What Is the Monod Kinetics Equation?
The Monod kinetics equation is μ = μmax · S / (Ks + S), where μ is the specific growth rate (h−1), μmax is the maximum specific growth rate the organism can achieve when substrate is in excess, S is the concentration of the limiting substrate (g/L or mg/L), and Ks is the half-saturation constant — the substrate concentration at which μ = μmax/2. It describes how microbial growth rate depends on the concentration of a single growth-limiting substrate.
Published by Jacques Monod in 1949, the Monod equation remains the most widely used kinetic model in bioprocess engineering and environmental microbiology, underpinning fed-batch feeding profiles, chemostat design, and microbial growth modelling.
The equation produces a hyperbolic curve: at low substrate concentrations (S « Ks), growth rate increases nearly linearly with S. As S rises well above Ks, growth rate saturates and approaches μmax asymptotically. This behaviour mirrors the Michaelis–Menten equation for enzyme kinetics, but the Monod equation is empirical — it was fit to whole-cell growth data, not derived from a mechanistic model of intracellular biochemistry.
Monod developed the equation from chemostat experiments with Escherichia coli and Bacillus subtilis growing on single carbon sources. Despite its simplicity, the model predicts steady-state behaviour in continuous culture remarkably well and forms the foundation for more complex kinetic models used in bioprocess design.
Understanding μmax and Ks
μmax and Ks are the two empirical parameters of the Monod equation, and together they fully characterise how a given organism responds to substrate availability under a fixed set of environmental conditions (temperature, pH, medium composition).
μmax (maximum specific growth rate) is the growth rate achieved when substrate is in large excess (S » Ks). It reflects the organism's intrinsic capacity to divide under ideal nutritional conditions. For E. coli on glucose at 37 °C, μmax is typically 0.7–1.0 h−1 in minimal medium, corresponding to a doubling time of 42–60 minutes. μmax depends on temperature, pH, medium composition, and strain genotype — it is not a universal constant for a species.
Ks (half-saturation constant) is the substrate concentration at which μ equals exactly half of μmax. A low Ks means the organism achieves near-maximal growth at very low substrate levels — it is an efficient scavenger. E. coli has a Ks for glucose of only 2–4 mg/L, while S. cerevisiae shows Ks values of 25–180 mg/L depending on the glucose transport system active at a given concentration.
Two limiting behaviours emerge from the Monod equation:
- When S « Ks (substrate-limited): μ ≈ (μmax/Ks) · S — growth is first-order in substrate concentration. This regime governs fed-batch feeding strategies and chemostat washout.
- When S » Ks (substrate-saturated): μ ≈ μmax — growth rate is independent of S. This is the exponential (log) phase in batch culture when nutrients are abundant.
The ratio μmax/Ks is sometimes called the substrate affinity and determines competitive fitness at low substrate concentrations. Organisms with high μmax/Ks dominate in nutrient-poor environments — a principle exploited in wastewater treatment and chemostat selection experiments.
Typical μmax and Ks Values by Organism
Monod kinetics parameters vary by more than two orders of magnitude across bioprocess-relevant organisms. The table below compiles literature values for glucose as the limiting substrate under standard culture conditions.
| Organism | T (°C) | μmax (h−1) | td (h) | Ks glucose (mg/L) | Medium |
|---|---|---|---|---|---|
| E. coli K-12 | 37 | 0.7–1.0 | 0.7–1.0 | 2–4 | M9 minimal + glucose |
| E. coli BL21 | 37 | 0.8–1.2 | 0.6–0.9 | 2–5 | LB / 2xYT |
| S. cerevisiae | 30 | 0.35–0.45 | 1.5–2.0 | 25–180 | YPD / SC minimal |
| Pichia pastoris (glycerol) | 30 | 0.15–0.25 | 2.8–4.6 | 10–30 | BSM + glycerol |
| Pichia pastoris (methanol) | 30 | 0.04–0.10 | 6.9–17 | 100–400 | BSM + methanol |
| CHO-K1 / DG44 | 37 | 0.03–0.04 | 17–23 | 50–75 | CD-CHO / FortiCHO |
| HEK293 | 37 | 0.025–0.035 | 20–28 | 40–80 | FreeStyle 293 |
| Sf9 (insect) | 27 | 0.025–0.035 | 20–28 | 30–60 | Sf-900 III |
| Bacillus subtilis | 37 | 0.5–0.8 | 0.9–1.4 | 5–15 | LB / minimal |
| Corynebacterium glutamicum | 30 | 0.3–0.45 | 1.5–2.3 | 10–50 | CGXII minimal |
Note the extremely low Ks for E. coli glucose uptake (2–4 mg/L). In fed-batch culture, this means even traces of glucose above the feed rate saturate the growth rate, which is why exponential feeding strategies work so effectively for maintaining a target μ below μmax.
