Clonalyzer 2

From raw CSV to publication-ready kinetics — in seconds, entirely in your browser

→ Open the live app

---

What is Clonalyzer 2?

Clonalyzer 2 is a browser-based kinetics analysis tool for CHO fed-batch cultures. Drop a raw measurement CSV and it computes over 20 kinetic parameters — specific rates, metabolic yields, integral cell counts — then renders interactive charts and packages everything into a downloadable ZIP or a PDF report ready to paste into your lab notebook.

It runs entirely in the browser through Pyodide, the full scientific Python stack compiled to WebAssembly. No installation. No scripts. No data ever leaves your machine.

---

Why it matters

After each fed-batch run, extracting kinetic parameters from viability counter and metabolite analyzer data typically means hours of manual spreadsheet work — repeated for every clone and replicate. Without a dedicated tool:

• Specific rates must be calculated interval by interval from raw concentration and volume data
• Feed events require manual correction to avoid confounding dilution with cellular activity
• Clone comparisons and replicate statistics are assembled by hand from separate files

Clonalyzer 2 automates the entire pipeline in a single CSV drop. What used to take an afternoon now takes under a minute.

---

How it works

Four steps. No command line. No configuration files.

Step 1 — Upload your data

Drag your CSV onto the drop zone or click to browse. The app accepts European decimal commas and automatically detects the column layout.

Step 2 — Set the exponential phase window

Define the start and end time (h) of the exponential growth phase. Specific rates (µ, q values) are summarized separately for the exponential and stationary phases. Default: 0–96 h.

Step 3 — Choose your culture mode

Select Lote (batch) for concentration-based calculations, or Lote alimentado (fed-batch) to enable the hybrid mass-balance approach that automatically switches from concentration-based to mass-balance at the first feed event.

Step 4 — Analyze and explore

Click Analyze data. In seconds you get four families of interactive charts — scatter, mean ± SD, exponential-phase bar plots, and pairwise correlations. Download everything as a ZIP or generate a compact PDF report for your lab notebook.

---

Methods

Clonalyzer 2 computes specific rates interval by interval between consecutive timepoints using a trapezoidal approximation. Two scenarios are supported depending on whether culture volume data are available.

Specific growth rate

μ is always calculated from viable cell density, regardless of mode:

$$\mu = \frac{\ln(VCD_2 / VCD_1)}{\Delta t} \quad \text{[h}^{-1}\text{]}$$

Using total cell counts (TC = VCD × V) for μ would produce artefactual negative values whenever volume drops during sampling. VCD is the correct basis.

---

Lote mode — concentration-based (default)

All intervals use the Integral Viable Cell Density (IVCD) as normalizer. No volume data are needed.

$$\Delta IVCD = \frac{VCD_1 + VCD_2}{2} \cdot \Delta t \quad \left[\frac{\text{cells} \cdot \text{h}}{\text{mL}}\right]$$

$$q_i = \frac{\Delta C_i}{\Delta IVCD} \quad \text{[pg or pmol / cell / day]}$$

Parameter	Numerator	Sign convention
qGlc	$Glc_1 - Glc_2$	+ consumed, reported as pmol/cell/day
qLac	$Lac_2 - Lac_1$	+ produced, reported as pmol/cell/day
qP	$rP_2 - rP_1$	+ produced
qGln	$Gln_1 - Gln_2$	+ consumed, reported as pmol/cell/day
qGlu	$Glu_2 - Glu_1$	+ produced, reported as pmol/cell/day

Metabolic yields are the ratio of concentration changes:

$$Y_{Lac/Glc} = \frac{Lac_2 - Lac_1}{Glc_1 - Glc_2} \quad Y_{Glu/Gln} = \frac{Glu_2 - Glu_1}{Gln_1 - Gln_2}$$

---

Lote alimentado mode — hybrid (concentration → mass balance)

Requires Vol_mL and is_post_feed. The engine applies a two-phase strategy per Clone × Replicate, detected automatically from is_post_feed:

Phase 1 — batch phase (before first feed event)

Before the first row where is_post_feed = TRUE, the culture has not yet received any feed addition. Volume only decreases due to sampling, so concentration changes are a faithful proxy for cellular activity. Concentration-based calculations are used — identical to Lote mode.

Phase 2 — fed-batch phase (from first feed event onwards)

Once feeding begins, adding a bolus of concentrated medium dilutes metabolites and increases the working volume. A raw concentration drop in glucose could reflect cellular consumption, feed dilution, or both. To isolate the biological signal, all calculations switch to total masses and total cell counts inside the reactor.

