Cultured Chicken Production Cost Model

Interactive Monte Carlo TEA for Cost Projections

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November 7 (engine 2026-11-07.2): forming cost. The optional forming switch now adds $0.5–2.5 per kg of hybrid product, sourced from GFI’s 2021 extrusion-plant costs, instead of an unsourced $2–15 per kg of cells. Default results are unchanged (the switch is off).

November 7 (engine 2026-11-07.1): physical consistency, provisional. Default results change. Medium use per kg can no longer fall below what the medium’s nutrients can build (a nutrient floor); recovery losses, failed or contaminated runs (more likely in long perfusion and continuous runs) and seed-train capital are now included; and the food-grade reactor cost ratio is 0.15–0.55 of pharma-grade, down from 0.35–0.85. An oxygen-transfer cap on density is available, off by default. The default median rises from $213 to about $276/kg dry ($16.90 to $21.25 per kg of hybrid product), and the share of draws under $10/kg hybrid falls from 23.6% to 11.6%. These changes have not yet been reviewed by engineers or cell-culture experts; the ranges are placeholders with stated sources. Each can be switched off under Structural stress tests. See physical consistency.

October 26 (target and basis, documentation only): the team set the model’s target: unconditional and hypothetical cost of a large plant built with the best technology available in 2036, whether or not such plants exist, in 2026 US dollars (estimand). It also set a planned standard output basis, washed cell biomass at a fixed solids content plus cost per kg of protein (output basis). The “downstream processing” switch is relabelled “forming and texturization” (outside the estimate). No model outputs changed.

October 26 (engine 2026-10-26.2): results are also shown per kg at a target solids content and per kg of protein, using placeholder conversions you can change under Structural stress tests. A placeholder harvest, washing and dewatering cost is available under Model Structure, off by default. Default results are unchanged.

October 26 (engine 2026-10-26.3): a third Fresh-media calculation option under Structural stress tests derives medium per kg from nutrient yield (grams of cell solids per litre of medium), so it no longer falls automatically as density rises. Off by default; default results are unchanged. See the structure check.

October 29 (engine 2026-10-29.1): hybrid product for comparisons. Products will not be made entirely of cultivated cells, so the number shown beside each dry-cell-mass cost, and the probability thresholds, are now for a kg of hybrid product: 25% cultivated cells (at chicken-meat moisture) plus 75% plant-based ingredients at $3/kg. The default median is about $17/kg of hybrid product ($213/kg dry). Change the inclusion rate under Hybrid product in the sidebar.

October 26 (engine 2026-10-26.4): default results change. The headline cost is now per kg of dry cell mass (about five times the old wet figures), thresholds then used a chicken-meat equivalent (all cells; replaced by the hybrid product on October 29), density inputs are g dry/L and $/kg inputs are per kg dry. Cost basis “wet” under Structural stress tests reproduces the earlier results exactly. See Basis: dry cell mass.

This page runs the current model (engine 2026-11-07.2). See the September 2026 review and correction log. For comparisons with beliefs recorded at the May 8 workshop, use the hosted workshop-era model or its tagged source. The archived model is preserved as it was deployed and includes errors corrected here.

Two supplied research critiques prompted optional median-preserving priors, explicit growth-factor price ranges, and structural comparisons for media use, GF dosage, and maturity dependence. Baseline numerical assumptions are retained. New controls include provenance tooltips and links to the discussion responses, justification, and remaining questions. These additions are AI-implemented scenario tests, pending expert review.

September 20: the model pages now connect the technical limitations to the workshop’s skeptical–optimistic disagreement map and structured response round. This adds no new parameter values or headline forecast. It makes the top-down microbial benchmark, interaction risk, and remaining measurement needs visible, with provenance and caveats in the response note.

October 26: an opt-in Process-mode selection setting in Structural stress tests replaces the mode weights with feasibility probabilities and uses the cheapest feasible mode in each simulated world, optionally with decision noise. The default (draw mode by weights) and its results are unchanged. See least-cost mode selection for the method and caveats.

AI-generated · provenance
How Cultured Meat is Made ~11 min
The Cost Model Explained ~10 min
Critique, revisions, and open questions ~32 min

This model is largely AI-generated and, as of September 2026, has not been independently validated. It is provided to fix ideas, make assumptions inspectable, and support discussion and comparison. Do not treat the outputs as authoritative cost estimates.

Several limitations are decision-relevant, not cosmetic: the single latent-maturity factor is ad hoc; source dollars are not yet normalized to one real-dollar year; source data on different weight bases are converted with a single assumed solids content (see Basis: dry cell mass); and the parameter ranges have not yet been checked against the target quantity decided in October 2026, the cost of a large plant built with the best technology available in 2036, whether or not such plants exist (estimand). The sensitivity chart is a dollar-swing ranking, not a variance decomposition. We are addressing these incrementally.

Read the full critique and our responses → Limits/Critique

We especially welcome expert review of: growth factor quantities and prices, bioreactor CAPEX ranges, and the maturity correlation structure — see below and the Sources section.

Since October 26, 2026 (engine 2026-10-26.4), the headline cost is per kg of dry cell mass, not per kg of wet biomass. Dry-mass figures are about five times the old wet figures (the default median is about $213/kg dry, against $43 per wet kg before), because a kg of harvested cells is only about 20% solids. Nothing about the underlying scenario changed.

Why. Harvested cells are mostly water, and washing and dewatering change what a “wet kg” contains: a paste at 15% solids carries half the protein of one at 30%. Dry mass does not change with washing or dewatering, maps to nutrition, and is the natural basis for nutrient stoichiometry (how much medium it takes to build a gram of cells).

What else you’ll see. Beside every dry-basis cost, the cost per kg of a hybrid product: 25% cultivated cells, counted at the water content of chicken meat (27.5% solids, a convention agreed on 28 October 2026), and 75% plant-based ingredients at $3/kg. Products will not be made entirely of cells, and there is no consensus inclusion rate, so 25% is a convention you can change in the sidebar. The probability thresholds ($5, $10, $25, $50) and chicken comparisons use the hybrid product. Cost per kg of protein is reported too. Cell density is entered in g dry cells per litre, and $/kg inputs are per kg dry. The old wet-basis output is one switch away (Structural stress tests → Cost basis) and reproduces the earlier numbers exactly. Shared links made before this change that set density or $/kg priors will now be read as dry-basis values. Why the basis matters, and how sources report it →

AI-drafted (Claude, 26 October 2026) from David Reinstein’s design decision; not yet reviewed by a person.