How to Determine μmax and Ks from Data
Both μmax and Ks must be determined experimentally for each organism-medium-temperature combination. Three methods are commonly used, each with distinct advantages.
Method 1: Lineweaver–Burk (Double-Reciprocal) Plot
Rearrange the Monod equation to: 1/μ = (Ks/μmax) · (1/S) + 1/μmax. Plot 1/μ (y-axis) versus 1/S (x-axis). The y-intercept equals 1/μmax and the slope equals Ks/μmax.
This method is intuitive and widely taught, but the reciprocal transformation amplifies experimental error at low S values (where 1/S is large), biasing both parameter estimates. Use it for quick visual checks, not final parameter values.
Method 2: Eadie–Hofstee Plot
Rearrange to: μ = −Ks · (μ/S) + μmax. Plot μ (y-axis) versus μ/S (x-axis). The y-intercept is μmax, the slope is −Ks. This linearisation distributes error more evenly than Lineweaver–Burk.
Method 3: Nonlinear Regression (Preferred)
Fit μ = μmax · S / (Ks + S) directly to paired (μ, S) data using least-squares minimisation. This avoids transformation bias and provides confidence intervals for both parameters. Available in Python (scipy.optimize.curve_fit), MATLAB (nlinfit), R (nls), and Excel Solver.
Worked Example: Estimating μmax and Ks from Chemostat Data
An E. coli K-12 strain is grown in a series of glucose-limited chemostats at 37 °C. Steady-state residual glucose and dilution rate (= μ at steady state) are measured:
| D = μ (h−1) | S (mg/L) | 1/μ | 1/S (L/mg) |
|---|---|---|---|
| 0.15 | 0.35 | 6.667 | 2.857 |
| 0.30 | 0.88 | 3.333 | 1.136 |
| 0.50 | 1.85 | 2.000 | 0.541 |
| 0.65 | 3.50 | 1.538 | 0.286 |
| 0.80 | 8.00 | 1.250 | 0.125 |
Lineweaver–Burk regression: 1/μ = 1.996 · (1/S) + 0.983
- y-intercept = 1/μmax = 0.983 → μmax = 1.02 h−1
- slope = Ks/μmax = 1.996 → Ks = 1.996 × 1.02 = 2.04 mg/L
These values are consistent with published E. coli K-12 data: μmax ≈ 0.7–1.0 h−1 and Ks ≈ 2–4 mg/L on glucose minimal medium at 37 °C. The data here reflect a fast-growing strain in well-controlled chemostats.
Interactive Monod Curve Explorer
Use the sliders below to explore how μmax and Ks shape the Monod curve. Adjust the parameters to match different organisms — try μmax = 0.8, Ks = 3 for E. coli, or μmax = 0.04, Ks = 60 for CHO cells.
The blue dot marks the Ks point where μ = μmax/2. The dashed line shows μmax. Hover over the curve to read exact values.
Fit μmax and Ks from your own data
Enter your substrate-versus-growth-rate measurements and the free Monod Kinetics Calculator fits μmax and Ks directly by nonlinear regression — no double-reciprocal transform needed.
Practical Bioprocess Applications
The Monod equation is not merely academic — it directly informs three core bioprocess design decisions: fed-batch feeding profiles, chemostat operating conditions, and bioreactor scale-up strategies.
