Culture cells aseptically, count and passage them, control contamination, model growth, understand bioreactors and purification, and work under GMP with validated systems and batch data.

Cell Culture & Bioprocessing

## Why it matters

Bacteria and yeast are the workhorses of many bioprocesses: they make insulin, enzymes, plasmid DNA and many other products. Knowing how fast a culture grows tells you when to induce product expression, when to feed, when to harvest and whether something is wrong (a slow culture can mean a bad medium lot, wrong temperature or contamination). Growth rates also let you compare strains and media objectively instead of saying "it looked a bit slower".

## The phases of a batch growth curve

Inoculate microbes into fresh medium in a closed flask or bioreactor and measure them over time, and you see four phases:

1. **Lag phase:** cells adjust to the new medium; little increase in number.
2. **Exponential (log) phase:** cells divide at a constant rate; the population doubles every fixed interval.
3. **Stationary phase:** a nutrient runs out or waste builds up; growth and death balance.
4. **Death (decline) phase:** viable cells decrease.

Growth is usually followed by **optical density at 600 nm (OD600)**, which measures light scattered by the cells. OD is only proportional to cell concentration over a limited range (often up to about 0.4 to 1.0 depending on the instrument), so dense samples are diluted before reading and the reading multiplied back. Viable counts (colony-forming units per mL) and dry weight are other measures.

## The maths of exponential growth

In the exponential phase:

```text
X(t) = X0 x e^(mu x t)
ln X(t) = ln X0 + mu x t
```

- **X** is biomass or OD, **t** is time, and **mu** is the **specific growth rate** (per hour).
- Taking the natural log turns the curve into a **straight line**, and its slope is mu. That is why growth curves are plotted on a log scale.
- **Doubling time** td = ln(2) / mu.

**Worked example.** OD600 is 0.050 at 2.0 h and 0.302 at 4.0 h, both in the exponential phase.

```text
mu = (ln 0.302 - ln 0.050) / (4.0 - 2.0) = (-1.197 - (-2.996)) / 2.0 = 0.90 per hour
td = 0.693 / 0.90 = 0.77 h, about 46 minutes
```

Using only two points is sensitive to noise. Better practice is to fit a straight line (linear regression) through ln(OD) for all points in the exponential phase, which is what the activity does.

## Choosing the exponential phase

Plot ln(OD) against time. The exponential phase is the straight section. Leave out:

- the **lag** at the start, where the line is flat;
- the **slowing** near stationary phase, where the line bends over;
- any readings outside the instrument's linear range.

Including lag or stationary points underestimates mu. Fitting tools in Python (for example `scipy.optimize.curve_fit` with a logistic or Gompertz model) can fit the whole curve, but a log-linear fit of the exponential window is the standard first step and is easy to check by eye.

## Where it is used in bioprocessing

- **Induction timing:** many recombinant *E. coli* processes add the inducer at a set OD in mid-exponential phase.
- **Fed-batch feeding:** feed rates are set to hold a target growth rate and avoid oxygen limitation or overflow metabolism.
- **Process monitoring:** online sensors (OD probes, off-gas CO2 and O2) track growth in real time in bioreactors.
- **Comparing conditions:** media, temperature and strain changes are judged by mu and final biomass.

Mammalian cells grow much more slowly than bacteria (doubling times of roughly a day), but the same log-linear maths applies to viable cell density.

## Safety and training

Growing microbes is done only in a teaching or employer lab with appropriate containment, sterilisation and waste procedures, under supervision. The activity here uses data only.

## Common mistakes

- Fitting a straight line to raw OD instead of ln(OD).
- Including lag or stationary points in the fit.
- Reading OD above the linear range without diluting.
- Mixing log10 and natural log: if you use log10, mu = slope x ln(10).
- Reporting a doubling time in hours as minutes, or the reverse.

## Quick recap

- Batch growth: lag, exponential, stationary, death.
- In exponential phase ln(OD) is linear in time; the slope is mu.
- Doubling time td = ln 2 / mu.
- Fit only the straight section, within the instrument's linear range.

## Try it

Open the **Do** tab. A Python script fits the growth rate; you add the log transform that makes it work.


Recognise the phases of a growth curve and fit the specific growth rate and doubling time from OD data.

Microbial growth curves

Bacterial Growth Curve

Specific Growth Rate Formula: What Is Specific Growth Rate (μ)?

## Why it matters

A shake flask is fine for a few hundred millilitres. Producing a vaccine or a monoclonal antibody for thousands of patients needs bioreactors of hundreds to thousands of litres, run identically batch after batch. Technicians in upstream manufacturing set up, monitor and sample bioreactors, and must understand what each control loop is doing and why a parameter drifting out of range matters.