From workshop evidence to model revision. The May 8, 2026 workshop is complete. Its outputs now form a linked evidence-to-model workflow: the summary records the technical arguments, the beliefs analysis shows the forecast spread, the demand bridge connects factory-gate cost to an adoption model, and the proposed modeling hack turns the remaining disagreements into testable model changes.

The follow-up now separates disagreements about definitions, factual estimates, evidence, source credibility, interpretation, model structure, and the importance of a claim to the final result. This matters because agreement that one input can become cheaper does not establish that the total system becomes cheap. The workshop synthesis explains the distinction, and the crux response form asks skeptics and optimists to diagnose it directly.

Workshop summary → · Where disagreement sits → · Respond to the crux map → · Cost → demand bridge → · Sensitivity methods →

The named beliefs-analysis dashboard remains an internal review artifact and is intentionally not linked from this public site; a redacted public synthesis should be produced separately.

Comment on specific text or a parameter with Hypothesis (click the < tab on the right edge). For longer comments, email contact@unjournal.org or open a GitHub issue.

Listen: Technical Review (22 min MP3) — Audio walkthrough of model architecture and areas for review

View ≡

Read our deep dive: How Cultured Chicken is Made — a detailed guide covering cell banking, bioreactors, media composition, growth factors, and why each step affects costs.

Quick summary: Cultured chicken is produced by growing avian muscle cells in bioreactors. The main cost drivers are media (amino acids, nutrients), growth factors (signaling proteins), bioreactors (capital equipment), and operating costs. For a side-by-side comparison of how published TEAs differ in their assumptions and estimates, see our TEA Comparison page. This model stops at a factory-gate cost; for what a given cost implies for consumer adoption and market share, see our Cost → Demand bridge, which connects to Pablo AMC’s demand-side model.

   CELL BANK  →  SEED TRAIN  →  PRODUCTION  →  HARVEST  →  PRODUCT
      [O]          [OOO]        [OOOOOOO]       [===]       [≡≡≡]

This model uses Monte Carlo simulation: we sample thousands of possible futures and see what distribution of costs emerges.

Distribution Types Used:

Parameter Type Distribution Why Examples in model
Costs, intensities Lognormal Always positive, right-skewed (some very high values possible) Media cost per liter, GF price per gram, cell density
Probabilities, fractions Beta Bounded between 0 and 1, flexible shape Maturity factor, adoption probabilities, utilization rate (0–1 share). For switching parameters (e.g., hydrolysate adoption), the model first samples an adoption probability from a Beta distribution, then uses a Bernoulli draw to determine whether each run uses the "adopted" or "non-adopted" cost regime.
Bounded ranges Uniform Equal probability within bounds; used when we have only a plausible range and no reason to favor values within it Asset life (years), CAPEX scale exponent, fixed OPEX scaling factor

Background reading. Each distribution name above links to its Wikipedia article. For a visual, non-technical introduction to distributions, see Seeing Theory: Probability Distributions (Brown University). The switching parameters also use a Bernoulli (yes/no) draw. How the model builds a lognormal from the p5/p95 (or p10/p90) values you enter is explained in Model formulas: how the distribution is constructed.

We are open to considering alternative distributional forms (e.g., triangular, PERT, or mixture distributions) and to making the modeling flexible enough for users to select or compare different distributional assumptions. Suggestions are welcome via Hypothesis annotations.

Reading the Results:

  • p5 (5th percentile): “Optimistic” - only 5% of simulations are cheaper
  • p50 (median): Middle outcome - half above, half below
  • p95 (95th percentile): “Pessimistic” - 95% of simulations cheaper

The “Maturity” Correlation:

A key feature is the latent maturity factor that links:

  • Technology adoption rates
  • Reactor costs
  • Financing costs (WACC)

In “good worlds” for cultured chicken (high maturity), multiple things improve together. This makes favorable assumptions co-occur more often. Technical success with difficult financing remains possible and should not be ruled out.

This approach draws on the concept of copula-based correlation in Monte Carlo simulation, where a shared latent variable induces dependence among otherwise independent marginal distributions. See methodological references.

More detail on the maturity factor implementation

The maturity factor is sampled once per simulation from a Beta distribution (mean set by the “Industry Maturity” slider, standard deviation 0.20). It then shifts the adoption probabilities (hydrolysates, food-grade, recombinant GFs) and adjusts WACC downward in high-maturity draws. This creates positive correlation among “good news” variables without imposing a rigid structure. The strength of the correlation is moderate by design — maturity explains some but not all of the variation in each parameter.

Caveat: This correlation structure is a modelling convenience, not an empirically calibrated design. The choice of a single latent variable, the specific sensitivity coefficients, and the functional form are all ad hoc. Results — especially in tail scenarios — may be sensitive to these choices. We encourage users to stress-test by varying the maturity slider widely and treat correlated-scenario outputs as illustrative rather than calibrated.

Interactive Model

Model Parameters

Hybrid product

Model Structure

What are these options? Why would you toggle them?
  • CAPEX: Bioreactor and facility capital costs, annualized. On by default because capital costs are a substantial portion of total production cost. You might toggle this off to isolate variable operating costs or to compare with analyses that report only VOC.
  • Plant overhead OPEX: Labor, maintenance, plant overhead. (Conventionally called “Fixed OPEX” in TEA literature, but it’s only fixed per plant — the total scales sub-linearly with plant size, so per-kg overhead falls as plants get larger.) On by default for the same reason as CAPEX. Toggle off to focus on marginal costs only.
  • Forming and texturization: Scaffolding, texturization, forming, blending and packaging for structured products. Off by default because these steps are outside the estimate, which is unstructured cell biomass. Toggle on to add the cost of blending and shaping the hybrid product: $0.5–2.5 per kg of product (5th–95th percentile), from GFI’s 2021 bottom-up costs for plant-protein extrusion plants. It is added to the hybrid-product cost only, not to the cell-mass cost. Scaffolded whole cuts (steaks, breast) would cost more and are not modeled. (Before November 7, 2026 this switch added an unsourced $2–15 per kg of cells; its URL parameter is still include_downstream.) Harvest, washing and dewatering to the standard solids basis are a different step: they belong inside the estimate but are not yet modeled explicitly. See downstream processing.
  • Harvest, washing and dewatering (placeholder): adds a placeholder $0.50–3.00/kg (5th–95th percentile) for bringing harvested cells to the standard solids basis. Off by default so published results do not change; the range is not sourced and will be replaced with values from a process model.
Most published TEAs report costs with CAPEX and plant overhead OPEX included. Toggling them off produces numbers that are not comparable to standard literature estimates.

