# app.py import streamlit as st import numpy as np import matplotlib.pyplot as plt import random st.set_page_config(page_title="Cell–Cell Communication Builder", layout="wide") # ----------------------------- # Base option sets (your terms) # ----------------------------- SECRETING_CELLS_BASE = [ "— select —", "Presynaptic neuron", "Hypothalamus/Pituitary/Adrenal cortex", "Cardiomyocyte", "Tumor cell", ] MOLECULES_BASE = [ "— select —", "EGF", "Neurotransmitter", "Intracellular ions", "Cortisol", ] RECEIVING_CELLS_BASE = [ "— select —", "Neighboring cardiomyocytes", "Same cell", "Postsynaptic cell", "Liver & skeletal muscle", ] SIGNAL_TYPES_BASE = [ "— select —", "direct", "autocrine", "paracrine", "endocrine", ] MOLECULE_CLASS_BASE = [ "— select —", "hydrophilic", "lipophilic", "N/A (not applicable)", ] RECEPTOR_LOCS_BASE = [ "— select —", "membrane-bound", "intracellular", "N/A (not applicable)", ] # ----------------------------- # Scenarios (titles simplified; type NOT shown in title) # ----------------------------- SCENARIOS = { "Cardiomyocyte signaling": { "secreting": "Cardiomyocyte", "molecule": "Intracellular ions", "receiving": "Neighboring cardiomyocytes", "type": "direct", "mol_class": "N/A (not applicable)", "receptor": "N/A (not applicable)", "explain": { "type": "Gap junctions are contact dependent → direct signaling.", "mol_class": "Electrical/ionic coupling across connexons; not a classic ligand.", "receptor": "No classic receptor: current spreads via channels/pores.", }, }, "Cancer proliferation": { "secreting": "Tumor cell", "molecule": "EGF", "receiving": "Same cell", "type": "autocrine", "mol_class": "hydrophilic", "receptor": "membrane-bound", "explain": { "type": "Cell releases a signal that acts on itself → autocrine.", "mol_class": "EGF is a protein → hydrophilic → can’t cross the bilayer.", "receptor": "EGF binds EGFR (RTK) at the membrane.", }, }, "Synapse signaling": { "secreting": "Presynaptic neuron", "molecule": "Neurotransmitter", "receiving": "Postsynaptic cell", "type": "paracrine", "mol_class": "hydrophilic", "receptor": "membrane-bound", "explain": { "type": "Very short-distance diffusion across synaptic cleft → paracrine.", "mol_class": "Classical neurotransmitters act extracellularly.", "receptor": "Postsynaptic receptors are membrane proteins (ionotropic/GPCR).", }, }, "HPA axis": { "secreting": "Hypothalamus/Pituitary/Adrenal cortex", "molecule": "Cortisol", "receiving": "Liver & skeletal muscle", "type": "endocrine", "mol_class": "lipophilic", "receptor": "intracellular", "explain": { "type": "Hormone travels via blood to distant targets → endocrine.", "mol_class": "Cortisol is steroidal → lipophilic → crosses the membrane.", "receptor": "Steroids bind cytosolic/nuclear receptors → gene transcription.", }, }, } # ----------------------------- # Session state helpers # ----------------------------- def shuffled(opts): head, rest = opts[0], opts[1:] random.shuffle(rest) return [head] + rest def ensure_state(): if "secret_opts" not in st.session_state: st.session_state.secret_opts = shuffled(SECRETING_CELLS_BASE[:]) st.session_state.molecule_opts = shuffled(MOLECULES_BASE[:]) st.session_state.recv_opts = shuffled(RECEIVING_CELLS_BASE[:]) st.session_state.type_opts = shuffled(SIGNAL_TYPES_BASE[:]) st.session_state.class_opts = shuffled(MOLECULE_CLASS_BASE[:]) st.session_state.recept_opts = shuffled(RECEPTOR_LOCS_BASE[:]) if "selections" not in st.session_state: st.session_state.selections = { "secreting": "— select —", "molecule": "— select —", "receiving": "— select —", "type": "— select —", "mol_class": "— select —", "receptor": "— select —", } def reshuffle_all(): st.session_state.secret_opts = shuffled(SECRETING_CELLS_BASE[:]) st.session_state.molecule_opts = shuffled(MOLECULES_BASE[:]) st.session_state.recv_opts = shuffled(RECEIVING_CELLS_BASE[:]) st.session_state.type_opts = shuffled(SIGNAL_TYPES_BASE[:]) st.session_state.class_opts = shuffled(MOLECULE_CLASS_BASE[:]) st.session_state.recept_opts = shuffled(RECEPTOR_LOCS_BASE[:]) clear_selections() def clear_selections(): for k in st.session_state.selections.keys(): st.session_state.selections[k] = "— select —" def evaluate(sel, key, scenario_key): if sel == "— select —": return None, "Incomplete — choose an option." correct = SCENARIOS[scenario_key][key] if sel == correct: return True, "✔" explain_map = SCENARIOS[scenario_key]["explain"] why = explain_map.get("type" if key not in ("mol_class", "receptor") else key, "") return False, f"Expected: {correct}. {why}" def draw_diagram(selections, results, scenario_label): fig, ax = plt.subplots(figsize=(9, 4.6)) ax.set_xlim(0, 13) ax.set_ylim(0, 6.3) ax.axis("off") def color_box(x, y, text, ok, blank=False): w, h = 2.8, 1.0 face = "#FFF8E1" if blank else ("#E8F5E9" if ok else "#FDECEA") edge = "#FFB300" if blank else ("#2E7D32" if ok else "#C62828") ax.add_patch(plt.Rectangle((x, y), w, h, fc=face, ec=edge, lw=2)) ax.text(x + w/2, y + h/2, text, ha="center", va="center", fontsize=11) return (x, y, w, h) def arrow(start_rect, end_rect, label=""): x, y, w, h = start_rect x2, y2, w2, h2 = end_rect ax.annotate("", xy=(x2, y2 + h/2), xytext=(x + w, y + h/2), arrowprops=dict(arrowstyle="->", lw=2)) if label: ax.text((x + w + x2)/2, y + h/2 + 0.15, label, ha="center", fontsize=10) # top row: secreting → molecule → receiving s_ok = results["secreting"][0] if results["secreting"][0] is not None else True m_ok = results["molecule"][0] if results["molecule"][0] is not None else True r_ok = results["receiving"][0] if results["receiving"][0] is not None else True s_rect = color_box(0.6, 3.9, f"Secreting Cell/Tissue\n{selections['secreting']}", s_ok, selections['secreting']=="— select —") m_rect = color_box(4.6, 3.9, f"Molecule\n{selections['molecule']}", m_ok, selections['molecule']=="— select —") r_rect = color_box(8.6, 3.9, f"Receiving Cell/Tissue\n{selections['receiving']}", r_ok, selections['receiving']=="— select —") # bottom row: type → class → receptor t_ok = results["type"][0] if results["type"][0] is not None else True c_ok = results["mol_class"][0] if results["mol_class"][0] is not None else True rc_ok = results["receptor"][0] if results["receptor"][0] is not None else True t_rect = color_box(2.6, 1.6, f"Signaling Type\n{selections['type']}", t_ok, selections['type']=="— select —") c_rect = color_box(6.6, 1.6, f"Molecule Class\n{selections['mol_class']}", c_ok, selections['mol_class']=="— select —") rc_rect = color_box(10.6,1.6, f"Receptor Location\n{selections['receptor']}", rc_ok, selections['receptor']=="— select —") arrow(s_rect, m_rect, "signal") arrow(m_rect, r_rect, "response") arrow(t_rect, c_rect) arrow(c_rect, rc_rect) ax.text(6.5, 5.9, scenario_label, ha="center", fontsize=12, fontweight="bold") st.pyplot(fig) # ----------------------------- # App UI # ----------------------------- ensure_state() st.title("Cell–Cell Communication Builder") left, right = st.columns([1.1, 0.9]) with left: scenario_label = st.selectbox( "Scenario", options=list(SCENARIOS.keys()), index=0, key="scenario_select" ) with right: shuffle_clicked = st.button("🔀 Shuffle options", use_container_width=True) if shuffle_clicked: reshuffle_all() st.markdown("Build a consistent map of **Secreting cell/tissue → Molecule → Receiving cell/tissue** and choose **Signaling type**, **Molecule class**, and **Receptor location**. Then click **Test**.") col1, col2 = st.columns(2) with col1: st.subheader("Actors") st.session_state.selections["secreting"] = st.selectbox( "Secreting Cell or Tissue", options=st.session_state.secret_opts, index=st.session_state.secret_opts.index(st.session_state.selections["secreting"]) if st.session_state.selections["secreting"] in st.session_state.secret_opts else 0, key="sec_dd", ) st.session_state.selections["molecule"] = st.selectbox( "Molecule", options=st.session_state.molecule_opts, index=st.session_state.molecule_opts.index(st.session_state.selections["molecule"]) if st.session_state.selections["molecule"] in st.session_state.molecule_opts else 0, key="mol_dd", ) st.session_state.selections["receiving"] = st.selectbox( "Receiving Cell or Tissue", options=st.session_state.recv_opts, index=st.session_state.recv_opts.index(st.session_state.selections["receiving"]) if st.session_state.selections["receiving"] in st.session_state.recv_opts else 0, key="recv_dd", ) with col2: st.subheader("Mechanism") st.session_state.selections["type"] = st.selectbox( "Signaling Type", options=st.session_state.type_opts, index=st.session_state.type_opts.index(st.session_state.selections["type"]) if st.session_state.selections["type"] in st.session_state.type_opts else 0, key="type_dd", ) st.session_state.selections["mol_class"] = st.selectbox( "Molecule Class", options=st.session_state.class_opts, index=st.session_state.class_opts.index(st.session_state.selections["mol_class"]) if st.session_state.selections["mol_class"] in st.session_state.class_opts else 0, key="class_dd", ) st.session_state.selections["receptor"] = st.selectbox( "Receptor Location", options=st.session_state.recept_opts, index=st.session_state.recept_opts.index(st.session_state.selections["receptor"]) if st.session_state.selections["receptor"] in st.session_state.recept_opts else 0, key="recept_dd", ) action_col1, action_col2 = st.columns([1,1]) with action_col1: tested = st.button("✅ Test", type="primary", use_container_width=True) with action_col2: cleared = st.button("🧹 Clear / Reshuffle", use_container_width=True) if cleared: reshuffle_all() results = {} if tested: keys = ["secreting", "molecule", "receiving", "type", "mol_class", "receptor"] for k in keys: results[k] = evaluate(st.session_state.selections[k], k, st.session_state.scenario_select) incomplete = any(v[0] is None for v in results.values()) n_ok = sum(1 for v in results.values() if v[0] is True) total = len(results) if incomplete: st.warning("◻️ **INCOMPLETE** — make all selections to test consistency.") elif n_ok == total: st.success(f"✅ **CONSISTENT** ({n_ok}/{total}) — Nice! Logical mapping.") else: st.error(f"⚠️ **INCONSISTENT** ({n_ok}/{total}). Try again—no hints provided.") draw_diagram(st.session_state.selections, results, st.session_state.scenario_select) else: tmp = {k:(None,"") for k in ["secreting","molecule","receiving","type","mol_class","receptor"]} draw_diagram(st.session_state.selections, tmp, st.session_state.scenario_select) st.caption("Options are shuffled on load and when you clear/reshuffle, to emphasize reasoning over pattern matching.")