Equations and assumptions

• Temperature factor (cells): Two models, user-selectable. Cardinal (default, Rosso, Lobry & Flandrois 1993, CTMI): f_T = (T−T_max)(T−T_min)² / [(T_opt−T_min) · ((T_opt−T_min)(T−T_opt) − (T_opt−T_max)(T_opt+T_min−2T))] for T_min < T < T_max, and exactly 0 outside that interval. Asymmetric (long shoulder below T_opt, sharp drop above), with biologically interpretable cardinal temperatures (defaults T_min=5°C, T_opt=32°C, T_max=42°C for industrial S. cerevisiae). Gaussian (legacy): f_T = exp(−((T−T_opt)²/(2σ_T²))) — symmetric, never reaches zero. Useful for back-compatibility with older parameter sets.
• pH factor (cells): Two models, user-selectable. Cardinal pH Model (default, Rosso et al. 1995): f_pH = (pH−pH_max)(pH−pH_min)² / [(pH_opt−pH_min) · ((pH_opt−pH_min)(pH−pH_opt) − (pH_opt−pH_max)(pH_opt+pH_min−2pH))] — asymmetric (long acidic shoulder, sharp alkaline cliff), reflecting S. cerevisiae's real pH response. Gaussian (legacy): f_pH = exp(−((pH−pH_opt)²/(2σ_pH²))).
• Substrate kinetics (cells): Two models, user-selectable. Haldane (default, unified): mu_S = S / (K_s + S + S²/K_i,S). Combines Monod uptake and substrate-inhibition into one biologically-grounded expression. Monod × inhibition (legacy): Monod S/(K_s+S) multiplied by 1/(1 + S/K_i,S).
• Ethanol inhibition (cells): Three models, user-selectable. Luong (default, 1985): f_E = 1 below E_inh, ((E_max−E)/(E_max−E_inh))ⁿ in the inhibition range, 0 above E_max. Best empirical fit for S. cerevisiae. Hill (legacy): f_E = max(0, (1 − (max(E−E_inh,0))/E_max)ⁿ). Aiba (1968): f_E = exp(−k_E · E). Smooth exponential; never reaches zero.
• Ethanol inhibition (production, decoupled): Same three model choices, separate parameters (E_max,p, E_inh,p, n_p, k_E,p). Only the Luedeking-Piret β term is gated by f_E,p — this is the *specifically* ethanol-inhibited non-growth production contribution. Maintenance catabolism (m_S, regime-adjusted ~25× higher for anaerobic than aerobic) continues regardless of E, because live cells always need ATP for maintenance. What actually terminates fermentation is cell death (k_d,eff rising with E), which drives X → 0 and thus r_P = q_P·X → 0 — not a hard metabolic cliff.
• Smooth substrate depletion: A soft-switch factor f_avail = S/(S + K_s,min) (K_s,min = 0.01 g/L) replaces the hard S=0 cutoff, ensuring rates taper smoothly as substrate runs out rather than dropping discontinuously.
• Substrate consumption: q_S = μ/Y_X/S + m_S·f_avail + q_Gly/Y_S/Gly; maintenance scales with substrate availability.
• Cell death (v19.90): k_d,eff = k_d·(1 + k_d,E·E + k_d,T·[(T−T_opt)²_heat + f_cold·(T−T_opt)²_cold] + k_d,pH·(pH−pH_opt)²). dX/dt = (μ − k_d,eff) · X. Heat-stress (T > T_opt) drives most thermal death; cold-stress contributes a tunable fraction f_cold (default 0.3) of the same quadratic to reflect membrane/osmotic damage at low T. The pH term was added to capture real yeast mortality at extremes of acidity or alkalinity (default k_d,pH=0.05 per pH-unit²).
• Enzyme hydrolysis (starch→glucose): v_hyd = k_hyd·(E_eff·1000)·f_act·f_T,E·f_pH,E·(Starch/(K_m,starch+Starch)); where E_eff = GA + 0.25·AA (U/mL); f_act is the remaining active fraction (decays via first-order inactivation).
• Enzyme thermal inactivation: df_act/dt = −k_inact · Q10^{(T−T_ref)/10} · f_act. Activity decays faster at higher temperatures (Q10 = 2).
• Instant rates (cells): q_S = μ/Y_X/S + m_S·f_avail + q_Gly/Y_S/Gly, q_P = Y_P/S·q_S + α·μ + β; r_P=q_P·X; q_Gly = α_Gly·μ + q_G0·stress(E,S,pH).