Media Accounting

Production Model

Key Parameters

These sliders set the center of each parameter’s distribution used in the Monte Carlo simulation. They are not simple point estimates – the simulation samples around these values. For example, setting Plant Capacity to 20 kTA means the simulation draws plant sizes from a lognormal distribution centered near 20 kTA (specifically, p5=10 and p95=40). All charts and results below update dynamically as you adjust these sliders.

What is this? Where do these ranges come from? (click to expand)

Annual production capacity in thousand metric tons (kTA). Current pilots: <1 kTA. Commercial target: 10-50 kTA.

Slider range (5-100 kTA): The lower bound (5 kTA) represents a modest first-generation commercial facility. The upper bound (100 kTA) represents an optimistic large-scale facility. The default (20 kTA) matches the reference scale in Risner et al. (2021) and is a commonly used benchmark in TEA literature.

What plant size affects:

Cost Component Effect of Larger Plant
CAPEX per kg ↓ Scales sub-linearly (power law ~0.6-0.9)
Plant overhead OPEX per kg ↓ Overhead spread over more output
Variable costs per kg — No direct effect (same $/L, $/g)
Larger plants have lower per-kg costs due to economies of scale in capital and fixed costs. Variable costs (media, growth factors) don’t change with scale — they depend on technology adoption.
What is this? Fraction of capacity actually used. Accounts for downtime, cleaning, maintenance. 90% is optimistic for new industry.
What is this?

A synthetic latent variable (0 = nascent, 1 = mature) that correlates technology adoption, reactor costs, and financing. It is not calibrated to observed historical trajectories and does not encode an annual learning rate. The slider is a scenario index: it represents your judgment about how many favorable conditions co-occur, not a measured pace of progress.

Slider Rough scenario interpretation
0.1–0.2 Industry largely stalls; few process improvements reach commercial use and financing remains difficult
0.3–0.4 Some technical progress, but major input, scale-up, or financing constraints remain
0.5 Neutral coupling assumption; the model uses the base technology probabilities without claiming this matches a historical trend
0.6–0.7 Several favorable technical and supply-chain developments co-occur
0.8–0.9 Strongly favorable bundle; most modeled pathways succeed and financing conditions improve

Recent evidence should update the individual parameter distributions, not be translated into a maturity learning rate. For example, GFI’s 2026 industry report records both tighter funding and 20,000-L production progress, while the FDA’s February 2026 inventory lists five completed cultured-cell food consultations. Those signals point in different directions and should not be compressed into a single estimated trend.

To approximate “today’s conditions” (minimal technical progress): set maturity to 0.1 and target year to 2026. Note that even then the parameter ranges reflect forward projections — the model has no explicitly calibrated 2024 cost baseline.

Full explanation, calibration caveats, and stress-test guidance →

Target Year

What does the year affect?

Earlier years → lower effective maturity, less technology adoption. The model scales maturity by a factor of (0.5 + 0.5 × (year − 2024) / 20), so:

  • 2026 → maturity scaled to ~55% of slider value
  • 2036 (default) → maturity scaled to ~80% of slider value
  • 2044 → maturity at full slider value
This scaling is mechanical (linear interpolation), not fit to historical data. To approximate minimal progress from today’s state: combine year = 2026 with maturity = 0.1.

Probability of Each Process Mode (?)

What are these modes and why does it matter? (click to expand)

These weights set the probability of each bioreactor process mode being drawn per Monte Carlo simulation. Each simulated plant runs in a single mode; the weights describe uncertainty about which mode the industry ends up using, not a mix of modes inside one facility. Within each mode, cell density and media-use multiplier are sampled from mode-specific ranges — preventing physically incoherent combinations.

How to set them. The question is which mode will actually be the lowest-cost method that is technically feasible and permitted by regulators in the projection year. By default the model does not choose the cheapest mode in each simulated world; it draws the mode independently of other parameters. So a fed-batch weight of 20% says: “in 20% of worlds, something (scale-up problems, contamination risk, regulation) rules out the higher-density modes and plants fall back on fed-batch.”

Alternative: cheapest feasible mode. Under How the mode is chosen, just below the weights, you can instead give the probability that perfusion and continuous culture each work at commercial scale. The model then prices every world under each mode and uses the cheapest feasible one; fed-batch is the fallback. The selected shares become an output. With default priors fed-batch is almost never the cheapest mode in a world, and continuous is cheaper than perfusion in about 84% of worlds, so feasibility of 70% (perfusion) and 30% (continuous) gives roughly the same shares and costs as the 20/50/30 weights. The rule favors whichever mode has the fewest downsides modeled: mode-specific equipment, consumable and contamination costs are not yet included. Method and caveats.

For reference, at default settings plants drawn as fed-batch come in under $25 per wet kg in only about 2% of draws (about 4% even with optimistic maturity, hydrolysate and growth-factor settings), compared with about 24% for perfusion and 52% for continuous (wet-basis figures from before the October 26, 2026 basis change). Treat this with caution: it follows largely from our fed-batch density prior (5–30 g wet/L, 1–6 g dry/L). Humbird (2021) reaches about 110 g/L in fed-batch with a metabolically improved cell line, and in his base case fed-batch is the cheaper mode ($37/kg vs $51/kg for perfusion). The 20/50/30 default weights are not anchored to a specific source.

Mode Density (g dry/L) Same, wet (g/L at 20% solids) Media-use (×) Typical application
Fed-batch 1–6 5–30 1.0–2.0 Periodic nutrient addition; lower achievable densities
Perfusion 6–30 30–150 1.0–5.0 Continuous exchange with cell retention (hollow fiber / centrifuge)
Continuous 10–40 50–200 0.5–3.0 Near-steady-state with recycling; highest densities

The dry ranges are the earlier wet ranges converted at 20% solids; the draws are identical.

Pure batch (single fill-and-dump) is excluded — not considered commercially viable for cultured meat at scale (expert reviewer feedback, April 2026).

Note: the default mix (20% / 50% / 30%) produces somewhat higher media usage per kg than the previous unconstrained defaults, because fed-batch and perfusion at medium density use more liquid than the prior lognormal(30–200 g/L) assumption. This is more physically realistic.

In full view, an “Override process mode constraints” toggle restores manual density and media-use sliders.

