diff --git a/assignment.xlsx b/assignment.xlsx new file mode 100644 index 0000000..8e8d890 Binary files /dev/null and b/assignment.xlsx differ diff --git a/docs/examples/SAHH_kinetics.ipynb b/docs/examples/SAHH_kinetics.ipynb index 032f852..346ee2b 100644 --- a/docs/examples/SAHH_kinetics.ipynb +++ b/docs/examples/SAHH_kinetics.ipynb @@ -45,12 +45,12 @@ "![Reaction Scheme](../images/reaction_scheme.png)\n", "\n", "\n", - "Kinetic enzyme assays for the hydrolysis of _S_-adenosyl-_L_-homocysteine (`SAH`) or _S_-adenosyl-_L_-cysteine (`SIH`), catalyzed by four different _S_-adenosyl-_L_-homocysteine hydrolase (SAHH) enzymes, were conducted using a 96-well plate. Each reaction was carried out in triplicate for the respective enzymes as shown in the plate layout. \n", - "For detection, Ellman's reagent (5,5'-dithiobis-2-nitrobenzoate) was used. It reacts with the homocysteine (`Hcy`) produced from the hydrolysis of SAH or SIH to form 2-nitro-5-thiobenzoate (`TNB`), which can be measured at an absorbance of 412 nm. The concentration of TNB was quantified using a calibration curve, which was also recorded on the same plate.\n", + "Kinetic enzyme assays for the hydrolysis of _S_-adenosyl-_L_-homocysteine (`SAH`) or _S_-inosly-_L_-homocysteine (`SIH`), catalyzed by four different _S_-adenosyl-_L_-homocysteine hydrolase (SAHH) enzymes, were conducted using a 96-well plate. Each reaction was carried out in triplicate for the respective enzymes as shown in the plate layout. \n", + "For detection, Ellman's reagent 5,5'-dithiobis-2-nitrobenzoate (`DTNB`) was used. It reacts with the homocysteine (`Hcy`) produced from the hydrolysis of SAH or SIH to form 2-nitro-5-thiobenzoate (`TNB`), which has an absorbance maximum at 412 nm. The concentration of TNB was quantified using a calibration curve, which was also recorded on the same plate.\n", "\n", "![Plate Layout](../images/plate%20SAHH.png)\n", "\n", - "The SAHH enzymes from _Mycoplasma capricolum_ (`McSAHH`), _Mycobacterium tuberculosis_ (`MtSAHH`), _Mycoplasma hyorhinis_ (`MhSAHH`), and _Thermotoga maritima_ (`TmSAHH`) were applied at a concentration of 5 µM, whereas the substrate concentration was 200 µM in all assays. The calibration curve for TNB was prepared in the range of 0 to 250 µM. Ellman's reagent was used at a concentration of 500 µM.\n", + "The SAHH enzymes from _Methanocella conradii_ (`McSAHH`), Methanohalobium evestigatum (`MeSAHH`), _Methanocaldocossus infernus_ (`MiSAHH`), and _Methanothrix thermoacetophila_ (`MtSAHH`) were applied at a concentration of 5 µM, whereas the substrate concentration was 200 µM in all assays. The calibration curve for TNB was prepared in the range of 0 to 250 µM. Ellman's reagent was used at a concentration of 500 µM.\n", "\n", "The goal of this workflow is to yield concentration time courses for the product TNB for each of the four enzymes and the two substrates, alongside the assigned reaction conditions in the standardized EnzymeML format. This enables further processing and fitting of the data with kinetic models to yield kinetic parameters for the respective enzymes.\n", "\n", @@ -144533,22 +144533,22 @@ "outputs": [], "source": [ "McSAHH = plate.define_protein(\n", - " id=\"McSAHH\", name=\"Mycoplasma capricolum SAHH\", constant=True\n", + " id=\"McSAHH\", name=\"Methanocella conradii SAHH\", constant=True\n", ")\n", "\n", - "MtSAHH = plate.define_protein(\n", - " id=\"MtSAHH\",\n", - " name=\"Mycobacterium tuberculosis SAHH\",\n", + "MeSAHH = plate.define_protein(\n", + " id=\"MeSAHH\",\n", + " name=\"Methanohalobium evestigatum SAHH\",\n", " constant=True,\n", " uniprot_id=\"P9WGV3\",\n", ")\n", "\n", - "MhSAHH = plate.define_protein(\n", - " id=\"MhSAHH\", name=\"Mycoplasma hyorhinis SAHH\", constant=True\n", + "MiSAHH = plate.define_protein(\n", + " id=\"MiSAHH\", name=\"Methanocaldocossus infernus SAHH\", constant=True\n", ")\n", "\n", - "TmSAHH = plate.define_protein(\n", - " id=\"TmSAHH\", name=\"Thermotoga maritima SAHH\", constant=True, uniprot_id=\"O51933\"\n", + "MtSAHH = plate.define_protein(\n", + " id=\"MtSAHH\", name=\"Methanothrix thermoacetophila SAHH\", constant=True\n", ")" ] }, @@ -144558,13 +144558,13 @@ "source": [ "## Assign initial concentrations\n", "\n", - "After defining the molecules and proteins, the initial concentrations of each molecule and protein in the respective wells need to be assigned. The most convenient way to do this is by using an Excel spreadsheet - especially when dealing with many different species and initial conditions like in this example. To read the in the Excel file, the `assign_init_conditions_from_spreadsheet` method is used. Therefore, only the `path` and the `concentration_unit` arguments are required. This method goes through the sheets of an Excel spreadsheet. Each sheet represents the plate map of a molecule or protein. The sheet name must match the id of the molecule or protein defined for the plate. The initial concentration from the plate map in the Excel spreadsheet is assigned to the respective well. The following requirements must be met for the structure of the Excel spreadsheet:\n", + "After defining the molecules and proteins, an Excel spreadsheet can be generated using the `create_assignment_spreadsheet` method to assign the initial concentrations and pH values for each species in the respective wells. The spreadsheet can then be filled out with the respective initial conditions.\n", + "\n", + "To read the Excel file, the `assign_init_conditions_from_spreadsheet` method is used. Therefore, only the `path` and the `concentration_unit` arguments are required. This method goes through the sheets of the Excel spreadsheet and assigns the initial concentration from the plate map to the respective well.\n", "\n", "__Requirements structure of the Excel spreadsheet:__\n", "\n", - "- The first row must contain the column numbers from 1 to 12.\n", - "- The first column must contain the row letters from A to H.\n", - "- Leaving cells empty is means that the respective species in not present in the well over the course of the experiment.\n", + "- Leaving cells empty means that the respective species in not present in the well over the course of the experiment.\n", "- Products of the reaction need to be set to a concentration of 0.\n", "\n", "After the Excel spreadsheet is created, the initial concentrations can be assigned to the respective wells using the `assign_init_conditions_from_spreadsheet` method." @@ -144578,13 +144578,26 @@ { "data": { "text/html": [ - "
📍 Assigned 228 initial concentration coditions for ['MhSAHH', 'MtSAHH', 'McSAHH', 'SAH', 'DTNB', 'Hyc', 'BUFFER', \n",
-       "'SIH', 'TmSAHH', 'TNB'] from data/plate_map_SAHH_kinetics.xlsx to the plate.\n",
+       "
File data/plate_map_SAHH_kinetics.xlsx already exists. Set 'overwrite=True' to overwrite.\n",
        "
\n"
       ],
       "text/plain": [
-       "📍 Assigned \u001b[1;36m228\u001b[0m initial concentration coditions for \u001b[1;35m[\u001b[0m\u001b[1;35m'MhSAHH'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'MtSAHH'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'McSAHH'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'SAH'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'DTNB'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'Hyc'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'BUFFER'\u001b[0m\u001b[1;35m, \u001b[0m\n",
-       "\u001b[1;35m'SIH'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'TmSAHH'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'TNB'\u001b[0m\u001b[1;35m]\u001b[0m from data/plate_map_SAHH_kinetics.xlsx to the plate.\n"
+       "File data/plate_map_SAHH_kinetics.xlsx already exists. Set \u001b[32m'\u001b[0m\u001b[32moverwrite\u001b[0m\u001b[32m=\u001b[0m\u001b[32mTrue\u001b[0m\u001b[32m'\u001b[0m to overwrite.\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
📍 Assigned 228 initial concentration coditions for  ['Hyc', 'MtSAHH', 'MeSAHH', 'MiSAHH', 'SAH', 'SIH', 'McSAHH', \n",
+       "'DTNB', 'TNB', 'BUFFER'] from data/plate_map_SAHH_kinetics.xlsx to the plate.\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "📍 Assigned \u001b[1;36m228\u001b[0m initial concentration coditions for  \u001b[1;35m[\u001b[0m\u001b[1;35m'Hyc'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'MtSAHH'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'MeSAHH'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'MiSAHH'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'SAH'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'SIH'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'McSAHH'\u001b[0m\u001b[1;35m, \u001b[0m\n",
+       "\u001b[1;35m'DTNB'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'TNB'\u001b[0m\u001b[1;35m, \u001b[0m\u001b[1;35m'BUFFER'\u001b[0m\u001b[1;35m]\u001b[0m from data/plate_map_SAHH_kinetics.xlsx to the plate.\n"
       ]
      },
      "metadata": {},
@@ -144594,6 +144607,9 @@
    "source": [
     "from mtphandler.units import uM\n",
     "\n",
+    "# Create spreadsheet\n",
+    "plate.create_assignment_spreadsheet(path=\"data/plate_map_SAHH_kinetics.xlsx\")\n",
+    "\n",
     "# Blank Plate from plate map\n",
     "plate.assign_init_conditions_from_spreadsheet(\n",
     "    conc_unit=uM,\n",
@@ -144714,12 +144730,12 @@
     {
      "data": {
       "text/html": [
-       "
Mean absorption of Mycoplasma capricolum SAHH (McSAHH) at 5.0 umol / l: 0.2990 ± 20%  calculated based on wells \n",
+       "
Mean absorption of Methanocella conradii SAHH (McSAHH) at 5.0 umol / l: 0.2990 ± 20%  calculated based on wells \n",
        "['B5', 'B6', 'B7'].\n",
        "
\n"
       ],
       "text/plain": [
-       "Mean absorption of \u001b[1;35mMycoplasma capricolum SAHH\u001b[0m \u001b[1m(\u001b[0mMcSAHH\u001b[1m)\u001b[0m at \u001b[1;36m5.0\u001b[0m umol \u001b[35m/\u001b[0m l: \u001b[1;36m0.2990\u001b[0m ± \u001b[1;36m20\u001b[0m%  calculated based on wells \n",
+       "Mean absorption of \u001b[1;35mMethanocella conradii SAHH\u001b[0m \u001b[1m(\u001b[0mMcSAHH\u001b[1m)\u001b[0m at \u001b[1;36m5.0\u001b[0m umol \u001b[35m/\u001b[0m l: \u001b[1;36m0.2990\u001b[0m ± \u001b[1;36m20\u001b[0m%  calculated based on wells \n",
        "\u001b[1m[\u001b[0m\u001b[32m'B5'\u001b[0m, \u001b[32m'B6'\u001b[0m, \u001b[32m'B7'\u001b[0m\u001b[1m]\u001b[0m.\n"
       ]
      },