Fed-Batch Exponential Feeding
In fed-batch culture, the goal is to maintain a constant specific growth rate μset below μmax to avoid overflow metabolism (acetate in E. coli, ethanol in S. cerevisiae). Because Ks for glucose is very low (2–4 mg/L for E. coli), the residual glucose at steady state is negligible, and the exponential feed rate is:
F(t) = (μset / Yx/s) · (X0 V0 / Sf) · eμset · t
Where Yx/s is the biomass yield on substrate (g/g), X0V0 is the initial biomass, and Sf is the feed concentration (g/L). Typical μset values: 0.1–0.3 h−1 for E. coli, 0.01–0.03 h−1 for CHO cells.
Fed-Batch Feed Strategy Calculator
Calculate exponential, linear, and constant feeding profiles using Monod kinetics. Organism presets for E. coli, CHO, Pichia, and yeast.
Chemostat Design and Washout
In a chemostat (CSTR), the dilution rate D = F/V sets the growth rate at steady state (μ = D). The Monod equation predicts the residual substrate concentration:
Sss = Ks · D / (μmax − D)
As D approaches μmax, Sss rises sharply and eventually exceeds the feed concentration, causing washout — cells are diluted out faster than they can divide. The critical dilution rate Dcrit = μmax · S0 / (Ks + S0) sets the maximum operating dilution rate. For E. coli with S0 = 10 g/L and Ks = 3 mg/L, Dcrit ≈ μmax (because S0 » Ks).
Worked Example: Chemostat Steady-State Prediction
A 2 L chemostat runs S. cerevisiae on glucose-limited minimal medium. Given: μmax = 0.40 h−1, Ks = 100 mg/L, Yx/s = 0.50 g/g, S0 = 10 g/L. Dilution rate D = 0.25 h−1.
Step 1: Residual substrate: Sss = 100 × 0.25 / (0.40 − 0.25) = 25.0 / 0.15 = 167 mg/L = 0.167 g/L
Step 2: Steady-state biomass: Xss = Yx/s · (S0 − Sss) = 0.50 × (10 − 0.167) = 4.92 g/L
Step 3: Volumetric productivity: rx = D · Xss = 0.25 × 4.92 = 1.23 g/L/h
Step 4: Washout dilution rate: Dcrit = 0.40 × 10 / (0.100 + 10) = 0.396 h−1
Monod Kinetics in Scale-Up
At production scale, mixing time increases and substrate gradients form near the feed point. Cells cycling through high-glucose zones (near the feed pipe) and low-glucose zones (far from the feed pipe) experience oscillating μ values that the Monod equation predicts. Computational fluid dynamics (CFD) models couple the Monod equation with hydrodynamic simulations to predict substrate gradient effects on growth and product quality.
CellTrack — Growth & Viability Tracker
Track VCD, viability, metabolites, and auto-calculate specific growth rate (μ), doubling time, and IVCD from your experimental data.
Extensions: Inhibition, Multiple Substrates, and Beyond
The basic Monod equation assumes no inhibition and a single limiting substrate. Real bioprocesses often violate these assumptions, which has led to widely used extensions.
Substrate Inhibition: The Haldane–Andrews Model
When high substrate concentrations inhibit growth (common with methanol, phenol, and aromatic hydrocarbons), the Haldane–Andrews model adds an inhibition term:
μ = μmax · S / (Ks + S + S²/KI)
Where KI is the substrate inhibition constant (g/L). Growth rate reaches a maximum at Sopt = √(Ks · KI) and declines at higher substrate concentrations. For Pichia pastoris on methanol, KI is typically 3–10 g/L, which is why methanol feeding requires tight control.
Product Inhibition
When the product itself inhibits growth (ethanol in yeast, lactic acid in Lactobacillus), the Levenspiel model modifies μmax:
μ = μmax · (1 − P/Pmax)n · S / (Ks + S)
Where P is the product concentration, Pmax is the critical product concentration at which growth ceases, and n is an empirical exponent (typically 0.5–2.0). For ethanol fermentation by S. cerevisiae, Pmax is approximately 90–120 g/L.