Technology Adoption by

Probability that the selected hypothetical plant uses each cost-reducing technology by the projection year. These are scenario probabilities, not industry production shares.

Basal media = the bulk nutrient broth cells grow in (water + amino acids + glucose + salts + vitamins) — everything except the expensive recombinant growth factors, which we model on a separate line. Hydrolysates are cheap enzymatically-digested plant or yeast proteins; they replace the purified pharmaceutical-grade amino acids (vegan but ~10× more expensive) that dominate Humbird’s basal-media cost.

The worked figures in this note are per kg wet biomass with wet densities; per kg dry cell mass at 20% solids, multiply litres and dollars by 5 and divide densities by 5.

Note on $/L vs $/kg output: The model uses $/L as an internal parameter, but the economically meaningful unit is $/kg of cell biomass produced — which equals ($/L) × (L consumed per kg output). L per kg = 1000/density(g/L) × media_turnover_multiplier. In an illustrative scenario (60 g/L density, 1.5× multiplier), this is ~25 L/kg: a $0.70/L hydrolysate medium costs ~$17.5/kg output; a $1.25/L pharma-grade medium costs ~$31/kg. At 200 g/L perfusion with 1.0× turnover (~5 L/kg), the same formulations cost $3.5/kg and $6.25/kg respectively — illustrating the inverse-density relationship when all other inputs are fixed; both density and price have unit elasticity in this formula. The “Media” bar in the cost breakdown chart already shows this $/kg result directly.

Note on correlation with density: $/L and cell density (g/L) are not fully independent — richer media is often needed to sustain higher densities, particularly in fed-batch systems where cells deplete the available nutrient pool. Combining a very low $/L with a very high density assumption in the same scenario may therefore overstate cost savings. The model partially addresses this via process-mode-specific density ranges, but does not impose explicit correlation between $/L and density within a given mode. See Model Limits for discussion.

What is this? (click to expand)

Reminder: Basal media is the bulk nutrient broth cells grow in (water, amino acids, glucose, salts, vitamins) — everything except the expensive recombinant growth factors, which we model as a separate line.

Hydrolysates are enzymatically digested plant/yeast proteins that provide amino acids for basal media — the bulk nutrient broth that cells grow in.

Important clarification: Hydrolysates replace amino acids, NOT growth factors. These are basically separate cost categories:

Component What it provides Hydrolysate impact
Basal media Amino acids, glucose, vitamins ✅ Hydrolysates reduce cost ~70%
Growth factors FGF-2, IGF-1, TGF-β signaling proteins ↗ Indirect benefit: can reduce GF requirements by improving cell health; cannot replace GF signaling

Cost comparison:

Media Type Cost ($/L) Source
Pharma-grade amino acids $0.50 – $2.50 Revised down from $1-4 based on GFI amino acid report (2025): real supplier quotes found Humbird’s AA prices 2-10x too high
Hydrolysate-based $0.20 – $1.20 Humbird 2021: ~$2/kg amino acids; O’Neill et al. 2021

Why 75% baseline? This is a judgmental scenario default, not an empirically estimated adoption rate. Pasitka et al. (2024) demonstrated a low-cost animal-component-free medium in one chicken-cell process, while Mi et al. (2025) found that selected plant hydrolysates reduced serum use by up to 70% in a porcine-cell prototype. These are meaningful feasibility results, but they do not establish full substitution, lot-to-lot consistency, or commercial performance across cell lines and large reactors.

How pivotal is this parameter? The unresolved questions are technical, operational, and regulatory: amino-acid completeness, supplementation needs, batch variability, quality control, performance at scale, residuals, and product-specific safety review. Current US and UK guidance focuses on process controls, hazards, and evidence for the submitted product; we have not found support for the stronger claim that regulators generally require pharmaceutical-grade ingredients. Treat 75% as a scenario to stress-test, not a consensus forecast.

See Maturity Correlation for how adoption probability is adjusted.
Note: basal micronutrients are folded into media $/L (click to expand)

This slider has been removed. A previous version of the model carried a separate “food-grade micronutrients” cost line (vitamins, minerals, trace salts). That structure double-counted basal media, which the model already describes as providing “amino acids, glucose, vitamins.”

Basal micronutrients (vitamins, minerals, trace salts) are not modeled separately here. Basal media on this page already includes vitamins, and the literature suggests vitamin/mineral sourcing is relatively low-cost compared with growth factors and recombinant proteins (O’Neill et al. 2021: vitamin sourcing is a “less pressing issue” and required minerals can “generally be obtained relatively inexpensively”). We therefore include basal micronutrients inside media $/L and reserve separate uncertainty terms for the supplemental recombinant proteins (insulin, transferrin, albumin) now modeled separately — see the model change log below.

Growth factors (GFs) signal cells to proliferate — at current research-grade prices, they can dominate media costs. The slider below sets P(at least one scalable production route — e.g., autocrine cell lines, plant-based farming, or precision fermentation — reaches commercial scale by the projection year), switching between “expensive” and “cheap” GF price regimes. For background on what growth factors do and why they’re expensive, see Learn → Step 5: Growth Factors; the production routes this slider refers to are compared in Solutions being developed.

What do these sliders control? (click to expand)

Two controls for growth factors: (1) P(Scalable GF technology) sets the probability of a breakthrough reaching commercial scale (switches between “expensive” and “cheap” price regimes). (2) GF cost reduction progress sets how far prices have fallen within each regime by the projection year — 0% = current prices, 100% = industry targets achieved.

Growth factors are signaling proteins (FGF-2, IGF-1, TGF-β, etc.) that tell cells to proliferate. Currently the most expensive media component — often 55-95% of media cost at research scale.