Total viable cells and ITVC:

$$TC = VCD \times V \quad \text{[cells]}$$

$$\Delta ITVC = \frac{TC_1 + TC_2}{2} \cdot \Delta t \quad \text{[cells} \cdot \text{h]}$$

Total metabolite mass and specific rates:

$$M_i = C_i \times \frac{V}{1000} \quad \text{[g or mmol]}$$

$$q_i = \frac{\Delta M_i}{\Delta ITVC} \quad \text{[pg or pmol / cell / day]}$$

Parameter	Numerator	Sign convention
qGlc	$M_{Glc,1} - M_{Glc,2}$	+ consumed, reported as pmol/cell/day
qLac	$M_{Lac,2} - M_{Lac,1}$	+ produced, reported as pmol/cell/day
qP	$M_{rP,2} - M_{rP,1}$	+ produced
qGln	$M_{Gln,1} - M_{Gln,2}$	+ consumed, reported as pmol/cell/day
qGlu	$M_{Glu,2} - M_{Glu,1}$	+ produced, reported as pmol/cell/day

Metabolic yields use mass changes in the same way:

$$Y_{Lac/Glc} = \frac{M_{Lac,2} - M_{Lac,1}}{M_{Glc,1} - M_{Glc,2}} \quad Y_{Glu/Gln} = \frac{M_{Glu,2} - M_{Glu,1}}{M_{Gln,1} - M_{Gln,2}}$$

Feed events are always excluded: intervals where is_post_feed transitions FALSE → TRUE are skipped entirely. The apparent concentration change at those points reflects medium addition, not cellular metabolism.

---

Features

Zero installation	Runs fully client-side via Pyodide — no Python, no pip, no server
20+ kinetic parameters	µ, qGlc, qLac, qP, qGln, qGlu, yields, IVCD/ITVC, fluorescence kinetics
Batch & fed-batch modes	Concentration-based (Lote) or hybrid mass-balance (Lote alimentado) with automatic phase detection
4 interactive plot families	Scatter · Mean ± SD · Exponential phase bars · Correlations
Configurable phase window	Set exponential phase start and end independently
Custom correlations	Build any pairwise plot on demand, segmented by clone and phase
PDF reports	Full report (A4 landscape, one chart per page) or compact report (Letter portrait, 8 charts per page) with the Clonalyzer logo — ready to paste into a lab notebook
ZIP download	All CSVs and PNGs packaged in one click
No data upload	Everything runs locally in your browser — your experimental data never leaves your machine

---

Input format

Required columns

Column	Description	Units
t_hr	Culture time	h
Clone	Clone identifier	—
Rep	Biological replicate number	—
is_post_feed	TRUE if sampled after a feed event	boolean
VCD	Viable cell density	cells/mL
DCD	Dead cell density	cells/mL
Viab_pct	Viability	%
rP_mg_L	Recombinant protein titer	mg/L
Glc_g_L	Glucose	g/L
Lac_g_L	Lactate	g/L
Gln_mM	Glutamine	mM
Glu_mM	Glutamate	mM

Two CSV layouts are accepted:

With metadata row (default — keep "Skip first row" checked)

Culture time (h),  Clone ID,  Biological replicate, ...   ← ignored
t_hr,              Clone,     Rep,                  ...   ← column names
0,                 Control,   1,                    ...   ← data

Without metadata row (uncheck "Skip first row")

t_hr,  Clone,  Rep,  ...   ← column names
0,     Control,  1,  ...   ← data

European decimal commas (1,5) are accepted and converted automatically.

Optional columns

If present, the corresponding features are enabled automatically.

Column	Description
Vol_mL	Culture volume (mL) — required for Lote alimentado mass-balance mode
GFP_mean / GFP_std	GFP fluorescence intensity (A.U.)
TMRM_mean / TMRM_std	TMRM fluorescence intensity (A.U.)
Bodipy_mean / Bodipy_std	BODIPY fluorescence intensity (A.U.)
CellROX_mean / CellROX_std	CellROX fluorescence intensity (A.U.)

---

Tech stack

Analysis (in-browser via WebAssembly)

Frontend

Packaging & export

---

Project structure

Clonalyzer-2/
├── index.html          ← markup and UI
├── style.css           ← Material Design 3 custom styles
├── app.js              ← Pyodide init, UI logic, ZIP/PDF generation
├── clonalyzer.py       ← analysis pipeline (runs in-browser via Pyodide)
└── Fig/                ← screenshot assets for this README

---

Author

Emiliano Balderas Ramírez Bioengineer · PhD Candidate in Biochemical Sciences Instituto de Biotecnología (IBt), UNAM

---

Related

CellSplit — Neubauer cell counting and passage planning for CHO cultures.

CellBlock — shared biosafety cabinet scheduling for cell culture research groups.

---

Clonalyzer 2 — drop a CSV, get your kinetics.