Multiple Substrates
When two substrates limit growth simultaneously, the multiplicative Monod model is commonly used:
μ = μmax · [S1/(Ks1 + S1)] · [S2/(Ks2 + S2)]
This applies when, for example, both glucose and dissolved oxygen limit growth simultaneously in a poorly aerated bioreactor. The alternative minimum model uses μ = μmax · min(S1/(Ks1+S1), S2/(Ks2+S2)), following Liebig's law of the minimum.
| Model | Equation | When to Use | Extra Parameter(s) |
|---|---|---|---|
| Haldane–Andrews | μmax S / (Ks + S + S²/KI) | Substrate inhibition (methanol, phenol) | KI (g/L) |
| Levenspiel | μmax (1−P/Pmax)n · S/(Ks+S) | Product inhibition (ethanol, lactic acid) | Pmax, n |
| Contois | μmax S / (KxX + S) | High cell density, mass transfer limitation | Kx (g/g) |
| Tessier | μmax (1 − e−S/Ks) | Alternative to Monod (exponential saturation) | None (same count) |
| Moser | μmax Sn / (Ks + Sn) | Sigmoidal growth response | n (Hill coefficient) |
| Dual substrate | μmax · [S1/(Ks1+S1)] · [S2/(Ks2+S2)] | Co-limitation (glucose + O2) | Ks1, Ks2 |
OTR & kLa Estimator
Check whether your aeration can supply enough oxygen to match the OUR demand at your target growth rate — essential when dual-substrate (glucose + O2) limitation applies.
Growth Rate Comparison Across Organisms
Monod Kinetics: The Actual Numbers for E. coli, CHO, Yeast, and Pichia
Most textbook tables lump “E. coli” or “yeast” into a single entry. In practice the strain matters, and the primary literature reports narrower, better-anchored values. Here are the published μmax and Ks ranges you can cite when you set up a chemostat or a fed-batch feed profile, together with the sources they come from.
μ = μmax · S / (Ks + S)
| Strain / cell line | Substrate | μmax (h−1) | Ks (mg/L) | Source |
|---|---|---|---|---|
| E. coli K-12 (MG1655, W3110) | Glucose | 0.6–1.0 | 5–15 | Kovárová-Kovar & Egli, 1998 |
| E. coli B / BL21 | Glucose | 0.7 (typical) | 4–10 | Kovárová-Kovar & Egli, 1998 |
| S. cerevisiae CEN.PK | Glucose (aerobic) | 0.40 (typical) | 20–50 | van Dijken et al., 2000 |
| P. pastoris (K. phaffii) | Glycerol | 0.18 (typical) | 3–5 | Zhang et al., 2000 |
| P. pastoris (K. phaffii) | Methanol | 0.14 (typical) | 10–40 | Zhang et al., 2000 |
| CHO-K1 (mammalian) | Glucose | 0.03–0.04 | 100–500 | Miller et al., 1989 |
A few practical observations sit behind these numbers. First, E. coli K-12 and BL21 have similar substrate affinity because both express the same phosphoenolpyruvate-dependent glucose PTS. Second, the S. cerevisiae CEN.PK entry is the aerobic value; under respiro-fermentative conditions the apparent Ks rises because low-affinity Hxt1/Hxt3 transporters dominate. Third, Pichia pastoris shows a lower Ks on glycerol than on methanol, which is one reason the AOX promoter induction phase is harder to control than the glycerol biomass build-up.
The CHO Ks of 100–500 mg/L (Miller et al., 1989) has a direct consequence for perfusion and fed-batch design: mammalian cultures cannot be run at a residual glucose approaching zero. If you want to stay near μmax, you need S ≥ 10 · Ks, because at S = 10 Ks the Monod equation gives μ/μmax = 10/(1+10) = 0.91. For CHO that means holding residual glucose at 1–5 g/L. For E. coli the same rule of thumb translates to only 50–150 mg/L residual glucose, which is why E. coli exponential feed strategies can run so much leaner without cutting into μ.
Feed-strategy rule of thumb
- Keep S ≥ 10 · Ks to stay within 91% of μmax.