Current vs. projected prices:

Scenario Price ($/g) Status Source
Research-grade FGF-2 $50,000 Current (2024) CEN
Research-grade TGF-β $1,000,000 Current GFI analysis
Model “expensive” $500 - $50,000 Limited progress Conservative projection
Model “cheap” $1 - $100 Breakthrough achieved Industry target
Plant-based (BioBetter) ~$1 Target FoodNavigator
What triggers the “cheap” scenario? (Key breakthroughs)

The probability slider represents whether at least one of these technology approaches succeeds at commercial scale by the projection year:

Technology Mechanism Target price Status (2025)
Autocrine cell lines Engineer cells to produce their own FGF2, eliminating external supply ~$0/g (no purchase needed) Proof of concept 2023 — Mosa Meat, Tufts collaborations
Plant molecular farming Express GFs in transgenic tobacco/lettuce $1-10/g BioBetter, ORF Genetics pilots
Precision fermentation High-density E. coli/yeast expression (like insulin) $10-100/g GFI 2024 analysis — scaling remains key challenge
Polyphenol substitution Curcumin (NaCur) activates FGF receptors, reduces FGF2 needs by 80% N/A (reduces quantity) Research 2024
Thermostable variants FGF2-G3 with 20-day half-life (vs hours) Same $/g, less usage Enantis commercial product

Why this is modeled as a binary switch + continuous progress:

  • The switch represents whether any scalable technology reaches commercial viability
  • The progress slider represents how far costs have dropped within that regime
  • Even in the “expensive” scenario (no breakthrough), incremental improvements occur
  • This structure captures the bimodal nature of the uncertainty: either scalable production is achieved, or it isn’t

Model cost impact:

Scenario Usage (g/kg) Price ($/g) Cost/kg chicken
No breakthrough (Humbird formulation × 8–60 L/kg media use) 0.001 – 0.006 $500 – $50,000 $0.5 – $300
Breakthrough achieved (≈3× usage reduction) 0.0005 – 0.002 $10 – $1,000 Endpoint products $0.005–2 (not a cost credible interval)

Where the quantity range comes from. GFI’s 2023 recombinant-protein cost analysis reproduces the Humbird medium formulation with FGF at 0.1 mg/L and TGFβ at 0.002 mg/L — about 0.102 mg/L of true growth factors combined. GFI’s efficient-future scenarios assume roughly 8–13 L media per kg meat; less-optimized processes may use up to ~60 L/kg. Multiplying through gives ≈0.0008–0.006 g true GF/kg as the defensible cited range, with Pasitka et al. (2024)’s empirical ACF chicken process landing at ~0.00187 g/kg — comfortably inside this band. An earlier version of this model used 0.0001–0.02 g/kg, which was too broad on both tails.

This is a pivotal uncertainty. The GFI 2024 State of the Industry report identifies growth factor cost reduction as one of the top technical challenges. Note that tightening the quantity range means the price regime is now doing more of the work in the tornado — the swing in cost comes primarily from the $/g uncertainty, not the g/kg uncertainty.

See Maturity Correlation for how adoption probability is adjusted.

Supplemental Recombinant Proteins (?)

About supplemental proteins and cost ranges (click to expand)

Albumin, transferrin, and insulin are media additives — proteins grouped separately here for cost accounting (insulin also has signaling functions). They are widely used in serum-free cell culture but rarely modeled explicitly in CM TEAs (usually bundled into “other VOC” or media cost).

Per GFI’s 2024 supply-chain analysis, albumin is expected to represent ~97% of anticipated recombinant protein production volume for CM. At typical cell-culture concentrations (~1 mg/mL albumin) and the model’s L/kg range, the per-kg cost contribution is:

Sourcing Albumin price Contribution to cell mass cost
Food-grade recombinant (yeast) $0.10–1/g ~$0.03–0.50/kg
Pharma-grade recombinant $5–50/g ~$0.50–4/kg
Albumin-free formulation $0/g ~$0/kg

Insulin and transferrin are used at much lower concentrations and contribute <$0.05/kg in all scenarios.

Financing

What are WACC and Asset Life? (click to expand)

WACC (Weighted Average Cost of Capital): The blended rate of return required by debt and equity investors, used to annualize capital costs via the Capital Recovery Factor (CRF). A WACC of 8% is typical for established food manufacturing; 20%+ reflects the high risk premium investors demand for an unproven technology. The simulation samples WACC from a lognormal distribution between these bounds, further adjusted by the maturity factor.

Asset Life: How many years bioreactor and facility equipment is depreciated over. These two sliders define a uniform distribution over the plausible range — not a single fixed value. The simulation draws a different asset life for each of the 30,000 Monte Carlo runs. Shorter life (8 years) means higher annual capital charges; longer life (20 years) spreads costs but assumes equipment remains productive. To set a specific value, set both sliders to the same number.

These two parameters together determine the Capital Recovery Factor (CRF).

Cell Density / Media-Use Override (?)

What is cell density and why does it matter so much? (click to expand)

Cell density (grams of dry cell mass per litre at harvest, since October 26, 2026) determines how much meat you get per liter of bioreactor volume. Under the default medium rule, higher density means less media per kilogram of product, which directly reduces the largest variable cost.

Units. Sources report density in three ways: wet weight per litre, dry weight per litre, or cell counts per millilitre times an assumed mass per cell. Dry and wet differ by the solids content (about 20% for harvested cells, so 50 g wet/L ≈ 10 g dry/L). Cell counts depend on an assumed mass per cell, which varies roughly twofold with growth rate and phase. The figures below give both.

Density (wet, as usually reported) Density (dry, at 20% solids) Media per kg wet / per kg dry Typical context
10 g/L 2 g/L ~100 / ~500 L Current lab scale
50 g/L 10 g/L ~20 / ~100 L Near-term commercial target
200 g/L 40 g/L ~5 / ~25 L Optimistic TEA projection

This is multiplicative. If media costs $1/L, going from 10 to 50 g wet/L cuts media cost from $100 to $20 per wet kg ($500 to $100 per dry kg). Going to 200 g wet/L cuts it to $5 per wet kg. Cell density is arguably the single most important technical parameter for cost reduction.

Current state: Most published data shows 10-50 g/L (wet). Some companies claim higher, but these claims are difficult to verify independently. Lever VC’s 2025 report claims 60-90 g/L has been achieved by “second generation” companies. Whether 200 g/L wet (40 g dry/L) is achievable by 2036 is a genuine open question.

What about bioreactor volume / tank size? (click to expand)

Bioreactor volume is another major uncertainty that is currently implicit in this model rather than a direct parameter.

The model computes total working volume as: total_volume = annual_output / (density × productivity × 365). It then applies a power-law scaling for CAPEX. But individual bioreactor tank size matters for several reasons:

Factor Small tanks (2,000-5,000L) Large tanks (20,000-50,000L)
Cost per liter Higher Lower (economies of scale)
Contamination risk Lower Higher (single failure = large loss)
Mixing/O2 transfer Easier Harder at scale
Flexibility More modular Less redundancy
Industry precedent Pharma standard Requires new engineering

Key debate: Some companies (e.g., Vow) claim to have built 20,000L bioreactors for under $1M in 14 weeks using custom food-grade designs. If true, this dramatically changes the CAPEX picture. Humbird’s analysis assumed pharma-grade bioreactors at $50-500/L.