• ODEs: dStarch/dt = −v_hyd; dS/dt = r_hyd − q_S·X; dX/dt=(μ − k_d,eff)·X; dEtOH/dt=q_P·X; dGly/dt=q_Gly·X; dCO₂/dt = q_CO₂·X; df_act/dt = −k_inact,eff·f_act; dLA/dt = Y_LA/S·q_S·X; dAA/dt = Y_AA/S·q_S·X. 15 state variables, integrated with RK4.
• Mass balance check: Carbon in (glucose consumed + starch consumed) vs carbon out (ethanol + CO₂ + glycerol + biomass). Warns if balance deviates >5%.
• Feedstock mass balance (Medium Calculator): Total fermentable sugar required is computed as sugar = ethG / Y_E/S, then split between two paths. Dry feedstock needed = sugar / Y_{sugar/feedstock}, where Y depends on the selected raw material: 1.00 g/g for pure glucose/sucrose, 1.10 for purified starch (stoichiometric hydrolysis), 0.80 for whole-kernel corn (~70% starch × 1.11 + ~3% free sugar), 0.83 for degermed corn flour, 0.81 for whole-grain corn meal, 0.78 for wheat flour, 0.70 for barley malt, 0.88 for dry cassava, 0.50 for cane molasses, 0.48 for beet molasses. As-received (wet) mass = dry mass ÷ (1 − moisture_fraction), so at 14% moisture, 20 kg dry corn weighs 23.3 kg as received. The feedstock dropdown pre-fills typical moisture values (grains 12–14%, molasses ~20%, purified starch ~11%, pure sugars 0%) and a Starch Fraction expressed as % of sugar-eq from starch hydrolysis, not mass%. Defaults: 100 for purified starch, 97 for whole corn, 98 for corn flour, 96 for corn meal, 97 for wheat, 87 for barley malt (malting pre-converts some starch to free sugars), 99 for cassava, 0 for pure sugars and molasses. The Starch Fraction drives the initial-condition split: at t=0 the broth contains (1 − starchFrac) × sugar-eq as free glucose and (starchFrac × sugar-eq) ÷ 1.11 as solid starch; amylase enzymes (α-amylase 500 U/g starch, glucoamylase 200 U/g starch) hydrolyze the starch to glucose during fermentation.
• Instant-Predictions dual-axis charts: three tab groups in the right column of the Simulator sample rates across temperature (0–50 °C) and pH (2–8) at the current operating point, each with two series on a primary/secondary y-axis. Ethanol Product: r_P primary + r_Gly secondary (both volumetric, g/L/h). Yeast Product: μ primary + k_d,eff secondary (both specific, h⁻¹); crossover point = washout T. Glycerol Product: r_Gly primary (volumetric, g/L/h) + q_Gly secondary (specific, g/g/h), useful for osmotic / redox stress diagnosis. All three tabs re-render on every parameter change via the 180 ms debounce.
• Nutrient coupling (Phase 2): μ is multiplied by a Liebig's-minimum factor f_nutrients = min(f_N, f_Mg, f_Zn, f_Erg, f_Tween), where each f is a Monod term on the corresponding nutrient. Growth slows sharply when any one nutrient falls below its half-saturation constant. The dominant limiter is reported in the "Limiting" pill above the time-course charts.
• FAN depletion: dN/dt = −(μ/Y_X/N)·X. With Y_X/N ≈ 10 g DCW/g N, 700 mg/L FAN supports ~7 g/L biomass growth — the boundary between normal-gravity and VHG fermentation.
• Cellular ergosterol dilution: d(Erg_q)/dt = −μ · Erg_q. Pure dilution — anaerobic yeast cannot synthesize sterols. The initial quota is erg_q_init + erg_init_broth / X0, so increasing either the per-cell reserve or the broth supplement shifts stuck-fermentation onset. This is the mechanism behind VHG stuck fermentations at 40–60% sugar utilisation.
• Adiabatic heat balance (optional): When temperature mode = adiabatic, T becomes a state variable with dT/dt = (Q_metab − Q_cool)/C_p, where Q_metab = r_P·ΔH_ferm/M_EtOH and Q_cool = k_cool·(T − T_set). Drop k_cool to see runaway heating.
