69b3e528",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ts = numpy.linspace(0, 1e5, 1000)\n",
+    "res = simulate_ode_model(om, times=ts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b1a1d4ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '# Molecules')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ts, res, label=om.vmap.keys())\n",
+    "plt.grid(alpha=0.25)\n",
+    "plt.ylim([0,4000])\n",
+    "plt.legend(loc=\"center left\", bbox_to_anchor=(1, 0.5))\n",
+    "plt.xlabel('Time (a.u.)')\n",
+    "plt.ylabel('# Molecules')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d1b94bcb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frameworks = {'regnet': template_model_to_regnet_json, 'petrinet': template_model_to_petrinet_json}\n",
+    "\n",
+    "for fkey, fun in frameworks.items():\n",
+    "    with open(f'atm_{fkey}.json', 'w') as fh:\n",
+    "        json.dump(fun(tm), fh, indent=1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}