Why it’s not a direct slider (yet): Adding individual tank size would require modeling the number of tanks, contamination batch-failure rates, and the trade-off between scale and reliability. This is a planned enhancement. For now, the Plant Capacity and Cell Density parameters together determine total working volume, and the custom reactor ratio (in full view) captures the pharma-vs-food-grade cost difference.

Workshop discussion: This was one of the key cruxes for the May 8, 2026 CM workshop — what bioreactor scale is realistic, and what does it cost?

Advanced: Media-use multiplier (×)

What is this — and why can it be below 1? (click to expand)

The model computes media volume per kg as (1000 / density) × multiplier. A value of 1 is traditional batch mode (fill reactor once, harvest); >1 is perfusion (multiple media-volume equivalents flow through during the run); <1 represents media recycling, fed-batch with concentrated feeds, or harvest-side cell concentration. The Learn page walks through all three mechanisms.

Range interpretation: The manual override uses p5–p95 0.5–3.0; default sampling instead uses process-specific ranges. These are lognormal percentiles, not hard bounds. Values below one need explicit medium recovery and consistently priced replacement nutrients. For example, 8 L/kg at 90 g/L implies a multiplier of 0.72, while the same medium use at 200 g/L implies 1.6. A lower multiplier is not the only way to achieve a particular L/kg value.

Show multiplier sliders

Results Summary

Quantity being modeled: manufacturing cost per kg of cultured-chicken dry cell mass, shown alongside the ingredient cost of a kg of hybrid product (25% cells at chicken-meat moisture, 75% plant-based ingredients at $3/kg; adjustable). Wet biomass is one switch away under Structural stress tests. Each figure is for a hypothetical plant/process scenario. Each draw samples one process; process weights are probabilities across scenarios, not production shares within a simulated industry. The output is not an industry-average forecast or a frontier estimate. (With cheapest-feasible mode selection on, each draw uses the lowest-cost feasible mode, a step toward a frontier estimate for the process choice only.) Commercialization probability is unmodeled; dollars retain mixed source years.

Tip: the URL in your address bar updates live as you move sliders, so the “Copy shareable link” button always reflects the scenario you’re currently viewing.

Cost Distribution

How to read this chart

The histogram shows the distribution of simulated production costs.

  • Blue bars: Frequency of each cost level across 30,000 simulations
  • Green dashed line: 5th percentile (optimistic scenario)
  • Blue solid line: Median (50th percentile)
  • Red dashed line: 95th percentile (pessimistic scenario)
  • Dark green dotted: $10/kg reference (ingredient cost only; not a price-parity line)
  • Orange dotted: $25/kg reference
  • Purple dashed: US wholesale boneless chicken, $3.50/kg (average September 2022 to September 2026 in 2026 dollars; USDA ERS wholesale prices). This is the comparison point for conventional chicken: the same stage of the supply chain as the model’s factory-gate cost, not the retail shelf price (about $9.27/kg).

On the default dry basis the reference lines are per kg of hybrid product (25% cells): $10 and $25 sit at about $113 and $331 per kg dry cell mass, and wholesale chicken at about $18 per kg dry. Because the plant-based filler alone costs $2.25 per kg of hybrid product, the cells have to cost less than about $18 per kg dry for the hybrid to match wholesale chicken. On the wet basis the purple line compares wet cells directly with chicken meat. Why wholesale, and the range →

The width of the distribution shows uncertainty. Narrow = confident. Wide = uncertain.

Probability Thresholds

These percentages answer: “What’s the chance that production costs fall below key price points?” — based on the parameter distributions you’ve set above. On the default dry basis the price points apply to a kg of hybrid product: 25% cultivated cells (counted at chicken-meat moisture, 27.5% solids) and 75% plant-based ingredients at $3/kg, both adjustable in the sidebar. Each card also gives the same price point per kg dry cell mass. These are ingredient costs; forming, packaging and margins are excluded. If you switch Structural stress tests → Cost basis to wet, the price points ($10 to $100) apply per kg of wet cell biomass instead, as they did before October 26, 2026.

Cost Breakdown

Where does the cost come from? This chart shows the mean contribution of each cost component across all 30,000 simulations (mean, not median — skewed components like growth factors will appear larger here than at the median). The largest bars are the cost drivers to focus on — these are where technological progress or parameter uncertainty has the most impact. How each component is calculated →

Understanding the cost components

Variable Operating Costs (VOC):

  • Media: Nutrient broth for cell growth (amino acids, glucose, vitamins, minerals, trace salts) — basal micronutrients are included here, not as a separate line item
  • Recombinant: Growth factors (proteins signaling cell division)
  • Other VOC: Utilities, consumables, waste disposal

Capacity-anchored Costs (per-plant, scale sub-linearly with plant size):

  • CAPEX: Capital costs (reactors, facilities) annualized via Capital Recovery Factor
  • Plant overhead OPEX: Labor, maintenance, plant overhead (conventionally called “Fixed OPEX” — total scales sub-linearly with plant size, so per-kg overhead falls as plants grow)
The relative sizes shift dramatically based on technology adoption assumptions.

Component Distributions

Show per-component distributions (click to expand)

Individual distributions for each cost driver, updating dynamically as you adjust parameters.

Reading these distributions

Each mini-histogram shows the distribution for one cost component across 30,000 simulations:

  • Solid black line: Median (p50) - the middle value
  • Dashed black lines: 5th and 95th percentiles (90% confidence interval)
  • Width of distribution: Uncertainty - wider means more uncertain
These distributions are generated from the simulation using the parameters you set in the sidebar above. When you adjust the sliders, these distributions update accordingly.
Realized adoption rates (click to expand)

Actual shares across the 30,000 draws. Near-identical to slider inputs at default maturity (0.5); deviates when maturity ≠ 0.5, because the maturity distribution shifts each draw’s adoption probability before sampling. Process mode shares always equal slider inputs exactly and are omitted here.

Sensitivity analysis (tornado chart): association under the current uncertainty model

Which parameters have the most impact on the final cost? Each bar shows the dollar swing in mean unit cost between simulations where the parameter is in its top 10% versus its bottom 10%. Larger bars indicate stronger associations under the selected assumptions. This is one of several ways to ask the question, and not the most robust one; the sensitivity methods page explains what the bars do and do not measure and compares alternatives on the same scenario.