• pH drift from NH₄⁺ assimilation (Phase 3): Each mole of ammonium taken up releases 1 mole of H⁺ as neutral N is incorporated into biomass. Modeled as a strong acid (pK_a = −2) with cumulative concentration (N_init − N_broth) · h_{per_N} / M_N, added to the existing aqueous-chemistry buffer solver. h_{per_N} is tunable (0 for urea-only, 1 for pure NH₄⁺, 0.5 for typical FAN mixtures).
• Inoculum sizing engineering identity (Phase 3): P = q_P·X_avg·t, so X_avg = P / (q_P·t). The sizing card solves this for target ethanol titer (auto-synced from Medium Calc) and fermentation time (read-only field auto-mirrored from Duration — the single source of truth for time), then converts X_avg → X_pitch via a tuneable ratio (default 0.6 for mild growth during lag + exponential phase).
• Live coupling to Medium Calculator: calc() in the Medium Calc updates window._calcCanonical with the unit-resolved totals (sugarG, ethVolL, volumeL, biomassG) on every input change, then calls importFromMedium() which reads those canonical values and pushes them into the Simulator's Fermentation Conditions (s, st) and Nutrient Coupling (n_init, mg_init, zn_init, erg_init_broth, tween80_init) fields — plus syncTiterToInoculum() for the Inoculum Sizing card. A recursion guard prevents re-entry. The green "← Import from Medium Calculator" button remains as a manual refresh escape hatch.
• Regime multipliers are applied relative to the strain-preset base values and are indicative only; they do not compound on repeated changes. Calibrate with your own strain data.
• Environmental viability gate (v19.90): both the maintenance term and the non-growth-associated Luedeking–Piret production are now multiplied by f_env = f_T·f_pH. At cardinal-model extremes where f_T=0 or f_pH=0, m_S·f_S·f_E,p·f_env = 0 and β·f_E,p·f_env = 0, i.e. dormant cells don't consume sugar for maintenance or produce ethanol non-growth-associatedly. Previously a ~0.5 g/L/h floor persisted at T=0 or pH=2 (physically impossible). The fix makes the r_P vs T / pH charts drop cleanly to zero outside [T_min, T_max] and [pH_min, pH_max].
• VHG glycerol coupling (v19.90): the growth-coupled glycerol term is now q_g,growth = α_Gly·μ·(1 + k_VHG,Gly·f_osm), coupling α_Gly to the same osmotic Hill that drives q_g,osm. This reflects biochemistry: under high total osmolyte, NADH disposal during biosynthesis becomes more glycerol-intensive because the cell is already stressed. At k_VHG,Gly=1 (default), full VHG (f_osm→1) doubles α_Gly. Setting k_VHG,Gly=0 recovers the pre-v19.90 behaviour. q_gly is now decomposed into q_gly,stress (osm+eth+T+pH+N, U-shape in T/pH) and q_{gly,growthlike} (α·μ·(1+k_VHG·f_osm) + q_G0, Gaussian) so the two biochemically distinct mechanisms display as separate curves on the Glycerol Product instant chart.
• Instant-prediction chart semantics (v19.90): the three prediction tabs were rebuilt to show distinct information instead of three copies of the μ-Gaussian. Ethanol Product now shows r_P primary + Y_P/S,apparent = q_P/q_S secondary (the effective yield after maintenance and glycerol diversions). Yeast Product now shows μ primary, μ−k_d,eff (net growth, dashed purple), and k_d,eff (dotted red, right axis) — the zero-crossings of the net curve mark the WASHOUT boundaries. Glycerol Product plots the two decomposition buckets described above.
• Organic acid production & dynamic pH coupling (v19.95): Lactic acid (LA) and acetic acid (AA) are now dynamic ODE state variables: dLA/dt = Y_LA/S·q_S·X, dAA/dt = Y_AA/S·q_S·X, with production yields defaulting to 0 (no production unless user enables or optimizer fits). Both accumulate during fermentation and feed back into the pH calculation via their pK_a equilibria (lactic pK_a = 3.86, acetic pK_a = 4.76) through the buildDynamicAcids() function, which replaces the static initial organic acid concentrations in the pH solver at each RK4 timestep. The full pH model now includes four acid sources: CO₂ dissolution (H₂CO₃), NH₄⁺ assimilation H⁺ release, dynamic lactic acid, and dynamic acetic acid — all solved simultaneously in the bisection-based proton balance. Environment chart shows both species on a dedicated "Org. acids (g/L)" axis.