@@ -144729,12 +144745,12 @@
     {
      "data": {
       "text/html": [
-       "
Blanked 9 wells containing Mycoplasma capricolum SAHH.\n",
+       "
Blanked 9 wells containing Methanocella conradii SAHH.\n",
        "\n",
        "
\n"
       ],
       "text/plain": [
-       "Blanked \u001b[1;36m9\u001b[0m wells containing Mycoplasma capricolum SAHH.\n",
+       "Blanked \u001b[1;36m9\u001b[0m wells containing Methanocella conradii SAHH.\n",
        "\n"
       ]
      },
@@ -144744,13 +144760,13 @@
     {
      "data": {
       "text/html": [
-       "
Mean absorption of Mycobacterium tuberculosis SAHH (MtSAHH) at 5.0 umol / l: 0.3711 ± 7%  calculated based on wells\n",
-       "['E5', 'E6', 'E7'].\n",
+       "
Mean absorption of Methanohalobium evestigatum SAHH (MeSAHH) at 5.0 umol / l: 0.3711 ± 7%  calculated based on \n",
+       "wells ['E5', 'E6', 'E7'].\n",
        "
\n"
       ],
       "text/plain": [
-       "Mean absorption of \u001b[1;35mMycobacterium tuberculosis SAHH\u001b[0m \u001b[1m(\u001b[0mMtSAHH\u001b[1m)\u001b[0m at \u001b[1;36m5.0\u001b[0m umol \u001b[35m/\u001b[0m l: \u001b[1;36m0.3711\u001b[0m ± \u001b[1;36m7\u001b[0m%  calculated based on wells\n",
-       "\u001b[1m[\u001b[0m\u001b[32m'E5'\u001b[0m, \u001b[32m'E6'\u001b[0m, \u001b[32m'E7'\u001b[0m\u001b[1m]\u001b[0m.\n"
+       "Mean absorption of \u001b[1;35mMethanohalobium evestigatum SAHH\u001b[0m \u001b[1m(\u001b[0mMeSAHH\u001b[1m)\u001b[0m at \u001b[1;36m5.0\u001b[0m umol \u001b[35m/\u001b[0m l: \u001b[1;36m0.3711\u001b[0m ± \u001b[1;36m7\u001b[0m%  calculated based on \n",
+       "wells \u001b[1m[\u001b[0m\u001b[32m'E5'\u001b[0m, \u001b[32m'E6'\u001b[0m, \u001b[32m'E7'\u001b[0m\u001b[1m]\u001b[0m.\n"
       ]
      },
      "metadata": {},
@@ -144759,12 +144775,12 @@
     {
      "data": {
       "text/html": [
-       "
Blanked 9 wells containing Mycobacterium tuberculosis SAHH.\n",
+       "
Blanked 9 wells containing Methanohalobium evestigatum SAHH.\n",
        "\n",
        "
\n"
       ],
       "text/plain": [
-       "Blanked \u001b[1;36m9\u001b[0m wells containing Mycobacterium tuberculosis SAHH.\n",
+       "Blanked \u001b[1;36m9\u001b[0m wells containing Methanohalobium evestigatum SAHH.\n",
        "\n"
       ]
      },
@@ -144774,13 +144790,13 @@
     {
      "data": {
       "text/html": [
-       "
Mean absorption of Mycoplasma hyorhinis SAHH (MhSAHH) at 5.0 umol / l: 0.1771 ± 10%  calculated based on wells \n",
-       "['B8', 'B9', 'B10'].\n",
+       "
Mean absorption of Methanocaldocossus infernus SAHH (MiSAHH) at 5.0 umol / l: 0.1771 ± 10%  calculated based on \n",
+       "wells ['B8', 'B9', 'B10'].\n",
        "
\n"
       ],
       "text/plain": [
-       "Mean absorption of \u001b[1;35mMycoplasma hyorhinis SAHH\u001b[0m \u001b[1m(\u001b[0mMhSAHH\u001b[1m)\u001b[0m at \u001b[1;36m5.0\u001b[0m umol \u001b[35m/\u001b[0m l: \u001b[1;36m0.1771\u001b[0m ± \u001b[1;36m10\u001b[0m%  calculated based on wells \n",
-       "\u001b[1m[\u001b[0m\u001b[32m'B8'\u001b[0m, \u001b[32m'B9'\u001b[0m, \u001b[32m'B10'\u001b[0m\u001b[1m]\u001b[0m.\n"
+       "Mean absorption of \u001b[1;35mMethanocaldocossus infernus SAHH\u001b[0m \u001b[1m(\u001b[0mMiSAHH\u001b[1m)\u001b[0m at \u001b[1;36m5.0\u001b[0m umol \u001b[35m/\u001b[0m l: \u001b[1;36m0.1771\u001b[0m ± \u001b[1;36m10\u001b[0m%  calculated based on \n",
+       "wells \u001b[1m[\u001b[0m\u001b[32m'B8'\u001b[0m, \u001b[32m'B9'\u001b[0m, \u001b[32m'B10'\u001b[0m\u001b[1m]\u001b[0m.\n"
       ]
      },
      "metadata": {},
@@ -144789,12 +144805,12 @@
     {
      "data": {
       "text/html": [
-       "
Blanked 9 wells containing Mycoplasma hyorhinis SAHH.\n",
+       "
Blanked 9 wells containing Methanocaldocossus infernus SAHH.\n",
        "\n",
        "
\n"
       ],
       "text/plain": [
-       "Blanked \u001b[1;36m9\u001b[0m wells containing Mycoplasma hyorhinis SAHH.\n",
+       "Blanked \u001b[1;36m9\u001b[0m wells containing Methanocaldocossus infernus SAHH.\n",
        "\n"
       ]
      },
@@ -144804,13 +144820,13 @@
     {
      "data": {
       "text/html": [
-       "
Mean absorption of Thermotoga maritima SAHH (TmSAHH) at 5.0 umol / l: 0.0199 ± 29%  calculated based on wells \n",
-       "['E8', 'E9', 'E10'].\n",
+       "
Mean absorption of Methanothrix thermoacetophila SAHH (MtSAHH) at 5.0 umol / l: 0.0199 ± 29%  calculated based on \n",
+       "wells ['E8', 'E9', 'E10'].\n",
        "
\n"
       ],
       "text/plain": [
-       "Mean absorption of \u001b[1;35mThermotoga maritima SAHH\u001b[0m \u001b[1m(\u001b[0mTmSAHH\u001b[1m)\u001b[0m at \u001b[1;36m5.0\u001b[0m umol \u001b[35m/\u001b[0m l: \u001b[1;36m0.0199\u001b[0m ± \u001b[1;36m29\u001b[0m%  calculated based on wells \n",
-       "\u001b[1m[\u001b[0m\u001b[32m'E8'\u001b[0m, \u001b[32m'E9'\u001b[0m, \u001b[32m'E10'\u001b[0m\u001b[1m]\u001b[0m.\n"
+       "Mean absorption of \u001b[1;35mMethanothrix thermoacetophila SAHH\u001b[0m \u001b[1m(\u001b[0mMtSAHH\u001b[1m)\u001b[0m at \u001b[1;36m5.0\u001b[0m umol \u001b[35m/\u001b[0m l: \u001b[1;36m0.0199\u001b[0m ± \u001b[1;36m29\u001b[0m%  calculated based on \n",
+       "wells \u001b[1m[\u001b[0m\u001b[32m'E8'\u001b[0m, \u001b[32m'E9'\u001b[0m, \u001b[32m'E10'\u001b[0m\u001b[1m]\u001b[0m.\n"
       ]
      },
      "metadata": {},
@@ -144819,12 +144835,12 @@
     {
      "data": {
       "text/html": [
-       "
Blanked 9 wells containing Thermotoga maritima SAHH.\n",
+       "
Blanked 9 wells containing Methanothrix thermoacetophila SAHH.\n",
        "\n",
        "
\n"
       ],
       "text/plain": [
-       "Blanked \u001b[1;36m9\u001b[0m wells containing Thermotoga maritima SAHH.\n",
+       "Blanked \u001b[1;36m9\u001b[0m wells containing Methanothrix thermoacetophila SAHH.\n",
        "\n"
       ]
      },
@@ -144842,9 +144858,9 @@
     ")\n",
     "plate.blank_species(SIH)\n",
     "plate.blank_species(McSAHH)\n",
-    "plate.blank_species(MtSAHH)\n",
-    "plate.blank_species(MhSAHH)\n",
-    "plate.blank_species(TmSAHH)"
+    "plate.blank_species(MeSAHH)\n",
+    "plate.blank_species(MiSAHH)\n",
+    "plate.blank_species(MtSAHH)"
    ]
   },
   {
@@ -147716,7 +147732,7 @@
    ],
    "source": [
     "enzml_TmSAHH_SAH = plate.to_enzymeml(\n",
-    "    name=\"TmSAHH with SAH substrate kinetic assay\",\n",
+    "    name=\"MtSAHH with SAH substrate kinetic assay\",\n",
     "    detected_molecule=TNB,\n",
     "    well_ids=[\"F8\", \"F9\", \"F10\", \"E8\", \"E9\", \"E10\"],\n",
     "    to_concentration=True,\n",
@@ -147749,7 +147765,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
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       ]
diff --git a/docs/examples/data/enzymeml_TmSAHH_SAH.json b/docs/examples/data/enzymeml_TmSAHH_SAH.json
index 1c191ca..8f3dccd 100644
--- a/docs/examples/data/enzymeml_TmSAHH_SAH.json
+++ b/docs/examples/data/enzymeml_TmSAHH_SAH.json
@@ -1,5 +1,5 @@
 {
-    "name": "TmSAHH with SAH substrate kinetic assay",
+    "name": "MtSAHH with SAH substrate kinetic assay",
     "references": [],
     "created": null,
     "modified": null,
@@ -18,7 +18,7 @@
                         "exponent": 1,
                         "multiplier": null,
                         "scale": 1.0,
-                        "ld_id": "enzml:BaseUnit/085c0609-ab77-4c89-9686-e00e2628535c",
+                        "ld_id": "enzml:BaseUnit/798bee96-593b-42e7-bd97-be300a2db202",
                         "ld_type": [
                             "enzml:BaseUnit"
                         ],
@@ -29,7 +29,7 @@
                         }
                     }
                 ],
-                "ld_id": "enzml:UnitDefinition/380763e6-e15c-4f8e-b8a0-4a286d0da22d",
+                "ld_id": "enzml:UnitDefinition/1122c109-28d6-4b14-9be7-46af8895bcb8",
                 "ld_type": [
                     "OBO:UO_0000000"
                 ],
@@ -40,7 +40,7 @@
                 }
             },
             "constant": true,
-            "ld_id": "enzml:Vessel/42805267-b5a9-4bfe-b348-061d4f044a46",
+            "ld_id": "enzml:Vessel/8a49291f-fee1-473c-abb7-c0b3e14941d7",
             "ld_type": [
                 "enzml:Vessel",
                 "OBO:OBI_0400081"
@@ -61,7 +61,7 @@
     "proteins": [
         {
             "id": "McSAHH",
-            "name": "Mycoplasma capricolum SAHH",
+            "name": "Methanocella conradii SAHH",
             "constant": true,
             "sequence": null,
             "vessel_id": "v1",
@@ -69,7 +69,7 @@
             "organism": null,
             "organism_tax_id": null,
             "references": [],
-            "ld_id": "enzml:Protein/efc8d1cb-4880-4115-9980-f0ae647afa2b",
+            "ld_id": "enzml:Protein/d01882a3-f8c3-455a-b540-185317cea501",
             "ld_type": [
                 "enzml:Protein",
                 "schema:Protein"
@@ -98,8 +98,8 @@
             }
         },
         {
-            "id": "MtSAHH",
-            "name": "Mycobacterium tuberculosis SAHH",
+            "id": "MeSAHH",
+            "name": "Methanohalobium evestigatum SAHH",
             "constant": true,
             "sequence": null,
             "vessel_id": "v1",
@@ -107,7 +107,7 @@
             "organism": null,
             "organism_tax_id": null,
             "references": [],
-            "ld_id": "enzml:Protein/84b031d1-b665-4d92-b783-c50f39095dd7",
+            "ld_id": "enzml:Protein/511ddd58-ecfd-4007-ac20-b0352f995eb5",
             "ld_type": [
                 "enzml:Protein",
                 "schema:Protein"
@@ -136,8 +136,8 @@
             }
         },
         {
-            "id": "MhSAHH",
-            "name": "Mycoplasma hyorhinis SAHH",
+            "id": "MiSAHH",
+            "name": "Methanocaldocossus infernus SAHH",
             "constant": true,
             "sequence": null,
             "vessel_id": "v1",
@@ -145,7 +145,7 @@
             "organism": null,
             "organism_tax_id": null,
             "references": [],
-            "ld_id": "enzml:Protein/2d55f1d7-2376-4fc1-b65e-0261440fc0c4",
+            "ld_id": "enzml:Protein/63e5b726-93e7-4262-a315-bad8cc809737",
             "ld_type": [
                 "enzml:Protein",
                 "schema:Protein"
@@ -174,8 +174,8 @@
             }
         },
         {
-            "id": "TmSAHH",
-            "name": "Thermotoga maritima SAHH",