- E. coli: residual glucose ∼100 mg/L is enough. P. pastoris on glycerol: ∼50 mg/L. CHO on glucose: 1–5 g/L.
- Below S ≈ Ks, μ drops proportionally with S. Feed profiles that overshoot the cell-population growth rate will pull S into this regime and stall the culture.
Frequently Asked Questions
What is the Monod kinetics equation?
The Monod kinetics equation is μ = μmax · S / (Ks + S), where μ is the specific growth rate (h−1), μmax is the maximum specific growth rate the organism can achieve when substrate is in excess, S is the concentration of the growth-limiting substrate (g/L or mg/L), and Ks is the half-saturation constant — the substrate concentration at which μ = μmax/2. It produces a hyperbolic saturation curve and was published by Jacques Monod in 1949 (Annual Review of Microbiology). When S « Ks, growth is first-order in substrate (μ ≈ (μmax/Ks)·S); when S » Ks, growth saturates at μmax.
What does the constant Ks represent in the Monod kinetics model?
Ks is the half-saturation constant in the Monod kinetics model — the substrate concentration at which the specific growth rate μ equals half of μmax. It quantifies the affinity of the organism for the limiting substrate: a low Ks means the cell scavenges substrate efficiently at low concentrations and sustains near-maximal growth even when substrate is scarce, while a high Ks means the organism needs much higher substrate to approach μmax. Typical Ks values for glucose: E. coli 2–4 mg/L, S. cerevisiae 25–180 mg/L, P. pastoris 10–30 mg/L, CHO cells 50–75 mg/L. Ks has the same units as substrate concentration (mg/L or g/L). It is not a thermodynamic equilibrium constant — it is an empirical fit parameter, unlike the mechanistically derived Km in Michaelis–Menten enzyme kinetics.
What are typical Monod kinetics parameter values for E. coli, CHO, and yeast?
Typical μmax on glucose at optimal temperature: E. coli 0.7–1.0 h−1 (minimal medium, 37 °C), S. cerevisiae 0.35–0.45 h−1 (30 °C), Pichia pastoris 0.15–0.25 h−1 on glycerol (30 °C), CHO cells 0.03–0.04 h−1 (37 °C). Typical Ks for glucose: E. coli 2–4 mg/L, S. cerevisiae 25–180 mg/L (with Crabtree effect), Pichia pastoris 10–30 mg/L, CHO cells 50–75 mg/L. E. coli's low Ks reflects its lac/PTS transport systems' high affinity; CHO cells have a high Ks because mammalian glucose transporters (GLUT1/3) saturate at higher concentrations.
How do you determine μmax and Ks from experimental data?
The most common method is the Lineweaver–Burk (double-reciprocal) plot: plot 1/μ versus 1/S and fit a straight line. The y-intercept equals 1/μmax and the slope equals Ks/μmax. However, nonlinear regression fitting μ = μmax · S / (Ks + S) directly is statistically preferred because it avoids the bias introduced by reciprocal transformation.
What are typical μmax and Ks values for common bioprocess organisms?
Typical μmax values on glucose at optimal temperature: E. coli 0.7–1.0 h−1 (minimal medium, 37 °C), S. cerevisiae 0.35–0.45 h−1 (30 °C), Pichia pastoris 0.15–0.25 h−1 on glycerol (30 °C), CHO cells 0.03–0.04 h−1 (37 °C). Typical Ks for glucose: E. coli 2–4 mg/L, S. cerevisiae 25–180 mg/L, CHO cells 50–75 mg/L.
What are the limitations of the Monod equation?
The Monod equation assumes a single limiting substrate, no product or substrate inhibition, balanced growth, dilute cell concentrations, and constant μmax and Ks. It breaks down when substrate is inhibitory (use Haldane–Andrews), when multiple substrates co-limit growth, during lag or death phases, when product accumulates to inhibitory levels, or when intracellular metabolic regulation causes hysteresis effects.
What is the difference between the Monod equation and Michaelis–Menten kinetics?
