add tetramerization

tetramer.py

@@ -0,0 +1,163 @@
+# %%
+
+from pathlib import Path
+
+import altair as alt
+import numpy as np
+import pandas as pd
+import solara
+import solara.lab
+import sympy as sp
+from scipy.optimize import root_scalar
+
+# %%
+P1, P2, P4, PT, kD1, kD2 = sp.symbols("P_1 P_2 P_4 P_T k_D1 k_D2", positive=True)
+
+# %%
+sub_p1_p2 = (P1, sp.solve(kD1 * (P2 / P1**2) - 1, P1)[0])
+sub_p2_p4 = (P2, sp.solve(kD2 * (P4 / P2**2) - 1, P2)[0])
+sub_p4_p2 = (P4, sp.solve(kD2 * (P4 / P2**2) - 1, P4)[0])
+
+# %%
+mass_balance = P1 + 2 * P2 + 4 * P4 - PT
+
+eq_p4 = mass_balance.subs([sub_p1_p2, sub_p2_p4])
+eq_p2 = mass_balance.subs([sub_p1_p2, sub_p4_p2])
+
+# %%
+
+
+def make_df(vmin: float, vmax: float, kD_1_v: float, kD2_v: float) -> pd.DataFrame:
+    PT_values = np.logspace(np.log10(vmin), np.log10(vmax), endpoint=True, num=100)
+
+    kd_subs = [(kD1, kD_1_v), (kD2, kD2_v)]
+
+    ld = sp.lambdify([P4, PT], eq_p4.subs(kd_subs))
+    P4_values = np.array(
+        [root_scalar(ld, bracket=(0, PT_v), args=(PT_v,)).root for PT_v in PT_values]
+    )
+
+    ld = sp.lambdify([P2, PT], eq_p2.subs(kd_subs))
+    P2_values = np.array(
+        [root_scalar(ld, bracket=(0, PT_v), args=(PT_v,)).root for PT_v in PT_values]
+    )
+
+    P1_values = PT_values - 2 * P2_values - 4 * P4_values
+
+    columns = {"P1": P1_values, "P2": P2_values, "P4": P4_values}
+    total = np.sum(list(columns.values()), axis=0)
+
+    df = pd.DataFrame(dict(PT=PT_values) | {k: v / total for k, v in columns.items()})
+
+    return df
+
+
+def make_chart(df: pd.DataFrame) -> alt.LayerChart:
+    source = df.melt("PT", var_name="species", value_name="y")
+
+    # Create a selection that chooses the nearest point & selects based on x-value
+    nearest = alt.selection_point(
+        nearest=True, on="pointerover", fields=["PT"], empty=False
+    )
+
+    # The basic line
+    line = (
+        alt.Chart(source)
+        .mark_line(interpolate="basis")
+        .encode(
+            x=alt.X(
+                "PT:Q",
+                scale=alt.Scale(type="log"),
+                title="Total protomer concentration",
+            ),
+            y=alt.Y("y:Q", title="Fraction of total"),
+            color="species:N",
+        )
+        .properties(width="container")
+    )
+
+    # Draw points on the line, and highlight based on selection
+    points = (
+        line.mark_point()
+        .encode(opacity=alt.condition(nearest, alt.value(1), alt.value(0)))
+        .properties(width="container")
+    )
+
+    # Draw a rule at the location of the selection
+    rules = (
+        alt.Chart(source)
+        .transform_pivot("species", value="y", groupby=["PT"])
+        .mark_rule(color="black")
+        .encode(
+            x="PT:Q",
+            opacity=alt.condition(nearest, alt.value(0.3), alt.value(0)),
+            tooltip=[
+                alt.Tooltip(c, type="quantitative", format=".2f") for c in df.columns
+            ],
+        )
+        .add_params(nearest)
+        .properties(width="container")
+    )
+
+    # Put the five layers into a chart and bind the data
+    chart = (
+        alt.layer(line, points, rules)
+        .properties(height=300)
+        .configure(autosize="fit-x")
+    )
+
+    return chart
+
+
+md = """
+This app calculates monomer and dimer concentrations given a total amount of protomer PT and the 
+dissociation constant KD. More info on how and why can be found [HuggingFace](https://huggingface.co/spaces/Jhsmit/binding-kinetics) (right click, open new tab).
+"""
+
+
+@solara.component
+def Page():
+    solara.Style(Path("style.css"))
+
+    dark_effective = solara.lab.use_dark_effective()
+    if dark_effective is True:
+        alt.themes.enable("dark")
+
+    elif dark_effective is False:
+        alt.themes.enable("default")
+
+    kD1 = solara.use_reactive(1.0)
+    kD2 = solara.use_reactive(100)
+
+    vmin = solara.use_reactive(1e-3)
+    vmax = solara.use_reactive(1e3)
+
+    async def update():
+        df = make_df(vmin.value, vmax.value, kD1.value, kD2.value)
+        chart = make_chart(df)
+
+        return chart
+
+    task: solara.lab.Task = solara.lab.use_task(
+        update, dependencies=[kD1.value, kD2.value, vmin.value, vmax.value]
+    )
+
+    solara.Title("Tetramerization Kinetics")
+
+    with solara.Card("Fraction monomer/dimer/tetramer"):
+        with solara.GridFixed(columns=2):
+            with solara.Tooltip("Dissociation constant monomer/dimer"):
+                solara.InputFloat("kD1", value=kD1)
+            with solara.Tooltip("Dissociation constant dimer/tetramer"):
+                solara.InputFloat("kD2", value=kD2)
+            with solara.Tooltip("X axis lower limit"):
+                solara.InputFloat("xmin", value=vmin)
+            with solara.Tooltip("X axis upper limit"):
+                solara.InputFloat("xmax", value=vmax)
+        solara.HTML(tag="div", style="height: 10px")
+
+        if task.finished:
+            solara.FigureAltair(task.value)
+
+
+# %%