+            "id": "MtSAHH",
+            "name": "Methanothrix thermoacetophila SAHH",
             "constant": true,
             "sequence": null,
             "vessel_id": "v1",
@@ -183,7 +183,7 @@
             "organism": null,
             "organism_tax_id": null,
             "references": [],
-            "ld_id": "enzml:Protein/abbc1199-549a-4ebb-8040-1873c6ea95f0",
+            "ld_id": "enzml:Protein/7cc3cb09-604d-4ab8-ae23-52d9b8132e95",
             "ld_type": [
                 "enzml:Protein",
                 "schema:Protein"
@@ -223,7 +223,7 @@
             "inchi": null,
             "inchikey": null,
             "references": [],
-            "ld_id": "enzml:SmallMolecule/f5ddc0d9-cfb3-44ec-876e-17a4eeec3def",
+            "ld_id": "enzml:SmallMolecule/da423176-fb7b-44d4-8cfe-47fee09a532a",
             "ld_type": [
                 "enzml:SmallMolecule"
             ],
@@ -414,7 +414,7 @@
             "name": "photometric measurement",
             "species_data": [
                 {
-                    "species_id": "TmSAHH",
+                    "species_id": "MtSAHH",
                     "initial": 5.0,
                     "data_unit": {
                         "id": null,
@@ -425,7 +425,7 @@
                                 "exponent": 1,
                                 "multiplier": null,
                                 "scale": -6.0,
-                                "ld_id": "md:BaseUnit/a7924577-c3ed-42b9-9574-0ac6a0b90357",
+                                "ld_id": "md:BaseUnit/8dcf6ad8-445b-45cd-9dfd-7e96ce3c4028",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -438,7 +438,7 @@
                                 "exponent": -1,
                                 "multiplier": null,
                                 "scale": 1.0,
-                                "ld_id": "md:BaseUnit/94a08161-ec0e-4dd7-a259-a1922ac4150e",
+                                "ld_id": "md:BaseUnit/9f506a24-d2f0-44ef-b3b9-e127ed5437a3",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -468,93 +468,7 @@
                                 "exponent": 1,
                                 "multiplier": 60.0,
                                 "scale": 1.0,
-                                "ld_id": "md:BaseUnit/703240e5-176d-43c5-bb5b-2caa43f67160",
-                                "ld_type": [
-                                    "md:BaseUnit"
-                                ],
-                                "ld_context": {
-                                    "md": "https://github.com/FAIRChemistry/MTPHandler"
-                                }
-                            }
-                        ],
-                        "ld_id": "OBO:UO_0000031",
-                        "ld_type": [
-                            "OBO:UO_0000000"
-                        ],
-                        "ld_context": {
-                            "md": "https://github.com/FAIRChemistry/MTPHandler"
-                        }
-                    },
-                    "is_simulated": false,
-                    "ld_id": "enzml:MeasurementData/2d97c741-c14f-49cd-a02f-84a032d483e0",
-                    "ld_type": [
-                        "enzml:MeasurementData"
-                    ],
-                    "ld_context": {
-                        "enzml": "http://www.enzymeml.org/v2/",
-                        "schema": "https://schema.org/",
-                        "OBO": "http://purl.obolibrary.org/obo/",
-                        "species_id": {
-                            "@type": "@id"
-                        }
-                    }
-                },
-                {
-                    "species_id": "BUFFER",
-                    "initial": 50.0,
-                    "data_unit": {
-                        "id": null,
-                        "name": "umol / l",
-                        "base_units": [
-                            {
-                                "kind": "mole",
-                                "exponent": 1,
-                                "multiplier": null,
-                                "scale": -6.0,
-                                "ld_id": "md:BaseUnit/a7924577-c3ed-42b9-9574-0ac6a0b90357",
-                                "ld_type": [
-                                    "md:BaseUnit"
-                                ],
-                                "ld_context": {
-                                    "md": "https://github.com/FAIRChemistry/MTPHandler"
-                                }
-                            },
-                            {
-                                "kind": "litre",
-                                "exponent": -1,
-                                "multiplier": null,
-                                "scale": 1.0,
-                                "ld_id": "md:BaseUnit/94a08161-ec0e-4dd7-a259-a1922ac4150e",
-                                "ld_type": [
-                                    "md:BaseUnit"
-                                ],
-                                "ld_context": {
-                                    "md": "https://github.com/FAIRChemistry/MTPHandler"
-                                }
-                            }
-                        ],
-                        "ld_id": "OBO:UO_0000064",
-                        "ld_type": [
-                            "OBO:UO_0000000"
-                        ],
-                        "ld_context": {
-                            "md": "https://github.com/FAIRChemistry/MTPHandler"
-                        }
-                    },
-                    "data_type": "http://purl.obolibrary.org/obo/PATO_0000033",
-                    "prepared": 50.0,
-                    "data": [],
-                    "time": [],
-                    "time_unit": {
-                        "id": null,
-                        "name": "s",
-                        "base_units": [
-                            {
-                                "kind": "second",
-                                "exponent": 1,
-                                "multiplier": 60.0,
-                                "scale": 1.0,
-                                "ld_id": "md:BaseUnit/703240e5-176d-43c5-bb5b-2caa43f67160",
+                                "ld_id": "md:BaseUnit/400db508-a105-4735-a873-e37b67c2c4da",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -572,7 +486,7 @@
                         }
                     },
                     "is_simulated": false,
-                    "ld_id": "enzml:MeasurementData/487ebfd9-6d55-4f0e-897c-d211a2f6dae9",
+                    "ld_id": "enzml:MeasurementData/19a13886-3936-4aa5-9544-dde918f5b675",
                     "ld_type": [
                         "enzml:MeasurementData"
                     ],
@@ -597,7 +511,7 @@
                                 "exponent": 1,
                                 "multiplier": null,
                                 "scale": -6.0,
-                                "ld_id": "md:BaseUnit/a7924577-c3ed-42b9-9574-0ac6a0b90357",
+                                "ld_id": "md:BaseUnit/8dcf6ad8-445b-45cd-9dfd-7e96ce3c4028",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -610,7 +524,7 @@
                                 "exponent": -1,
                                 "multiplier": null,
                                 "scale": 1.0,
-                                "ld_id": "md:BaseUnit/94a08161-ec0e-4dd7-a259-a1922ac4150e",
+                                "ld_id": "md:BaseUnit/9f506a24-d2f0-44ef-b3b9-e127ed5437a3",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -2084,7 +1998,7 @@
                                 "exponent": 1,
                                 "multiplier": 60.0,
                                 "scale": 1.0,
-                                "ld_id": "md:BaseUnit/703240e5-176d-43c5-bb5b-2caa43f67160",
+                                "ld_id": "md:BaseUnit/400db508-a105-4735-a873-e37b67c2c4da",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -2102,7 +2016,7 @@
                         }
                     },
                     "is_simulated": false,
-                    "ld_id": "enzml:MeasurementData/97d7bf19-8513-4730-a815-2a84b8ea2625",
+                    "ld_id": "enzml:MeasurementData/7f7f6627-e319-4ecf-aab7-460187f2c802",
                     "ld_type": [
                         "enzml:MeasurementData"
                     ],
@@ -2114,61 +2028,10 @@
                             "@type": "@id"
                         }
                     }
-                }
-            ],
-            "group_id": "",
-            "ph": 7.4,
-            "temperature": 37.04466019417476,
-            "temperature_unit": {
-                "id": null,
-                "name": "Celsius",
-                "base_units": [
-                    {
-                        "kind": "celsius",
-                        "exponent": 1,
-                        "multiplier": null,
-                        "scale": 1.0,
-                        "ld_id": "md:BaseUnit/d0bcada3-1c11-4e8f-aa1e-aebc218f6be5",
-                        "ld_type": [
-                            "md:BaseUnit"
-                        ],
-                        "ld_context": {
-                            "md": "https://github.com/FAIRChemistry/MTPHandler"
-                        }
-                    }
-                ],
-                "ld_id": "md:UnitDefinition/6ac5c79a-36fd-4f2c-acba-6ac83b58fdba",
-                "ld_type": [
-                    "OBO:UO_0000000"
-                ],
-                "ld_context": {
-                    "md": "https://github.com/FAIRChemistry/MTPHandler"
-                }
-            },
-            "ld_id": "enzml:Measurement/cb1495b3-cd84-4c00-ac7c-4c79c9aa7619",
-            "ld_type": [
-                "enzml:Measurement"
-            ],
-            "ld_context": {
-                "enzml": "http://www.enzymeml.org/v2/",
-                "schema": "https://schema.org/",
-                "OBO": "http://purl.obolibrary.org/obo/",
-                "id": {
-                    "@id": "schema:identifier",
-                    "@type": "@id"
                 },
-                "group_id": {
-                    "@type": "@id"
-                }
-            }
-        },
-        {
-            "id": "F8",
-            "name": "photometric measurement",