Both have the same hyperbolic mathematical form, but Michaelis–Menten describes enzyme reaction velocity as a function of substrate for a single enzyme with a mechanistic derivation. The Monod equation describes whole-cell growth rate as a function of limiting substrate and is purely empirical — it was fit to experimental data, not derived from intracellular biochemistry.
How is the Monod equation used in fed-batch fermentation?
In fed-batch fermentation, the Monod equation underpins exponential feeding. By keeping S low (S « Ks), μ becomes first-order in S. The feed rate F(t) = (μset · X · V) / (Yx/s · Sf) maintains the desired growth rate below μmax, avoiding overflow metabolism (acetate in E. coli, ethanol in yeast). Typical μset is 0.1–0.3 h−1 for E. coli and 0.01–0.03 h−1 for CHO.
What are typical Monod Ks values for E. coli, yeast, and CHO?
Published Ks values on glucose: E. coli K-12 5–15 mg/L and E. coli B/BL21 4–10 mg/L (Kovárová-Kovar & Egli, Microbiol. Mol. Biol. Rev. 1998); S. cerevisiae CEN.PK 20–50 mg/L (van Dijken et al., Enz. Microb. Technol. 2000); Pichia pastoris (Komagataella phaffii) 3–5 mg/L on glycerol and 10–40 mg/L on methanol (Zhang et al., Biotechnol. Prog. 2000); CHO-K1 100–500 mg/L on glucose (Miller et al., Biotechnol. Bioeng. 1989). E. coli's very low Ks reflects the high-affinity PTS glucose transport system, while the CHO Ks reflects GLUT1/GLUT3 saturation kinetics in mammalian cells.
How is Monod kinetics used in fed-batch bioreactor design?
In fed-batch bioreactor design, the feed rate is chosen so that residual substrate stays at or above about 10 · Ks. At S = 10 · Ks the Monod equation gives μ = μmax · 10/(1+10) = 0.91 · μmax, keeping the culture near μmax without overflow. Below that threshold μ drops proportionally with S. For E. coli (Ks ≈ 10 mg/L on glucose) a residual set point of ∼100 mg/L sustains near-maximal growth. For CHO cells (Ks ≈ 100–500 mg/L) the equivalent set point is 1–5 g/L residual glucose. Setting this window correctly prevents both starvation (S « Ks) and overflow metabolism (S well above the critical uptake rate).
What are typical Monod parameters for CHO cells growing on glutamine (rather than glucose)?
Glutamine is a second co-limiting substrate for CHO and hybridoma cells, and its Monod half-saturation constant sits an order of magnitude lower than for glucose. Published Ks(glutamine) values: approximately 0.06 mM (~9 mg/L) for murine hybridoma (Batt & Kompala, Biotechnol. Bioeng. 1989) and 0.15 mM (~22 mg/L) for CHO (Pörtner & Schäfer, J. Biotechnol. 1996). μmax under glutamine limitation stays close to the glucose-limited value of 0.03–0.04 h−1 because both substrates are co-required for growth. In practice CHO fed-batch and perfusion designs treat glutamine as the limiting substrate more often than glucose, because chemical hydrolysis to ammonia at 37 °C degrades roughly 8–10% of the glutamine per day and forces low residual set points (typically 0.2–0.5 mM) to avoid ammonia inhibition of growth and glycosylation. Glutamine-substitute media (glutamate + glutamine synthetase, or dipeptides such as GlutaMAX) sidestep the hydrolysis problem but change the effective Monod parameters.
Is the Monod equation applicable to batch processes?
Yes, but with a caveat. The Monod equation describes balanced growth and is most rigorously measured at chemostat steady state, where μ equals the dilution rate. In batch culture the substrate is usually far above Ks for most of the exponential phase, so μ sits at μmax and the Monod dependence only appears briefly during deceleration as S falls toward Ks. Parameters fitted from batch data — especially Ks — are therefore less reliable than chemostat-derived values.
How do you calculate the maximum specific growth rate (μmax)?
μmax is the plateau the Monod curve approaches when substrate is non-limiting (S » Ks). Measure it as the slope of ln(biomass) versus time during unrestricted exponential growth, or extrapolate a Monod fit of μ-versus-S data to high S. The free Monod Kinetics Calculator on this site fits μmax and Ks directly from substrate-rate data.