## The stirred-tank bioreactor

The most common design is a **stirred tank**: a vessel (stainless steel, or a **single-use** plastic bag inside a support) with:

- an **impeller** that mixes the culture;
- a **sparger** that bubbles air, oxygen, nitrogen or CO2 into the liquid;
- **probes** for temperature, pH and **dissolved oxygen (DO)**, plus often online cell density, and off-gas analysis;
- **ports** for adding base, feeds and antifoam, and for taking samples aseptically;
- a jacket or heating blanket for temperature control.

A control system reads the probes and adjusts the equipment to hold each parameter at its **set point**.

## The main control parameters

| Parameter | Typical mammalian set point | How it is controlled |
|---|---|---|
| Temperature | about 36 to 37 °C (sometimes shifted lower later in the run) | Jacket heating and cooling |
| pH | about 6.9 to 7.2 | CO2 sparging lowers pH; base addition raises it |
| Dissolved oxygen | often 30 to 50 percent of air saturation | Sparging air or O2, agitation speed |
| Agitation | set to mix well without damaging cells | Impeller speed |
| Feeds, glucose | kept in a target range | Scheduled or controlled feed additions |
| Foam | kept low | Antifoam addition |

Microbial fermentations run hotter and much faster, and need far more oxygen, so their agitation and aeration are more intense. Oxygen transfer is often the limit, described by the **kLa** (volumetric mass transfer coefficient).

## Operating modes

- **Batch:** everything is added at the start; cells grow until a nutrient runs out. Simple but low yield.
- **Fed-batch:** concentrated nutrient feeds are added during the run, extending growth and production. This is the standard for most monoclonal antibody production in CHO (Chinese hamster ovary) cells, typically over about two weeks.
- **Perfusion:** fresh medium flows in while spent medium (and often product) is removed, with cells retained by a filter. High cell densities and continuous production, but more complex.

## Scale-up

Production starts from a vial of the **working cell bank**, which is expanded through a **seed train** of progressively larger flasks and bioreactors until there are enough cells to inoculate the production bioreactor.

Scaling up is hard because not everything scales together. In a bigger tank:

- **Mixing time** gets longer, so added base or feed takes longer to disperse, and local pH or nutrient gradients form.
- **Oxygen transfer** and **CO2 removal** change: dissolved CO2 can build up in large tanks.
- **Shear** from the impeller tip and bursting bubbles can damage sensitive cells.
- The **surface-to-volume ratio** falls, so heat removal and aeration behave differently.

Engineers therefore choose which criteria to keep constant, commonly **power input per volume (P/V)**, **kLa**, **impeller tip speed** or **gas flow per volume (vvm)**, and accept changes in others. Scale-down models (small bioreactors that mimic the large one) are used to study problems without running full-size batches.

**Worked example.** A 2 L bench bioreactor runs at 0.1 vvm air (0.1 volume of gas per volume of liquid per minute), which is 0.2 L/min. Keeping vvm constant at 2,000 L means 200 L/min. Keeping superficial gas velocity constant instead would give a very different number, which is why scale-up needs a deliberate choice and verification runs.

## Process data and GMP

In GMP manufacturing, critical process parameters and their ranges are defined during development and validation. Excursions outside the ranges are deviations that must be investigated. Batch data (set points, alarms, samples, operator entries) form part of the batch record you will review in module 3. Real bioreactor operation is learned on the job, under supervision and the employer's training programme.

## Common mistakes

- Treating a DO or pH probe reading as truth without regular calibration and offline checks.
- Ignoring dissolved CO2 build-up at large scale.
- Changing agitation to fix DO without considering shear.
- Breaking sterility at a sampling port.
- Assuming a process that works at 2 L will behave the same at 2,000 L.

## Quick recap

- Stirred-tank bioreactors control temperature, pH, DO, agitation, feeds and foam.
- Batch, fed-batch (most common for mAbs) and perfusion modes.
- Seed train from the working cell bank to the production bioreactor.
- Scale-up changes mixing, oxygen and CO2 transfer, shear and heat; pick scale-up criteria deliberately.

## Try it

The **Do** tab has a quiz on control parameters and scale-up.


Explain what a bioreactor controls, the batch, fed-batch and perfusion modes, and what changes when you scale up.