-            "species_data": [
                 {
-                    "species_id": "Hyc",
-                    "initial": 0.0,
+                    "species_id": "BUFFER",
+                    "initial": 50.0,
                     "data_unit": {
                         "id": null,
                         "name": "umol / l",
@@ -2178,7 +2041,7 @@
                                 "exponent": 1,
                                 "multiplier": null,
                                 "scale": -6.0,
-                                "ld_id": "md:BaseUnit/a7924577-c3ed-42b9-9574-0ac6a0b90357",
+                                "ld_id": "md:BaseUnit/8dcf6ad8-445b-45cd-9dfd-7e96ce3c4028",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -2191,7 +2054,7 @@
                                 "exponent": -1,
                                 "multiplier": null,
                                 "scale": 1.0,
-                                "ld_id": "md:BaseUnit/94a08161-ec0e-4dd7-a259-a1922ac4150e",
+                                "ld_id": "md:BaseUnit/9f506a24-d2f0-44ef-b3b9-e127ed5437a3",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -2209,7 +2072,7 @@
                         }
                     },
                     "data_type": "http://purl.obolibrary.org/obo/PATO_0000033",
-                    "prepared": 0.0,
+                    "prepared": 50.0,
                     "data": [],
                     "time": [],
                     "time_unit": {
@@ -2221,7 +2084,7 @@
                                 "exponent": 1,
                                 "multiplier": 60.0,
                                 "scale": 1.0,
-                                "ld_id": "md:BaseUnit/703240e5-176d-43c5-bb5b-2caa43f67160",
+                                "ld_id": "md:BaseUnit/400db508-a105-4735-a873-e37b67c2c4da",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -2239,7 +2102,7 @@
                         }
                     },
                     "is_simulated": false,
-                    "ld_id": "enzml:MeasurementData/1ebd3b14-4e5f-4507-9271-065086d3db7b",
+                    "ld_id": "enzml:MeasurementData/148453d8-1738-4222-a7b3-b0589edc52aa",
                     "ld_type": [
                         "enzml:MeasurementData"
                     ],
@@ -2251,9 +2114,60 @@
                             "@type": "@id"
                         }
                     }
+                }
+            ],
+            "group_id": "",
+            "ph": 7.4,
+            "temperature": 37.04466019417476,
+            "temperature_unit": {
+                "id": null,
+                "name": "Celsius",
+                "base_units": [
+                    {
+                        "kind": "celsius",
+                        "exponent": 1,
+                        "multiplier": null,
+                        "scale": 1.0,
+                        "ld_id": "md:BaseUnit/b54ba3e4-7ce5-45c5-90b8-0c3903af81e8",
+                        "ld_type": [
+                            "md:BaseUnit"
+                        ],
+                        "ld_context": {
+                            "md": "https://github.com/FAIRChemistry/MTPHandler"
+                        }
+                    }
+                ],
+                "ld_id": "md:UnitDefinition/bb4b9f87-48ab-425d-b1b8-1b2553b53c07",
+                "ld_type": [
+                    "OBO:UO_0000000"
+                ],
+                "ld_context": {
+                    "md": "https://github.com/FAIRChemistry/MTPHandler"
+                }
+            },
+            "ld_id": "enzml:Measurement/ab99659c-31eb-4a20-9e89-b335395db905",
+            "ld_type": [
+                "enzml:Measurement"
+            ],
+            "ld_context": {
+                "enzml": "http://www.enzymeml.org/v2/",
+                "schema": "https://schema.org/",
+                "OBO": "http://purl.obolibrary.org/obo/",
+                "id": {
+                    "@id": "schema:identifier",
+                    "@type": "@id"
                 },
+                "group_id": {
+                    "@type": "@id"
+                }
+            }
+        },
+        {
+            "id": "F8",
+            "name": "photometric measurement",
+            "species_data": [
                 {
-                    "species_id": "TmSAHH",
+                    "species_id": "MtSAHH",
                     "initial": 5.0,
                     "data_unit": {
                         "id": null,
@@ -2264,7 +2178,7 @@
                                 "exponent": 1,
                                 "multiplier": null,
                                 "scale": -6.0,
-                                "ld_id": "md:BaseUnit/a7924577-c3ed-42b9-9574-0ac6a0b90357",
+                                "ld_id": "md:BaseUnit/8dcf6ad8-445b-45cd-9dfd-7e96ce3c4028",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -2277,7 +2191,7 @@
                                 "exponent": -1,
                                 "multiplier": null,
                                 "scale": 1.0,
-                                "ld_id": "md:BaseUnit/94a08161-ec0e-4dd7-a259-a1922ac4150e",
+                                "ld_id": "md:BaseUnit/9f506a24-d2f0-44ef-b3b9-e127ed5437a3",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -2307,7 +2221,7 @@
                                 "exponent": 1,
                                 "multiplier": 60.0,
                                 "scale": 1.0,
-                                "ld_id": "md:BaseUnit/703240e5-176d-43c5-bb5b-2caa43f67160",
+                                "ld_id": "md:BaseUnit/400db508-a105-4735-a873-e37b67c2c4da",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -2325,7 +2239,7 @@
                         }
                     },
                     "is_simulated": false,
-                    "ld_id": "enzml:MeasurementData/47fb1524-ca52-4f7a-a04e-8bb7657e8de1",
+                    "ld_id": "enzml:MeasurementData/053188c9-7515-4139-b032-11ace9de943c",
                     "ld_type": [
                         "enzml:MeasurementData"
                     ],
@@ -2339,8 +2253,8 @@
                     }
                 },
                 {
-                    "species_id": "DTNB",
-                    "initial": 500.0,
+                    "species_id": "Hyc",
+                    "initial": 0.0,
                     "data_unit": {
                         "id": null,
                         "name": "umol / l",
@@ -2350,7 +2264,7 @@
                                 "exponent": 1,
                                 "multiplier": null,
                                 "scale": -6.0,
-                                "ld_id": "md:BaseUnit/a7924577-c3ed-42b9-9574-0ac6a0b90357",
+                                "ld_id": "md:BaseUnit/8dcf6ad8-445b-45cd-9dfd-7e96ce3c4028",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -2363,7 +2277,7 @@
                                 "exponent": -1,
                                 "multiplier": null,
                                 "scale": 1.0,
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                                 "scale": 1.0,
-                                "ld_id": "md:BaseUnit/94a08161-ec0e-4dd7-a259-a1922ac4150e",
+                                "ld_id": "md:BaseUnit/9f506a24-d2f0-44ef-b3b9-e127ed5437a3",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -11623,7 +11451,179 @@
                                 "exponent": 1,
                                 "multiplier": 60.0,
                                 "scale": 1.0,
-                                "ld_id": "md:BaseUnit/703240e5-176d-43c5-bb5b-2caa43f67160",
+                                "ld_id": "md:BaseUnit/400db508-a105-4735-a873-e37b67c2c4da",
+                                "ld_type": [
+                                    "md:BaseUnit"
+                                ],
+                                "ld_context": {
+                                    "md": "https://github.com/FAIRChemistry/MTPHandler"
+                                }
+                            }
+                        ],
+                        "ld_id": "OBO:UO_0000031",
+                        "ld_type": [
+                            "OBO:UO_0000000"
+                        ],
+                        "ld_context": {
+                            "md": "https://github.com/FAIRChemistry/MTPHandler"
+                        }
+                    },
+                    "is_simulated": false,
+                    "ld_id": "enzml:MeasurementData/b6b07520-a30c-4b47-a545-af00a784f235",
+                    "ld_type": [
+                        "enzml:MeasurementData"
+                    ],
+                    "ld_context": {
+                        "enzml": "http://www.enzymeml.org/v2/",
+                        "schema": "https://schema.org/",
+                        "OBO": "http://purl.obolibrary.org/obo/",
+                        "species_id": {
+                            "@type": "@id"
+                        }
+                    }
+                },
+                {
+                    "species_id": "BUFFER",
+                    "initial": 50.0,
+                    "data_unit": {
+                        "id": null,
+                        "name": "umol / l",
+                        "base_units": [
+                            {
+                                "kind": "mole",
+                                "exponent": 1,
+                                "multiplier": null,
+                                "scale": -6.0,
+                                "ld_id": "md:BaseUnit/8dcf6ad8-445b-45cd-9dfd-7e96ce3c4028",
+                                "ld_type": [
+                                    "md:BaseUnit"
+                                ],
+                                "ld_context": {
+                                    "md": "https://github.com/FAIRChemistry/MTPHandler"
+                                }
+                            },
+                            {
+                                "kind": "litre",
+                                "exponent": -1,
+                                "multiplier": null,
+                                "scale": 1.0,
+                                "ld_id": "md:BaseUnit/9f506a24-d2f0-44ef-b3b9-e127ed5437a3",