Red bars: high values occur with higher costs. Green bars: high values occur with lower costs. Bar length = conditional-mean dollar swing between top 10% vs. bottom 10% of that parameter’s distribution across the 30,000 Monte Carlo draws. This is an association, not an intervention effect or variance decomposition. Correlated inputs overlap; bars should not be summed or interpreted as research value. Industry Maturity is a latent variable whose bar captures the bundled effect of correlated improvements. Bars of a few dollars per wet kilogram (about five times that per dry kilogram) are within simulation noise. Full methodology →

Analyze this scenario on the sensitivity methods page → Other tail widths, other output statistics, conditioning versus intervention, expected uncertainty after learning one input, rank regression, and Shapley effects.

Structural comparison

These runs keep your other inputs and seed (42) fixed. Each row changes one structural choice relative to your selected scenario. Fresh-media endpoints and GF concentration use the values in Structural stress tests. Comparisons reflect those provisional inputs as well as model form; they are not empirical validation or a variance decomposition. Overrides may make a comparison inactive; see the notes column.

Why these alternatives, and what remains unmodeled.

Industry Maturity Deep Dive

The maturity factor is a continuous variable ranging from 0 (nascent industry) to 1 (fully mature) that correlates multiple model inputs. It is not binary — intermediate values represent partial industry development. Higher maturity means:

  • Higher technology adoption rates (hydrolysates, scalable GFs, affordable supplemental proteins)
  • Lower reactor costs (more custom/food-grade equipment)
  • Lower financing costs (WACC)

Not calibrated to observed historical trajectories. The maturity slider is a forward-looking judgment parameter — it does not incorporate data on how fast CM costs have actually fallen since 2015. The model’s parameter ranges (e.g., media $/L, GF $/g) were set from TEA literature snapshots (Humbird 2021, Pasitka 2024, GFI 2025 amino acid report) — point estimates, not a fitted time series. The year → maturity scaling formula (linear from 2024 to 2044) is mechanical convenience, not a regression on industry data.

What this means: “maturity = 0.5” does not mean “the historical rate of progress continues.” It means “the model’s default assumption is that by 2036 the industry lands roughly in the middle of the plausible range.” Whether that default is consistent with observed progress since 2020 is a substantive question this model cannot answer on its own — and a good one to raise with experts at the workshop.

Correlation structure caveat (important for interpretation): The model uses a single shared latent factor to induce correlation across technology adoption, reactor costs, and financing. This is an ad hoc modelling convenience — not an empirically calibrated copula. The specific sensitivity coefficients (e.g., ±0.25 shifts to adoption probabilities, ±0.03 shifts to WACC) and the choice of a single dimension are choices made for tractability, not derived from data.

What this means for interpretation: - Scenarios where all dimensions co-move together (high maturity = everything goes well; low maturity = everything is hard) will be overrepresented relative to a fully independent model - Scenarios where, say, bioprocess technology matures rapidly but financing remains difficult are partly suppressed - Stress test: Set the maturity slider to 0.1 (pessimistic) and 0.9 (optimistic) and compare — the difference captures the full correlated scenario range. Then check whether P(cost < $25/kg) changes materially - Setting the maturity mean to 0.5 does not remove dependence: maturity still varies across draws. A true independence stress test must change dependence while preserving marginal assumptions.

Use Structural stress tests → Maturity dependence to compare shared and independent maturity channels. The explanations below describe the shared baseline. In independent-channel mode the displayed reference maturity distribution is the common marginal distribution; no single maturity draw drives the whole plant scenario.

Understanding the maturity correlation

The scatter plot shows how maturity (x-axis) relates to unit cost (y-axis), with points colored by cell density.

Key patterns:

  • Negative slope: Higher maturity → lower costs (red trend line)
  • Color gradient: Higher density (yellow) tends to cluster at lower costs
  • Spread: Even at high maturity, costs vary due to other random factors
This is an assumed dependence structure. Technical success with difficult financing, and the reverse, remain plausible scenarios.

Quick Start

  1. Explore the “Learn” sections at the top to understand how cultured chicken production works and how this model handles uncertainty.

  2. Adjust parameters in the left sidebar. Each parameter has an expandable explanation.

  3. Results update automatically as you change the Basic Parameters sliders — the summary cards, cost distribution, and probability thresholds all recalculate. (The Component Distributions section shows the underlying distributions and updates when you change the relevant sliders.)

  4. Navigate the results to see:

    • Cost Distribution: Full probability distribution with key percentiles
    • Probability Thresholds: Chances of hitting key price points
    • Cost Breakdown: Which components drive costs
    • Technology Adoption: Realized adoption rates

Pivotal Questions Context

This model is part of The Unjournal’s Pivotal Questions initiative — a project to identify and quantify uncertainties that matter most for high-impact decisions.

A pivotal question is one where resolving the uncertainty would significantly change optimal resource allocation. For cultured chicken:

  • If factory-gate costs approach US wholesale chicken (about $3.50/kg of product) by 2036: large-scale displacement of conventional chicken becomes plausible → prioritize support for industry scale-up
  • If costs remain far above it (say, over $50/kg): focus shifts to alternative interventions (plant-based, policy, etc.)

Both sides of that comparison are at the factory gate: the model’s cost and the wholesale price of boneless chicken (comparison point), not the retail price.

The pivotal question framework helps prioritize research and forecasting efforts where they matter most.

See: The Unjournal’s Pivotal Questions documentation

The cultured chicken pivotal question asks:

What will be the production cost of cultured chicken by the projection year, and what does this imply for animal welfare interventions?

Key links:

Resource Description
Pivotal Questions overview Full documentation on the PQ framework
Cultured meat on Metaculus Forecasting questions with community predictions
The Unjournal Independent evaluations of impactful research
UC Davis ACBM Calculator Original academic cost model (Risner et al.)
Good Food Institute Industry reports and data

Model formulas

Note

View Full Model Formulas page — Complete model formulas, parameter definitions, code reference, and methodology.

Quick reference:

\(\text{Unit Cost} = \text{VOC} + \text{CAPEX}_{/\text{kg}} + \text{Fixed OPEX}_{/\text{kg}} + \text{Downstream}_{/\text{kg}}\)

Jump to each term on the formulas page: Total unit cost · VOC (media, growth factors, other) · Growth factors · Annualized CAPEX · Maturity and adoption · Parameter definitions · Equations at a glance

The model runs 30,000 Monte Carlo simulations, sampling from parameter distributions and technology adoption switches to produce cost distributions and probability thresholds.