• Model Exp. Data tab (v19.95): Fourth main tab for fitting the model to observed fermentation data. Accepts all 12 standard ethanol fermentation HPLC columns: time, pH, temperature, DCW, DP4+, DP3, DP2, DP1, lactic acid, glycerol, acetic acid, ethanol. Column names are case-insensitive with multiple aliases supported (e.g. ethanol, EtOH, E all match). DP2 + DP3 are automatically summed to a dextrin pool for scoring against the model's dextrin trajectory. Replicate samples at the same time point are automatically detected and averaged to mean ± SD.
• Model Exp. Data — four actions (v19.95):
  • Statistical analysis — evaluates raw data quality: dataset overview (time points, duration, sampling density), per-species quality table (n valid, missing, min/max/range, CV% from replicates, trend detection ↑↓→↕, quality rating ✅⚠️❌), parameter estimability checklist (which parameters the data can support estimating), and data limitation warnings (too few points, missing species, no replicates, large gaps, short duration).
  • Evaluate fit — runs the simulator with current parameters, interpolates predictions at observation times, and reports R², RMSE, and mean bias per species. Overlays observed data as scatter points on the Metabolism chart.
  • Calculate parameters — derives kinetic parameters analytically from the time-course data: μ_max (corrected: slope of ln(X) + k_d,eff ÷ f_product at operating conditions), Y_X/S (apparent ΔX/ΔS), Y_P/S (apparent ΔE/ΔS), α/β (Luedeking–Piret regression of q_P vs μ), k_d (apparent k_d,eff from decline phase), m_S (apparent stationary-phase q_S), plus glycerol and organic acid yields. All parameters labeled "apparent" with explanations of entanglement with the full ODE model.
  • Train (optimise) — Nelder–Mead simplex on user-selected parameters, normalised [0,1] within bounds, async with Stop button. 14 fittable parameters across 3 groups: growth kinetics (μ_max, K_S, m_S, k_d, k_d,E), yields (Y_X/S, Y_P/S, α_Gly, Y_LA/S, Y_AA/S), ethanol production (α, β, E_max, E_inh).
  A "Next steps" panel appears after each action with contextual guidance and re-run buttons for iterative refinement.
• Broth volume tracking from CO₂ outgassing: ethanol fermentation loses mass as C₆H₁₂O₆ → 2 C₂H₅OH + 2 CO₂ drives 0.4886 g of CO₂ out of each gram of glucose consumed. On typical VHG mashes, 5–10% of broth volume exits as gas. Three modes are available (Fermentation Conditions → Strain & operating conditions → Volume tracking): Constant V (default, no tracking); Post-proc — ODE runs at constant V, then V_final is computed from cumulative CO₂ mass balance and all in-solution species are rescaled by V₀/V_final for display; In-ODE rigorous — V becomes a state variable (15-element vector: st, s, x, e, gly, co2_aq, f_act, N, Mg, Erg_q, T, dx, V, la, aa) with dV/dt = −r_CO2·V/(ρ·1000); the companion dilution term +C·r_CO2/(ρ·1000) is applied to every per-broth-volume species (starch, glucose, biomass, ethanol, glycerol, dissolved CO₂, FAN, Mg, dextrins, lactic acid, acetic acid), derived from the product-rule expansion d(C·V)/dt = r·V. Erg_q (per-biomass mg/g DCW), f_act (dimensionless), and T (intensive) do not receive the dilution term. In-ODE mode captures the nonlinear coupling where ethanol inhibition feels the rising concentration during the run — slightly lowering total ethanol mass but raising end-of-run g/L.
• DCW → cell count conversion: cells/mL = X (g DCW/L) × f_cells × 10⁷, where f_cells is a user-editable input in the Inoculum Sizing card (field: "Cells per g DCW", units ×10¹⁰). Default 5 corresponds to ~20 pg single-cell dry weight (industrial S. cerevisiae exponential phase). Published values span 3–10 (60–200 pg/cell equivalent) depending on strain, growth phase (stationary cells carry glycogen/trehalose → heavier), ethanol stress (cells shrink in VHG runs), and measurement method. The factor is used by the Inoculum Sizing cell-density readout, the Yeast chart's cells-per-mL curve, and the Yeast summary panel's Pitch/Peak/End cell counts.

Strain presets are indicative; calibrate with your strain data. Regime modifies yields and glycerol base to mimic redox shifts.

References

Key primary sources for the kinetic, stoichiometric, and engineering choices encoded in the suite. All citations are illustrative — the implementation often blends or adapts published forms; see the bullets above for exact functional forms.