+                                "ld_type": [
+                                    "md:BaseUnit"
+                                ],
+                                "ld_context": {
+                                    "md": "https://github.com/FAIRChemistry/MTPHandler"
+                                }
+                            }
+                        ],
+                        "ld_id": "OBO:UO_0000064",
+                        "ld_type": [
+                            "OBO:UO_0000000"
+                        ],
+                        "ld_context": {
+                            "md": "https://github.com/FAIRChemistry/MTPHandler"
+                        }
+                    },
+                    "data_type": "http://purl.obolibrary.org/obo/PATO_0000033",
+                    "prepared": 50.0,
+                    "data": [],
+                    "time": [],
+                    "time_unit": {
+                        "id": null,
+                        "name": "s",
+                        "base_units": [
+                            {
+                                "kind": "second",
+                                "exponent": 1,
+                                "multiplier": 60.0,
+                                "scale": 1.0,
+                                "ld_id": "md:BaseUnit/400db508-a105-4735-a873-e37b67c2c4da",
+                                "ld_type": [
+                                    "md:BaseUnit"
+                                ],
+                                "ld_context": {
+                                    "md": "https://github.com/FAIRChemistry/MTPHandler"
+                                }
+                            }
+                        ],
+                        "ld_id": "OBO:UO_0000031",
+                        "ld_type": [
+                            "OBO:UO_0000000"
+                        ],
+                        "ld_context": {
+                            "md": "https://github.com/FAIRChemistry/MTPHandler"
+                        }
+                    },
+                    "is_simulated": false,
+                    "ld_id": "enzml:MeasurementData/0d3dccd9-a316-49e3-8e6d-a273e7ca63ac",
+                    "ld_type": [
+                        "enzml:MeasurementData"
+                    ],
+                    "ld_context": {
+                        "enzml": "http://www.enzymeml.org/v2/",
+                        "schema": "https://schema.org/",
+                        "OBO": "http://purl.obolibrary.org/obo/",
+                        "species_id": {
+                            "@type": "@id"
+                        }
+                    }
+                },
+                {
+                    "species_id": "SAH",
+                    "initial": 200.0,
+                    "data_unit": {
+                        "id": null,
+                        "name": "umol / l",
+                        "base_units": [
+                            {
+                                "kind": "mole",
+                                "exponent": 1,
+                                "multiplier": null,
+                                "scale": -6.0,
+                                "ld_id": "md:BaseUnit/8dcf6ad8-445b-45cd-9dfd-7e96ce3c4028",
+                                "ld_type": [
+                                    "md:BaseUnit"
+                                ],
+                                "ld_context": {
+                                    "md": "https://github.com/FAIRChemistry/MTPHandler"
+                                }
+                            },
+                            {
+                                "kind": "litre",
+                                "exponent": -1,
+                                "multiplier": null,
+                                "scale": 1.0,
+                                "ld_id": "md:BaseUnit/9f506a24-d2f0-44ef-b3b9-e127ed5437a3",
+                                "ld_type": [
+                                    "md:BaseUnit"
+                                ],
+                                "ld_context": {
+                                    "md": "https://github.com/FAIRChemistry/MTPHandler"
+                                }
+                            }
+                        ],
+                        "ld_id": "OBO:UO_0000064",
+                        "ld_type": [
+                            "OBO:UO_0000000"
+                        ],
+                        "ld_context": {
+                            "md": "https://github.com/FAIRChemistry/MTPHandler"
+                        }
+                    },
+                    "data_type": "http://purl.obolibrary.org/obo/PATO_0000033",
+                    "prepared": 200.0,
+                    "data": [],
+                    "time": [],
+                    "time_unit": {
+                        "id": null,
+                        "name": "s",
+                        "base_units": [
+                            {
+                                "kind": "second",
+                                "exponent": 1,
+                                "multiplier": 60.0,
+                                "scale": 1.0,
+                                "ld_id": "md:BaseUnit/400db508-a105-4735-a873-e37b67c2c4da",
                                 "ld_type": [
                                     "md:BaseUnit"
                                 ],
@@ -11641,7 +11641,7 @@
                         }
                     },
                     "is_simulated": false,
-                    "ld_id": "enzml:MeasurementData/8e3de43f-f7a1-49a1-9047-3ac9522dd7b6",
+                    "ld_id": "enzml:MeasurementData/77de4841-ce83-42d8-9d49-ea7b75a6d3c2",
                     "ld_type": [
                         "enzml:MeasurementData"
                     ],
@@ -11667,7 +11667,7 @@
                         "exponent": 1,
                         "multiplier": null,
                         "scale": 1.0,
-                        "ld_id": "md:BaseUnit/d0bcada3-1c11-4e8f-aa1e-aebc218f6be5",
+                        "ld_id": "md:BaseUnit/b54ba3e4-7ce5-45c5-90b8-0c3903af81e8",
                         "ld_type": [
                             "md:BaseUnit"
                         ],
@@ -11676,7 +11676,7 @@
                         }
                     }
                 ],
-                "ld_id": "md:UnitDefinition/6ac5c79a-36fd-4f2c-acba-6ac83b58fdba",
+                "ld_id": "md:UnitDefinition/bb4b9f87-48ab-425d-b1b8-1b2553b53c07",
                 "ld_type": [
                     "OBO:UO_0000000"
                 ],
@@ -11684,7 +11684,7 @@
                     "md": "https://github.com/FAIRChemistry/MTPHandler"
                 }
             },
-            "ld_id": "enzml:Measurement/3125d780-d5c9-40c7-9a7f-b4e5ade74f94",
+            "ld_id": "enzml:Measurement/90f0c28b-d092-424e-aec0-a85f92700df4",
             "ld_type": [
                 "enzml:Measurement"
             ],
@@ -11704,7 +11704,7 @@
     ],
     "equations": [],
     "parameters": [],
-    "ld_id": "enzml:EnzymeMLDocument/b8f2c4ae-16f4-4816-8450-3d543c6d7af2",
+    "ld_id": "enzml:EnzymeMLDocument/21b8aaeb-d342-4250-b7a3-efe0b78d09ee",
     "ld_type": [
         "enzml:EnzymeMLDocument"
     ],
diff --git a/docs/examples/data/new_device_calib.xlsx b/docs/examples/data/new_device_calib.xlsx
new file mode 100644
index 0000000..bcaf75c
Binary files /dev/null and b/docs/examples/data/new_device_calib.xlsx differ
diff --git a/docs/examples/data/new_device_kinetic.xlsx b/docs/examples/data/new_device_kinetic.xlsx
new file mode 100644
index 0000000..8dac39c
Binary files /dev/null and b/docs/examples/data/new_device_kinetic.xlsx differ
diff --git a/docs/examples/data/plate_map_SAHH_kinetics.xlsx b/docs/examples/data/plate_map_SAHH_kinetics.xlsx
index 2207ec4..069454d 100644
Binary files a/docs/examples/data/plate_map_SAHH_kinetics.xlsx and b/docs/examples/data/plate_map_SAHH_kinetics.xlsx differ
diff --git a/docs/examples/data/~$20240719_biotek_FDH.V9_0.02enzyme_kinetics.xlsx b/docs/examples/data/~$20240719_biotek_FDH.V9_0.02enzyme_kinetics.xlsx
deleted file mode 100644
index 1ac3755..0000000
Binary files a/docs/examples/data/~$20240719_biotek_FDH.V9_0.02enzyme_kinetics.xlsx and /dev/null differ
diff --git a/docs/examples/data/~$plate_map_SAHH_kinetics.xlsx b/docs/examples/data/~$plate_map_SAHH_kinetics.xlsx
deleted file mode 100644
index 1ac3755..0000000
Binary files a/docs/examples/data/~$plate_map_SAHH_kinetics.xlsx and /dev/null differ
diff --git a/docs/images/plate SAHH.png b/docs/images/plate SAHH.png
index 37b7ab4..ed2d522 100644
Binary files a/docs/images/plate SAHH.png and b/docs/images/plate SAHH.png differ
diff --git a/docs/usage.md b/docs/usage.md
index c910a23..2d1c053 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -23,12 +23,12 @@ graph LR
     K1 --> TNB[2-nitro-5-thiobenzoate
 detected at λ412 nm]
 ```
 
-Kinetic enzyme assays for the hydrolysis of _S_-adenosyl-_L_-homocysteine (`SAH`) or _S_-adenosyl-_L_-cysteine (`SIH`), catalyzed by four different _S_-adenosyl-_L_-homocysteine hydrolase (SAHH) enzymes, were conducted using a 96-well plate. Each reaction was carried out in triplicate for the respective enzymes as shown in the plate layout.  
-For detection, Ellman's reagent 5,5'-dithiobis-2-nitrobenzoate (`DTNB`) was used. It reacts with the homocysteine (`Hcy`) produced from the hydrolysis of SAH or SIH to form 2-nitro-5-thiobenzoate (`TNB`), which can be measured at an absorbance of 412 nm. The concentration of TNB was quantified using a calibration curve, which was also recorded on the same plate.
+Kinetic enzyme assays for the hydrolysis of _S_-adenosyl-_L_-homocysteine (`SAH`) or _S_-inosly-_L_-homocysteine (`SIH`), catalyzed by four different _S_-adenosyl-_L_-homocysteine hydrolase (SAHH) enzymes, were conducted using a 96-well plate. Each reaction was carried out in triplicate for the respective enzymes as shown in the plate layout.  
+For detection, Ellman's reagent 5,5'-dithiobis-2-nitrobenzoate (`DTNB`) was used. It reacts with the homocysteine (`Hcy`) produced from the hydrolysis of SAH or SIH to form 2-nitro-5-thiobenzoate (`TNB`), which has an absorbance maximum at 412 nm. The concentration of TNB was quantified using a calibration curve, which was also recorded on the same plate.