How do you integrate the Monod equation over a batch culture?
Combined with a substrate balance dS/dt = −(1/Yx/s)·μX and the biomass balance dX/dt = μX, the Monod model has no simple closed-form solution when Ks is significant, so it is integrated numerically (for example with Euler or Runge–Kutta methods). Two limiting cases simplify by hand: when S » Ks the equation reduces to exponential growth (μ ≈ μmax) and X = X0·eμmax·t; when S « Ks it becomes first-order in S. For most of a batch run S sits well above Ks, so numerical integration mainly matters through the deceleration phase as S falls toward Ks.
Como linearizar a equação de Monod?
A equação de Monod é linearizada da mesma forma que a de Michaelis–Menten. O gráfico de Lineweaver–Burk representa 1/μ em função de 1/S (inclinação Ks/μmax, intercepto 1/μmax); o gráfico de Hanes–Woolf representa S/μ em função de S; e o gráfico de Eadie–Hofstee representa μ em função de μ/S. A regressão não linear sobre μ = μmax·S/(Ks+S) é preferível, porque as transformações lineares distorcem os erros.
Hvad er Monod-kinetik?
Monod-kinetik beskriver, hvordan den specifikke væksthastighed afhænger af et enkelt begrænsende substrat: μ = μmax·S/(Ks+S). μmax er den maksimale specifikke væksthastighed, og Ks er halvmætningskonstanten (den substratkoncentration, hvor μ er halvdelen af μmax). Det er den mikrobielle vækst-analog til Michaelis–Menten.
¿Cómo hallar μ a partir de la ecuación de Monod con μmax y Ks?
Sustituya la concentración del substrato limitante S en μ = μmax·S/(Ks+S). Si se conoce μ y se necesita hallar S, reordene la ecuación a S = Ks·μ/(μmax−μ). Ambas formas son equivalentes: la primera da la velocidad de crecimiento a partir del substrato, y la segunda da el substrato a partir de una velocidad de crecimiento conocida.
Related Tools
- Fed-Batch Calculator — Calculate exponential, linear, and constant feeding profiles using Monod kinetics with organism presets.
- CellTrack — Track VCD, viability, and auto-calculate μ, doubling time, and IVCD from your time-course data.
- Growth Curve Fitter — Fit growth curves to logistic, Gompertz, and Baranyi models to extract μmax and lag time.
References
- Monod J. The growth of bacterial cultures. Annual Review of Microbiology. 1949;3:371–394. doi:10.1146/annurev.mi.03.100149.002103
- Kovárová-Kovar K, Egli T. Growth kinetics of suspended microbial cells: from single-substrate-controlled growth to mixed-substrate kinetics. Microbiology and Molecular Biology Reviews. 1998;62(3):646–666. doi:10.1128/mmbr.62.3.646-666.1998
- Shuler ML, Kargi F, DeLisa MP. Bioprocess Engineering: Basic Concepts. 3rd ed. Pearson; 2017. ISBN: 978-0-13-706270-6.
- Han K, Levenspiel O. Extended Monod kinetics for substrate, product, and cell inhibition. Biotechnology and Bioengineering. 1988;32(4):430–447. doi:10.1002/bit.260320404
- van Dijken JP, Bauer J, Brambilla L, et al. An interlaboratory comparison of physiological and genetic properties of four Saccharomyces cerevisiae strains. Enzyme and Microbial Technology. 2000;26(9–10):706–714. doi:10.1016/S0141-0229(00)00162-9
- Zhang W, Bevins MA, Plantz BA, Smith LA, Meagher MM. Modeling Pichia pastoris growth on methanol and optimising the production of a recombinant protein. Biotechnology Progress. 2000;16(6):995–1004. doi:10.1021/bp000116h
- Miller WM, Wilke CR, Blanch HW. Effects of dissolved oxygen concentration on hybridoma growth and metabolism in continuous culture. Biotechnology and Bioengineering. 1989;33(4):477–486. doi:10.1002/bit.260330413