• Luedeking, R. & Piret, E. L. (1959). A kinetic study of the lactic acid fermentation. J. Biochem. Microbiol. Technol. Eng. 1: 393–412. — Original growth-associated + non-growth product term q_P = α·μ + β.
• Luong, J. H. T. (1985). Kinetics of ethanol inhibition in alcohol fermentation. Biotechnol. Bioeng. 27: 280–285. — f_E with E_inh threshold and E_max; default ethanol-inhibition model in this suite.
• Aiba, S., Shoda, M. & Nagatani, M. (1968). Kinetics of product inhibition in alcohol fermentation. Biotechnol. Bioeng. 10: 845–864. — Exponential f_E = exp(−k_E·E); legacy alternative.
• Andrews, J. F. (1968). A mathematical model for the continuous culture of microorganisms utilizing inhibitory substrates. Biotechnol. Bioeng. 10: 707–723. — Haldane substrate-inhibition term S/(K_s+S+S²/K_i,S).
• Bai, F. W., Anderson, W. A. & Moo-Young, M. (2008). Ethanol fermentation technologies from sugar and starch feedstocks. Biotechnol. Adv. 26: 89–105. — VHG fermentation, industrial yield benchmarks (Y_E/S=0.45–0.49), feedstock yield coefficients.
• Walker, G. M. (2011). Pichia and Saccharomyces yeast biology. The Yeasts: A Taxonomic Study, 5th ed. — Magnesium / zinc / vitamin requirements, sterol & unsaturated-fatty-acid auxotrophy under anaerobiosis.
• Casey, G. P. & Ingledew, W. M. (1986). Ethanol tolerance in yeasts. CRC Crit. Rev. Microbiol. 13: 219–280. — Membrane / Mg²⁺ / lipid mechanisms behind ethanol toxicity; basis for VHG sterol & oleate supplementation.
• Pham, T. K. & Wright, P. C. (2008). The proteomic response of Saccharomyces cerevisiae in very high glucose conditions. J. Proteome Res. 7: 4766–4774. — Osmotic-stress glycerol overproduction; basis for the Hill term q_g,osm.
• Atala, D. I. P., Costa, A. C., Maciel, R. & Maciel Filho, R. (2001). Kinetics of ethanol fermentation with high biomass concentration. Appl. Biochem. Biotechnol. 91–93: 353–365. — Cell-death rate dependence on ethanol & temperature.
• Verduyn, C. et al. (1990). Physiology of Saccharomyces cerevisiae in anaerobic glucose-limited chemostat cultures. J. Gen. Microbiol. 136: 395–403. — Anaerobic Y_X/S ≈ 0.10 g/g, ergosterol & UFA quotas, maintenance coefficient m_S.
• Cot, M., Loret, M.-O., François, J. & Benbadis, L. (2007). Physiological behaviour of Saccharomyces cerevisiae in aerated fed-batch fermentation for high-level production of bioethanol. FEMS Yeast Res. 7: 22–32. — Industrial fed-batch & ergosterol dilution behaviour.
• Stewart, G. G. (2017). Brewing and Distilling Yeasts. Springer. — α-Amylase / glucoamylase loading conventions (≈500 U/g starch / 200 U/g starch), nitrogen targets (200–700 mg FAN/L for normal-gravity to VHG).
• Pirt, S. J. (1965). The maintenance energy of bacteria in growing cultures. Proc. R. Soc. B 163: 224–231. — Maintenance-energy framework underpinning the m_S·X term.
• Kosaric, N. & Vardar-Sukan, F. (2001). Potential source of energy and chemical products. In The Biotechnology of Ethanol, Wiley-VCH. — Feedstock-yield reference values for corn / wheat / cassava / molasses.

Medium Calculator — Quick Start

The calculator is organized as a two-column layout with five top-level collapsible cards: Target Production & Broth Target Concentrations on the left (inputs); Summary, Carbon Mass Balance, and Results & Tabs on the right (outputs). Each card with sub-categories opens further into Macronutrients / Trace Metals / Vitamins / Anaerobic Lipid Factors sub-collapsibles, all collapsed by default to keep the view tidy.

1. Open Target Production → Ethanol Fermentation Targets. Enter Target Ethanol Production (default 10 L; switchable to US gallons) and Target Ethanol Titer (default 17% v/v — VHG territory; also % w/v or g/L). The calculator converts internally before running the mass balance.