 
 ![Plate Layout](images/plate SAHH.png){: style="height:350px", align=right}
 
-The SAHH enzymes from _Mycoplasma capricolum_ (`McSAHH`), _Mycobacterium tuberculosis_ (`MtSAHH`), _Mycoplasma hyorhinis_ (`MhSAHH`), and _Thermotoga maritima_ (`TmSAHH`) were applied at a concentration of 5 µM, whereas the substrate concentration was 200 µM in all assays. The calibration curve for TNB was prepared in the range of 0 to 250 µM. Ellman's reagent was used at a concentration of 500 µM.
+The SAHH enzymes from _Methanocella conradii_ (`McSAHH`), Methanohalobium evestigatum (`MeSAHH`), _Methanocaldocossus infernus_ (`MiSAHH`), and _Methanothrix thermoacetophila_ (`MtSAHH`) were applied at a concentration of 5 µM, whereas the substrate concentration was 200 µM in all assays. The calibration curve for TNB was prepared in the range of 0 to 250 µM. Ellman's reagent was used at a concentration of 500 µM.
 
 The goal of this workflow is to yield concentration time courses for the product TNB for each of the four enzymes and the two substrates, alongside the assigned reaction conditions in the standardized EnzymeML format. This enables further processing and fitting of the data with kinetic models to yield kinetic parameters for the respective enzymes.
 
@@ -147,34 +147,31 @@ __Optional arguments:__
 
     ```python
     McSAHH = plate.define_protein(
-        id="McSAHH", name="Mycoplasma capricolum SAHH", constant=True
+        id="McSAHH", name="Methanocella conradii SAHH", constant=True
     )
 
-    MtSAHH = plate.define_protein(
-        id="MtSAHH",
-        name="Mycobacterium tuberculosis SAHH",
-        constant=True,
-        uniprot_id="P9WGV3",
+    MeSAHH = plate.define_protein(
+        id="MeSAHH", name="Methanohalobium evestigatum SAHH", constant=True,
     )
 
-    MhSAHH = plate.define_protein(
-        id="MhSAHH", name="Mycoplasma hyorhinis SAHH", constant=True
+    MiSAHH = plate.define_protein(
+        id="MiSAHH", name="Methanocaldocossus infernus SAHH", constant=True
     )
 
-    TmSAHH = plate.define_protein(
-        id="TmSAHH", name="Thermotoga maritima SAHH", constant=True, uniprot_id="O51933"
+    MtSAHH = plate.define_protein(
+        id="MtSAHH", name="Methanothrix thermoacetophila SAHH", constant=True
     )
     ```
 
 ## Assign initial concentrations
 
-After defining the molecules and proteins, the initial concentrations of each molecule and protein in the respective wells need to be assigned. The most convenient way to do this is by using an Excel spreadsheet - especially when dealing with many different species and initial conditions like in this example. To read the in the Excel file, the `assign_init_conditions_from_spreadsheet` method is used. Therefore, only the `path` and the `concentration_unit` arguments are required. This method goes through the sheets of an Excel spreadsheet. Each sheet represents the plate map of a molecule or protein. The sheet name must match the id of the molecule or protein defined for the plate. The initial concentration from the plate map in the Excel spreadsheet is assigned to the respective well. The following requirements must be met for the structure of the Excel spreadsheet:
+After defining the molecules and proteins, an Excel spreadsheet can be generated using the `create_assignment_spreadsheet` method to assign the initial concentrations and pH values for each species in the respective wells. The spreadsheet can then be filled out with the respective initial conditions.
+
+To read the Excel file, the `assign_init_conditions_from_spreadsheet` method is used. Therefore, only the `path` and the `concentration_unit` arguments are required. This method goes through the sheets of the Excel spreadsheet and assigns the initial concentration from the plate map to the respective well.
 
 __Requirements structure of the Excel spreadsheet:__
 
-- The first row must contain the column numbers from 1 to 12.
-- The first column must contain the row letters from A to H.
-- Leaving cells empty is means that the respective species in not present in the well over the course of the experiment.
+- Leaving cells empty means that the respective species in not present in the well over the course of the experiment.
 - Products of the reaction need to be set to a concentration of 0.
 
 !!! example ":material-microsoft-excel: Excel spreadsheet with initial concentrations of SAHH kinetic assay"
@@ -184,7 +181,7 @@ __Requirements structure of the Excel spreadsheet:__
         !!! note "Leaving wells empty"
             Wells in which a molecule or protein is not present throughout the assay are left empty. In this assay the outermost wells were filled with water only and thus not considered in the analysis.
 
-        | µM | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
+        |    | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
         |----|----|----|----|----|----|----|----|----|----|----|----|----|
         | A  |    |    |    |    |    |    |    |    |    |    |    |    |
         | B  |    | 50 | 50 | 50 | 50 | 50 | 50 | 50 | 50 | 50 | 50 |    |
@@ -202,7 +199,7 @@ __Requirements structure of the Excel spreadsheet:__
 
             Here we additionally defined that the `TNB` concentration in our buffer-only wells (`D11`, `E11`, `F11`, `G11`) is 0 µM. Therefore, these wells are automatically detected as suitable wells for creating a calibration model for the detected `TNB` molecule.
 
-        | µM | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
+        |    | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
         |----|----|----|----|----|----|----|----|----|----|----|----|----|
         | A  |    |    |    |    |    |    |    |    |    |    |    |    |
         | B  |    | 250| 250| 250| 0  | 0  | 0  | 0  | 0  | 0  |    |    |
@@ -215,7 +212,7 @@ __Requirements structure of the Excel spreadsheet:__
 
     === "`SAH`"
 
-        | µM | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
+        |    | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
         |----|----|----|----|----|----|----|----|----|----|----|----|----|
         | A  |    |    |    |    |    |    |    |    |    |    |    |    |
         | B  |    |    |    |    |    |    |    |    |    |    | 200|    |
@@ -228,7 +225,7 @@ __Requirements structure of the Excel spreadsheet:__
 
     === "`SIH`"
 
-        | µM | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
+        |    | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
         |----|----|----|----|----|----|----|----|----|----|----|----|----|
         | A  |    |    |    |    |    |    |    |    |    |    |    |    |
         | B  |    |    |    |    |    |    |    |    |    |    |    |    |
@@ -241,7 +238,7 @@ __Requirements structure of the Excel spreadsheet:__
 
     === "`Hyc`"
 
-        | µM | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
+        |    | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
         |----|----|----|----|----|----|----|----|----|----|----|----|----|
         | A  |    |    |    |    |    |    |    |    |    |    |    |    |
         | B  |    |    |    |    |    |    |    |    |    |    |    |    |
@@ -254,20 +251,20 @@ __Requirements structure of the Excel spreadsheet:__
 
     === "`DNTB`"
 
-        | µM | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
+        |    | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
         |----|----|----|----|----|----|----|----|----|----|----|----|----|
         | A  |    |    |    |    |    |    |    |    |    |    |    |    |
         | B  |    |    |    |    |    |    |    |    |    |    |    |    |
-        | C  |    |    | 500| 500| 500| 500| 500| 500|    |    |    |    |
-        | D  |    |    | 500| 500| 500| 500| 500| 500|    |    |    |    |
+        | C  |    |    | 400| 400| 400| 400| 400| 400|    |    |    |    |
+        | D  |    |    | 400| 400| 400| 400| 400| 400|    |    |    |    |
         | E  |    |    |    |    |    |    |    |    |    |    |    |    |
-        | F  |    |    | 500| 500| 500| 500| 500| 500|    |    |    |    |
-        | G  |    |    | 500| 500| 500| 500| 500| 500|    |    |    |    |
+        | F  |    |    | 400| 400| 400| 400| 400| 400|    |    |    |    |
+        | G  |    |    | 400| 400| 400| 400| 400| 400|    |    |    |    |
         | H  |    |    |    |    |    |    |    |    |    |    |    |    |
 
     === "`McSAHH`"
 
-        | µM | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
+        |    | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
         |----|----|----|----|----|----|----|----|----|----|----|----|----|
         | A  |    |    |    |    |    |    |    |    |    |    |    |    |
         | B  |    |    |    |    | 5  | 5  | 5  |    |    |    |    |    |
@@ -278,9 +275,9 @@ __Requirements structure of the Excel spreadsheet:__
         | G  |    |    |    |    |    |    |    |    |    |    |    |    |
         | H  |    |    |    |    |    |    |    |    |    |    |    |    |
 
-    === "`MtSAHH`"
+    === "`MeSAHH`"
 
-        | µM | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
+        |    | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
         |----|----|----|----|----|----|----|----|----|----|----|----|----|
         | A  |    |    |    |    |    |    |    |    |    |    |    |    |
         | B  |    |    |    |    |    |    |    |    |    |    |    |    |
@@ -291,9 +288,9 @@ __Requirements structure of the Excel spreadsheet:__
         | G  |    |    |    |    | 5  | 5  | 5  |    |    |    |    |    |
         | H  |    |    |    |    |    |    |    |    |    |    |    |    |
 
-    === "`MhSAHH`"
+    === "`MiSAHH`"
 
-        | µM | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
+        |    | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
         |----|----|----|----|----|----|----|----|----|----|----|----|----|
         | A  |    |    |    |    |    |    |    |    |    |    |    |    |
         | B  |    |    |    |    |    |    |    | 5  | 5  | 5  |    |    |
@@ -304,9 +301,9 @@ __Requirements structure of the Excel spreadsheet:__
         | G  |    |    |    |    |    |    |    |    |    |    |    |    |
         | H  |    |    |    |    |    |    |    |    |    |    |    |    |
 
-    === "`TmSAHH`"
+    === "`MtSAHH`"
 
-        | µM | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
+        |    | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  | 9  | 10 | 11 | 12 |
         |----|----|----|----|----|----|----|----|----|----|----|----|----|
         | A  |    |    |    |    |    |    |    |    |    |    |    |    |
         | B  |    |    |    |    |    |    |    |    |    |    |    |    |
@@ -317,7 +314,7 @@ __Requirements structure of the Excel spreadsheet:__
         | G  |    |    |    |    |    |    |    | 5  | 5  | 5  |    |    |
         | H  |    |    |    |    |    |    |    |    |    |    |    |    |
 
-After the Excel spreadsheet is created, the initial concentrations can be assigned to the respective wells using the `assign_init_conditions_from_spreadsheet` method.