Bioreactors and scale-up

How a bioreactor works

Bioprocess design and scale-up

## Why it matters

At the end of a production run the product, for example a monoclonal antibody, is a tiny fraction of what is in the bioreactor: cells, cell debris, host-cell proteins, DNA, media components, and possibly viruses. **Downstream processing** turns that mixture into a pure, safe, concentrated drug substance. It is often the most expensive part of manufacturing, and every step costs yield. Technicians run chromatography, filtration and buffer preparation, and need to know what each step is for.

## The usual order

Downstream processing usually follows this logic:

1. **Harvest and clarification:** remove cells and debris.
2. **Capture:** grab the product and concentrate it, removing most impurities in one step.
3. **Virus inactivation:** chemically or physically kill enveloped viruses.
4. **Polishing:** remove remaining trace impurities, aggregates and variants.
5. **Virus filtration:** physically remove viruses by size.
6. **Concentration and buffer exchange:** bring the product to final concentration in its formulation buffer.
7. **Sterile filtration and fill:** filter through 0.2 um filters into containers for storage or further processing.

A typical monoclonal antibody (mAb) platform looks like this:

| Step | Common technology | Removes or achieves |
|---|---|---|
| Clarification | Centrifugation and/or depth filtration | Cells, debris |
| Capture | **Protein A affinity chromatography** | Most host-cell proteins, DNA, media; concentrates the mAb |
| Virus inactivation | Low-pH hold (for example around pH 3.5 to 3.7 for a set time) | Enveloped viruses |
| Polishing | **Cation and/or anion exchange chromatography** (sometimes hydrophobic interaction) | Aggregates, residual host-cell proteins and DNA, leached Protein A, variants |
| Virus filtration | Nanofiltration (pores around 20 nm) | Viruses, including small non-enveloped ones |
| UF/DF | **Ultrafiltration/diafiltration** by tangential flow filtration (TFF) | Concentrates product; exchanges into formulation buffer |
| Final filtration | 0.2 um sterilising-grade filter | Bacteria |

Products made in bacteria differ: *E. coli* often makes proteins inside the cell, sometimes as insoluble inclusion bodies, so downstream starts with cell lysis and may include protein refolding.

## How chromatography separates

- **Affinity:** a ligand binds the product specifically (Protein A binds the Fc region of antibodies). Impurities flow through; the product is eluted, often with low pH.
- **Ion exchange:** proteins bind according to their charge at a given pH, and are eluted by increasing salt or changing pH. Depending on conditions, the product either binds while impurities flow through (bind and elute), or the impurities bind while the product flows through (flow-through mode).
- **Hydrophobic interaction and size exclusion** separate by surface hydrophobicity and size.

## Yield adds up

Each step loses some product, and losses multiply.

**Worked example.** Six steps with yields of 95, 90, 98, 92, 95 and 97 percent:

```text
overall yield = 0.95 x 0.90 x 0.98 x 0.92 x 0.95 x 0.97 = about 0.71 (71 percent)
```

Removing or improving even one step can matter a great deal at manufacturing scale. Process development balances purity against yield and cost.

## What quality checks look for

In-process and release testing measure product concentration, purity (often by size-exclusion HPLC and capillary electrophoresis), **host-cell protein** and **host-cell DNA** levels, aggregates, **endotoxin**, bioburden, and potency. Virus clearance is demonstrated by spiking studies during validation, not tested on every batch. Specifications for these tests are defined in the product's regulatory filing, guided by international guidelines such as ICH Q6B for biotechnological products (ICH quality guidelines: https://www.ich.org/page/quality-guidelines, checked on 2026-10-23).

## Common mistakes

- Thinking of purification as one step rather than orthogonal steps that each remove different impurities.
- Forgetting the cumulative effect of small yield losses.
- Treating a low-pH hold as optional when it is a validated virus-inactivation step with set time and pH.
- Confusing ultrafiltration (concentrates product) with sterile filtration (removes bacteria).
- Holding product between steps longer than validated hold times.

## Quick recap

- Clarify, capture, inactivate viruses, polish, filter viruses, UF/DF, sterile filter.
- mAb platform: Protein A capture, low-pH hold, ion exchange polishing, nanofiltration, TFF, 0.2 um filtration.
- Each step removes different impurities; yields multiply.
- Quality tests cover purity, HCP, DNA, aggregates, endotoxin and potency.

## Try it

The **Do** tab has a quiz on ordering steps and what each removes.


Order the steps that recover and purify a biological product and explain what each removes.

Downstream processing

Bioprocessing Part 2: Separation / Recovery

Bioprocessing Part 3: Purification

Fit microbial growth curves, explain how bioreactors are controlled and scaled up, and order the steps of downstream purification.