2. Open Target Production → Feedstock & Substrate. Pick a Feedstock from the 10-option dropdown (default: Corn — whole kernel). Selecting one auto-populates Moisture % and Starch Fraction. Each option carries a fermentable-sugar yield (g sugar / g dry feedstock, 0.48–1.10) shown next to the dropdown. Override Moisture (grains 12–14%, molasses ~20%, dried cassava ~12%) or Starch Fraction (% of sugar-eq from starch hydrolysis, not mass%) if your raw material differs from the defaults. Optional: enable "Credit nutrients provided by feedstock" to subtract built-in FAN / K / Mg / vitamins from the salt recipe.
3. Open Target Production → Stoichiometric Yields & Batch %. Slide Y_E/S (0.38–0.50, default 0.46), Y_X/S (0.02–0.10, default 0.05), Y_Gly/S (0.01–0.08, default 0.04), and the % Nutrients in Initial Batch Phase (30–100, default 100 = full batch).
4. Click "Load Standard" in the Broth Target Concentrations card to populate all 23 nutrient rows (7 macronutrients, 6 trace metals, 8 vitamins, 2 lipid factors) with fuel-ethanol defaults. Expand any of the four nutrient sub-cards (▶ Macronutrients / Trace Metals / Vitamins / Anaerobic Lipid Factors) to edit individual targets.
5. Read the Summary card — three snapshot-style rows of computed values:
   • Production overview: Total Sugar Required (kg / lbs / ton), Ethanol Produced (L abs. / gal / kg / lbs), Broth Volume (L / gal / hL) with biomass-produced as descriptor. Switch units via the compact Units strip at the bottom.
   • Substrate split (visible only when Starch Fraction > 0): Starch Needed (starchFrac × sugar ÷ 1.11), Direct Glucose ((1 − starchFrac) × sugar), Feedstock as-received (dry mass ÷ (1 − moisture)).
   • Enzyme loading (same trigger): α-Amylase @ 500 U/g starch, Glucoamylase @ 200 U/g starch, plus combined total. Auto-formatted as U / kU / MU based on magnitude.
   For glucose-only feedstocks (cane / beet molasses, pure sugars), the substrate + enzyme rows hide and a small italic notice replaces them.
6. Check the Carbon Mass Balance card — the stacked horizontal bar should show 95–100% closure across Ethanol / CO₂ / Glycerol / Biomass / Other. Below 95% means missing products; above 100% means yield coefficients exceed stoichiometry and need reducing.
7. Open Results & Tabs. Five tabs across the top:
   • Total Required — every nutrient grouped into the same 4 sub-cards (Macro / Trace / Vitamin / Lipid), each row showing the chosen salt, mass to weigh per batch, and a ✓ "covered" indicator when an element is auto-supplied as a co-element from another salt (e.g., S from MgSO₄).
   • Batch Medium & VHG Feed — splits the Total Required between the initial batch and the fed-batch supplement based on the Batch % slider.
   • Reference — typical concentrations and molar masses; also includes an Ethanol Fermentation — Process Notes collapsible with 12 topical guidance items (yield expectations, FAN targets, Mg / Zn / vitamin roles, VHG operation, pH & temperature ranges, sterility tips).
   • Test Medium — enter actual salt masses you weighed out and see coverage % per nutrient (green ≥100%, amber 50–99%, red <50%) plus the most-limiting row identified in the verdict bar.
8. Customize individual nutrients by editing target concentrations in Broth Target Concentrations or picking different salts from the dropdowns in Total Required. Recipe updates live across all tabs.
9. Switch to the Time-Course Simulator tab. Initial glucose, starch, FAN, Mg, Zn, ergosterol, oleate, broth volume, and target titer are already live-synced from the Medium Calc — no Import click needed for routine edits. Click Run Simulation or rely on the 180 ms auto-debounce.

Live sync: Every input change in the Medium Calculator (target ethanol, titer, feedstock, starch fraction, moisture, Y_E/S / Y_X/S / Y_Gly/S sliders, batch %, and every nutrient target) propagates automatically to the Simulator's Substrate Pools, Init Conditions, Nutrient Coupling, and Inoculum Sizing cards. The "← Import from Medium Calculator" button in the Simulator is still there as a manual refresh if you ever need to force a re-sync. All headers (outer h2-style and inner sub-collapsibles) are click-to-toggle — useful once you've configured a section.