+After the Excel spreadsheet is filled out, the initial concentrations can be assigned to the respective wells using the `assign_init_conditions_from_spreadsheet` method.
 
 !!! example "Assign initial concentrations from Excel spreadsheet"
     !!! tip "Predefined units"
diff --git a/mtphandler/plate_manager.py b/mtphandler/plate_manager.py
index 72c19f0..a06a255 100644
--- a/mtphandler/plate_manager.py
+++ b/mtphandler/plate_manager.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import os
 from collections import defaultdict
 from typing import Any, Literal, Optional, Tuple, get_args
 
@@ -497,6 +498,65 @@ def _assign_species_to_all_except(
                 f" {init_conc} {conc_unit} to all wells except {well_ids}."
             )
 
+    def create_assignment_spreadsheet(
+        self,
+        path: str = "assignment.xlsx",
+        overwrite: bool = False,
+    ):
+        """Create an Excel spreadsheet for assigning initial concentrations. The spreadsheet
+        contains a separate sheet for each species defined on the plate, with validation
+        to allow only numerical values in the input cells and prevent changes to all other cells.
+
+        Args:
+            path (str, optional): Path to save the assignment spreadsheet. Defaults to "assignment.xlsx".
+            overwrite (bool, optional): If True, the file is overwritten if it already exists. Defaults to False.
+        """
+
+        if not overwrite:
+            if os.path.exists(path):
+                print(f"File {path} already exists. Set 'overwrite=True' to overwrite.")
+                return
+        with pd.ExcelWriter(path, engine="xlsxwriter") as writer:
+            df = pd.DataFrame(
+                index=[chr(65 + i) for i in range(8)],
+                columns=[str(i + 1) for i in range(12)],
+            )
+            df.to_excel(writer, sheet_name="pH", index=True)
+            worksheet = writer.sheets["pH"]
+
+            worksheet.data_validation(
+                "B2:M9",
+                {
+                    "validate": "decimal",
+                    "criteria": "between",
+                    "minimum": 0,
+                    "maximum": 12,
+                    "error_message": "pH must be a numerical value between 0 and 12",
+                },
+            )
+            for species in self.molecules + self.proteins:
+                df = pd.DataFrame(
+                    index=[chr(65 + i) for i in range(8)],
+                    columns=[str(i + 1) for i in range(12)],
+                )
+
+                df.to_excel(writer, sheet_name=species.id, index=True)
+
+                worksheet = writer.sheets[species.id]
+
+                worksheet.data_validation(
+                    "B2:M9",
+                    {
+                        "validate": "decimal",
+                        "criteria": "between",
+                        "minimum": 0,
+                        "maximum": 1e18,
+                        "error_message": "This cell only accepts numerical values",
+                    },
+                )
+
+        print(f"Created assignment spreadsheet at {path}.")
+
     def assign_init_conditions_from_spreadsheet(
         self,
         conc_unit: UnitDefinition,
@@ -561,9 +621,24 @@ def assign_init_conditions_from_spreadsheet(
                     well, species_id, init_conc, contributes_to_signal=None
                 )
 
+        ph_set = False
+        if "pH" in sheet_names:
+            df = pd.read_excel(io=path, header=header, index_col=index, sheet_name="pH")
+            for well in self.plate.wells:
+                ph = df.iloc[well.y_pos, well.x_pos]
+
+                if np.isnan(ph):
+                    continue
+
+                well.ph = ph
+
+                count += 1
+                ph_set = True
+
         if not silent:
+            ph_message = "[bold magenta]pH[/] and" if ph_set else ""
             print(
-                f"📍 Assigned {count} initial concentration coditions for [bold magenta]{list(species_matches)}[/]"
+                f"📍 Assigned {count} initial concentration coditions for {ph_message} [bold magenta]{list(species_matches)}[/]"
                 f" from {path} to the plate."
             )
 
@@ -1116,164 +1191,82 @@ def read_tekan_magellan(
         return cls(**data)
 
     @classmethod
-    def read_multiskan_sky(
+    def read_new_device(
         cls,
         path: str,
+        temperature: float,
         ph: float | None = None,
         name: str | None = None,
+        temperature_unit: UnitDefinition = C,
     ) -> PlateManager:
-        """Read a `*.xlsx` file exported from a Multiskan Sky and create a PlateManager object.
+        """Read a `*.xlsx` file exported from a new device and create a PlateManager object.
 
         Args:
-            path (str): Path to the Multiskan Sky file.
+            path (str): Path to the file.
+            temperature (float): The temperature of the measurements.
             ph (float | None, optional): The pH value of the measurements. Defaults to None.
             name (str | None, optional): Name of the plate. Defaults to None.
+            temperature_unit (UnitDefinition, optional): Unit of temperature. Defaults to C.
 
         Returns:
-            PlateManager: _description_
+            PlateManager: PlateManager object.
         """
-        from mtphandler.readers import read_multiskan_sky as reader
+        from mtphandler.readers import read_new_device as reader
 
-        data: dict[str, Any] = {"plate": reader(path, ph)}
+        data: dict[str, Any] = {
+            "plate": reader(
+                path=path,
+                temperature=[temperature],
+                temperature_unit=temperature_unit,
+                ph=ph,
+            )
+        }
 
         if name is not None:
             data["name"] = name
 
         return cls(**data)
 
+    @classmethod
+    def read_multiskan_sky(
+        cls,
+        path: str,
+        ph: float | None = None,
+        name: str | None = None,
+    ) -> PlateManager:
+        """Read a `*.xlsx` file exported from a Multiskan Sky and create a PlateManager object.
 
-if __name__ == "__main__":
-    # path = (
-    #     "/Users/max/Documents/GitHub/MTPHandler/docs/examples/data/spectra_max_190.txt"
-    # )
-
-    # pm = PlateManager.read_spectra_max_190(path, ph=6.9)
-    # pm.visualize()
-
-    # pm
-
-    # h1 = pm.get_well("H1")
-
-    # print(h1.id)
-    # print(h1.x_pos)
-    # print(h1.y_pos)
-    # print(h1.measurements[0].absorption[0])
-    # print(h1.measurements[0].absorption[-1])
-
-    # path = "/Users/max/Documents/GitHub/MTPHandler/docs/examples/data/tekan_spark.xlsx"
-    # from mtphandler.model import Plate
-
-    # p = PlateManager.read_tecan_spark(path, 7.4)
-
-    # print(p.plate.temperatures)
-
-    # p.visualize()
-
-    # path = "/Users/max/Documents/GitHub/MTPHandler/docs/examples/data/magellan.xlsx"
+        Args:
+            path (str): Path to the Multiskan Sky file.
+            ph (float | None, optional): The pH value of the measurements. Defaults to None.
+            name (str | None, optional): Name of the plate. Defaults to None.
 
-    # plate = PlateManager.read_tekan_magellan(path, wavelength=600, ph=7)
+        Returns:
+            PlateManager: The PlateManager object.
+        """
+        from mtphandler.readers import read_multiskan_sky as reader
 
-    # plate.visualize()
+        data: dict[str, Any] = {"plate": reader(path, ph)}
 
-    # path = (
-    #     "/Users/max/Documents/training_course/jules/Spectramax190 molecular Devices.txt"
-    # )
+        if name is not None:
+            data["name"] = name
 
-    # from mtphandler.units import mM
+        return cls(**data)
 
-    # pm = PlateManager.read_spectra_max_190(path, ph=7)
-    # # pm.visualize()
-    # testo = pm.define_molecule("testo", 60857, "testosterone")
-    # mpi = pm.define_molecule("mpi", 60961, "methylparaben")
 
-    # pm.assign_species(
-    #     species=testo,
-    #     init_conc=0.1,
-    #     conc_unit=mM,
-    #     to="all",
-    # )
+if __name__ == "__main__":
+    from devtools import pprint
 
-    # path = (
-    #     "/Users/max/Documents/GitHub/MTPHandler/docs/examples/data/ BioTek_Epoch2.xlsx"
-    # )
+    from mtphandler.units import mM
 
-    # from mtphandler.units import mM
+    path = "docs/examples/data/ BioTek_Epoch2.xlsx"
 
-    # p = PlateManager.read_biotek(path, ph=7.4)
+    p = PlateManager.read_biotek(path)
+    p.define_molecule("alc", 123)
+    p.define_molecule("ala", 124)
+    p.define_protein("asd", "adfaddf")
     # p.visualize()
 
-    # testo = p.define_molecule("testo", 60857, "testosterone")
-    # aldolase = p.define_protein("aldolase", "Aldolase")
-
-    # p.assign_init_conditions(
-    #     species=testo,
-    #     init_conc=0.1,
-    #     conc_unit=mM,
-    #     to="all",
-    # )
-
-    # aldo = p.define_protein("aldolase", "Aldolase")
-    # p.assign_init_conditions(
-    #     species=aldo, init_conc=0.1, conc_unit=mM, to="all", contributes_to_signal=False
-    # )
-
-    # enz = p.to_enzymeml(name="Test EnzymeML", wells_with_protein_only=False)
-
-    # with open("enz.json", "w") as f:
-    #     f.write(enz.model_dump_json(indent=4))
-    # print(len(enz.measurements))
-
-    # from mtphandler.units import C, min
-
-    # path = "docs/examples/data/multiskan_spectrum_1500.txt"
-
-    # ph = 7.0
-    # wavelength = 450.0
-
-    # time = np.arange(0, 15.5, 0.5).tolist()
-    # print(f"the thime is {time}")
-
-    # plate = PlateManager.read_multiskan_spectrum_1500(
-    #     path=path,
-    #     ph=ph,
-    #     time=time,
-    #     time_unit=min,
-    #     temperature=37.0,
-    #     temperature_unit=C,
-    # )
-    # plate.visualize()
-
-    path = (
-        "/Users/max/Documents/GitHub/MTPHandler/docs/examples/data/Multiskan Sky.xlsx"
-    )
-
-    p = PlateManager.read_multiskan_sky(path, ph=None)
-    p.visualize(darkmode=True)
-    print(p)
-
-
-class BlankResult(BaseModel):
-    species_id: str = Field(
-        description="The id of the species for which the blank was calculated.",
-        default=None,
-    )
-    wavelength: float = Field(
-        description="The wavelength at which the blank was calculated.",
-        default=None,
-    )
-    control_well_ids: list[str] = Field(
-        description="The ids of the wells used to calculate the blank.",
-        default=[],
-    )
-    mean_contribution: float = Field(
-        description="The mean contribution of the species to the signal.",
-        default=None,
-    )
-    std_contribution: float = Field(
-        description="The standard deviation of the contribution of the species to the signal.",
-        default=None,
-    )
-    applied_to_well_ids: list[str] = Field(
-        description="The ids of the wells to which the blank was applied.",
-        default=[],
-    )
+    p.create_assignment_spreadsheet()
+    p.assign_init_conditions_from_spreadsheet(conc_unit=mM, path="assignment.xlsx")
+    pprint(p.get_well("B2").ph)
diff --git a/mtphandler/readers/__init__.py b/mtphandler/readers/__init__.py
index 2aecda4..0c5b8eb 100644
--- a/mtphandler/readers/__init__.py
+++ b/mtphandler/readers/__init__.py
@@ -5,3 +5,4 @@
 from .tekan_magellan import read_tekan_magellan  # noqa
 from .tekan_spark import read_tekan_spark  # noqa
 from .multiskan_sky import read_multiskan_sky  # noqa
+from .new_device import read_new_device  # noqa
diff --git a/mtphandler/readers/new_device.py b/mtphandler/readers/new_device.py
new file mode 100644
index 0000000..2836a40
--- /dev/null
+++ b/mtphandler/readers/new_device.py
@@ -0,0 +1,163 @@
+from __future__ import annotations
+
+import re
+
+import pandas as pd
+
+from mtphandler.model import Plate, UnitDefinition
+from mtphandler.readers.utils import id_to_xy
+from mtphandler.units import second
+
+
+def read_new_device(
+    path: str,
+    ph: float | None,
+    temperature: list[float],
+    temperature_unit: UnitDefinition,
+) -> Plate:
+    pattern = r"\(([A-H][0-9]{2})\)"
+
+    if isinstance(temperature, (int | float)):
+        temperature = [temperature]
+
+    df = pd.read_excel(path)
+
+    wavelength = float(df.iloc[4, 0].split(" ")[1])
+    time_str = df.iloc[1, 0]
+    timestamp = str(pd.to_datetime(time_str, format="%m/%d/%Y %I:%M:%S %p"))
+    name = df.iloc[0, 0]
+
+    # Check for "Reading", "Abs", or "Sample" in the DataFrame
+    if "Reading" in df.values:
+        plate = handle_timecourse_read(
+            df, path, name, timestamp, temperature_unit, wavelength, ph, pattern
+        )
+    elif any(keyword in df.values for keyword in ["Abs", "Sample"]):
+        plate = handle_endpoint_read(
+            df, path, name, timestamp, temperature_unit, wavelength, ph, pattern
+        )
+    else:
+        raise ValueError(
+            "Could not find 'Reading', 'Abs', or 'Sample' in the DataFrame."
+        )
+
+    return plate
+
+
+def handle_timecourse_read(
+    df: pd.DataFrame,
+    path: str,
+    name: str,
+    timestamp: str,
+    temperature_unit: UnitDefinition,
+    wavelength: float,
+    ph: float | None,
+    pattern: str,
+) -> Plate:
+    """Handles the case where 'Reading' is found in the DataFrame."""
+
+    row_number = df[df.iloc[:, 0] == "Reading"].index[0]
+    df = pd.read_excel(path, skiprows=row_number + 1).dropna(axis=0, how="any")
+    time = df.pop("avg. time [s]").values.tolist()
+
+    plate = Plate(
+        name=name,
+        date_measured=timestamp,
+        temperature_unit=temperature_unit,
+    )
+
+    # Iterate over the columns
+    for column in df.columns:
+        match = re.search(pattern, column)
+        if match:
+            well_id = match.group(1)
+            name_id = column.split(" ")[0]
+            x, y = id_to_xy(well_id)
+            well = plate.add_to_wells(
+                id=name_id,
+                x_pos=x,
+                y_pos=y,
+                ph=ph,
+            )
+
+            well.add_to_measurements(
+                wavelength=wavelength,
+                absorption=df[column].values.tolist(),
+                time=time,
+                time_unit=second,
+            )
+
+    return plate
+
+
+def handle_endpoint_read(
+    df: pd.DataFrame,
+    path: str,
+    name: str,
+    timestamp: str,
+    temperature_unit: UnitDefinition,
+    wavelength: float,
+    ph: float | None,
+    pattern: str,
+) -> Plate:
+    """Handles the case where 'Abs' or 'Sample' is found in the DataFrame."""
+
+    absorption_row = df[df.iloc[:, 0] == "Abs"].index[0]
+    sample_row = df[df.iloc[:, 0] == "Sample"].index[0]
+
+    absorption_df = pd.read_excel(
+        path,
+        skiprows=absorption_row + 1,
+        nrows=8,
+        usecols=range(13),
+        index_col=0,
+    )
+
+    sample_df = pd.read_excel(
+        path,
+        skiprows=sample_row + 1,
+        usecols=range(13),
+        nrows=8,
+        index_col=0,
+    )
+
+    plate = Plate(
+        name=name,
+        date_measured=timestamp,
+        temperature_unit=temperature_unit,
+    )
+
+    for row_id in range(8):
+        for column_id in range(12):
+            if pd.isnull(sample_df.iloc[row_id, column_id]) or pd.isnull(
+                absorption_df.iloc[row_id, column_id]
+            ):
+                continue
+            print(sample_df.iloc[row_id, column_id])
+            well = plate.add_to_wells(
+                id=sample_df.iloc[row_id, column_id],
+                x_pos=column_id,
+                y_pos=row_id,
+                ph=ph,
+            )
+
+            well.add_to_measurements(
+                wavelength=wavelength,
+                absorption=[absorption_df.iloc[row_id, column_id]],
+                time=[0],
+                time_unit=second,
+            )
+
+    return plate
+
+
+if __name__ == "__main__":
+    path = "docs/examples/data/new_device_calib.xlsx"
+    from mtphandler.units import C
+
+    plate = read_new_device(
+        path=path,
+        ph=7.4,
+        temperature=[37.0],
+        temperature_unit=C,
+    )
diff --git a/mtphandler/readers/utils.py b/mtphandler/readers/utils.py
index 889e7ac..9427dff 100644
--- a/mtphandler/readers/utils.py
+++ b/mtphandler/readers/utils.py
@@ -10,8 +10,10 @@ def xy_to_id(x: int, y: int) -> str:
 
 
 def id_to_xy(well_id: str) -> tuple[int, int]:
-    """Well ID to well coordinates"""
-    return int(well_id[1:]) - 1, ord(well_id[0].upper()) - 65
+    """Convert well ID (e.g., A01 or A1) to well coordinates"""
+    row = ord(well_id[0].upper()) - 65
+    column = int(well_id[1:]) - 1
+    return column, row
 
     def get_well(self, id: str) -> Well:
         for well in self.plate.wells:
@@ -26,3 +28,6 @@ def get_well(self, id: str) -> Well:
     print(id_to_xy("A1"))
     print(id_to_xy("H12"))
     print(id_to_xy("C3"))
+    print(id_to_xy("A02"))
+    print(id_to_xy("A01"))
+    print(id_to_xy("A11"))
diff --git a/plate_visualization.json b/plate_visualization.json
new file mode 100644
index 0000000..f965f73
--- /dev/null
+++ b/plate_visualization.json
@@ -0,0 +1 @@
+{"data":[{"hovertemplate":"%{y:.2f}\u003cbr\u003e","line":{"color":"#636EFA"},"mode":"lines","name":"630.0 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diff --git a/pyproject.toml b/pyproject.toml
index 7805e01..eadcef5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "mtphandler"
-version = "0.2.11"
+version = "0.2.12"
 description = "Python package for processing, enriching, and converting microtiter plate data into standardized EnzymeML time-course data, ready for data science."
 authors = ["haeussma <83341109+haeussma@users.noreply.github.com>"]
 license = "MIT License"
diff --git a/tests/unit_tests/test_readers/test_multiscan.py b/tests/unit_tests/test_readers/test_multiscan.py
index 50405ea..0652d74 100644
--- a/tests/unit_tests/test_readers/test_multiscan.py
+++ b/tests/unit_tests/test_readers/test_multiscan.py
@@ -12,7 +12,6 @@
 def test_read_multiskan_spectrum_1500():
     # Arrange
     path = "docs/examples/data/multiskan_spectrum_1500.txt"
-    print(time)
 
     # Act
     plate = read_multiskan_spectrum_1500(
diff --git a/tests/unit_tests/test_readers/test_new_device.py b/tests/unit_tests/test_readers/test_new_device.py
new file mode 100644
index 0000000..e52e8a5
--- /dev/null
+++ b/tests/unit_tests/test_readers/test_new_device.py
@@ -0,0 +1,42 @@
+from mtphandler.readers import read_new_device
+from mtphandler.units import C
+
+
+def test_new_reader_kinetic():
+    path = "docs/examples/data/new_device_kinetic.xlsx"
+
+    plate = read_new_device(
+        path=path,
+        ph=7.4,
+        temperature=[37.0],
+        temperature_unit=C,
+    )
+
+    assert len(plate.wells) == 72
+    assert plate.wells[0].x_pos == 0
+    assert plate.wells[0].y_pos == 0
+    assert plate.wells[0].id == "ArODH0001"
+    assert plate.wells[-1].x_pos == 11
+    assert plate.wells[-1].y_pos == 5
+    assert plate.wells[-1].id == "mODH-582"
+    assert plate.wells[-1].measurements[0].absorption[0] == 0.5626
+    assert plate.wells[-1].measurements[0].absorption[-1] == 0.6376
+
+
+def test_new_reader_endpoint():
+    path = "docs/examples/data/new_device_calib.xlsx"
+
+    plate = read_new_device(
+        path=path,
+        ph=7.4,
+        temperature=[37.0],
+        temperature_unit=C,
+    )
+
+    assert len(plate.wells) == 42
+    assert plate.wells[0].x_pos == 0
+    assert plate.wells[0].y_pos == 0
+    assert plate.wells[0].id == "NADH0001"
+    assert plate.wells[0].measurements[0].absorption[0] == 3.116
+    assert plate.name == "NADH_NADPH_calib.skax"
+    assert plate.date_measured == "2023-08-08 04:06:03"