Key Insights

The versioned September audit compares the current defaults across process scenarios and multiple random seeds. Use the live results above for your selected settings. The largest remaining questions concern coherent process productivity and medium requirements, commercial-success conditioning, and evidence for the joint priors.

Model status (reviewed July 20, 2026): This model is largely AI-generated and we cannot yet vouch for all parameter values. It is provided to fix ideas, give a sense of the sort of modeling we’re interested in, and present a framework for discussion and comparison — not a definitive cost estimate. We welcome scrutiny and suggestions via Hypothesis annotations or email.

  1. Static snapshot model — Projects costs for a single projection year (adjustable 2026-2050). Does not model year-over-year learning curves; instead, the “maturity” parameter serves as a proxy for cumulative industry development.

  2. Forming and texturization are outside the estimate — Forming can be added to the hybrid product via toggle ($0.5–2.5 per kg of product, GFI 2021; scaffolded whole cuts are not modeled). Harvest, washing and dewatering belong inside the estimate but are not yet modeled explicitly.

  3. No geography — Assumes generic global costs, not region-specific labor, energy, or regulatory factors.

  4. Limited contamination modeling — No batch failure or contamination event distributions.

Sources & How They Inform the Model

This model was largely AI-generated. We have grounded key parameter ranges in published TEAs and recently revised the pharma-grade media cost range downward (from $1-4/L to $0.50-2.50/L) based on GFI’s 2025 amino acid supplier data. However, we have not yet systematically verified that every hardcoded range traces to a specific source. Cycle days is the main remaining parameter that still lacks a direct citation; growth factor quantities and the media-use multiplier were both tightened in April 2026 based on GFI 2023 / Humbird / Pasitka primary sources. This is one reason we are seeking expert review.

Earlier versions of this model carried a separate food-grade micronutrient cost line (vitamins, minerals, trace elements) with its own adoption toggle, usage range (0.1–10 g/kg), and price range ($0.02–20/g). External review flagged two problems:

  1. Category confusion / double counting. The page already describes basal media as containing “amino acids, glucose, vitamins,” and the model comments described media as “basal media, excluding growth factors.” A separate line for vitamins/minerals was therefore adding a second charge for the same inputs.
  2. Ranges not coherent with the literature. O’Neill et al. (2021) describe vitamin sourcing as a “less pressing issue” and note required minerals can “generally be obtained relatively inexpensively.” Meanwhile Pasitka et al. (2024) report an ACF-medium supplement (vitamins + minerals) at roughly 0.151 kg per kg wet biomass — orders of magnitude above the slider’s 0.1–2 g/kg range. So the old slider was not tracking any coherent category.

What changed: The separate slider and cost line were removed. Basal vitamins, minerals, and trace salts are now included inside media $/L along with amino acids and glucose, consistent with how the media-cost literature is reported. Supplemental recombinant proteins (insulin, transferrin, albumin) remain unmodeled as a standalone line for now; if we reintroduce a separate uncertainty term, it will be repurposed to these proteins where per-g prices and per-kg usage can be sourced.

Sources that directly informed model structure and parameter ranges (click to expand)
Source What it informed in the model How
Risner et al. (2021) Reference plant scale (20 kTA), reactor cost structure Default plant capacity; CAPEX scaling reference
Humbird (2021) Pharma-grade media costs ($1-4/L), reactor costs ($50-500/L), scale exponents (0.6-0.9) Directly used for pharma-grade parameter ranges in simulate()
O’Neill et al. (2021) Serum-free media cost benchmarks Informed hydrolysate media cost range ($0.20-1.20/L)
GFI amino acid report (2025) Pharma-grade media cost revised downward ($1-4/L → $0.50-2.50/L) Real supplier quotes found Humbird AA prices 2-10x too high; since AA dominates basal media cost, this lowers the pharma range
GFI recombinant-protein cost analysis (2023) Growth-factor quantity range (tightened April 2026) Reproduces Humbird formulation (FGF 0.1 mg/L, TGFβ 0.002 mg/L) and cost-competitive media-use assumption (8–13 L/kg); drives the revised 0.0005–0.006 g/kg quantity range
GFI State of Industry (2024) Growth factor cost targets, technology status Informed GF price regime bounds and adoption probability defaults
Sources cited for context but not directly integrated into parameter values (click to expand)
Source Relevance
Pasitka et al. (2024) Optimistic TEA ($6/lb); also provides an empirical ACF chicken process figure of ~0.00187 g growth factor/kg wet biomass, which is used as a cross-check for the GFI-derived quantity range (April 2026)
Goodwin et al. (2024) Scoping review comparing TEAs — informed our understanding of where TEAs disagree
CE Delft (2021) European cost benchmarks — cited in documentation but not directly used for parameter ranges
Lever VC (2025) Industry claims on cell density (60-90 g/L) — cited as context for density range upper bound
The Unjournal evaluations Independent evaluation of RP forecasting paper — informed our project framing
Parameters where source grounding is weakest — review priority (click to expand)
  • Supplemental recombinant proteins (insulin, transferrin, albumin) are now broken out as a separate cost line in separable-media mode. Their ranges remain provisional and need supplier quotes or process-linked dosage evidence. In bundled complete-medium mode they are included in media $/L and are not added again.
  • Growth factor quantities — tightened April 2026 from a very broad 0.0001–0.02 g/kg stress-test range to a cited 0.0005–0.006 g/kg default, derived from the Humbird medium formulation reproduced in GFI 2023 (FGF 0.1 mg/L + TGFβ 0.002 mg/L) × media-use assumptions of 8–60 L/kg, and cross-checked against Pasitka et al. (2024)’s ~0.00187 g/kg empirical ACF figure. Both dosage and price remain uncertain; the current per-kg dosage draw is not coupled to realized medium use.
  • Media-use multiplier — renamed and tightened April 2026 from “Media turnover (1–10×)” to “Media-use multiplier (0.5–3.0×)”. The rename flags that the parameter is a bundled ratio (fresh media / nominal reactor volume), not a literal count of reactor-volume changes, so values below 1 (recycling, fed-batch, explicit medium recovery) are now allowed. The new range lets the model represent GFI 2023 cost-competitive scenarios (8–13 L/kg ≈ 0.5–1.2× at 60–90 g/L); the old floor of 1.0 excluded some combinations at fixed density. See Learn: media-use mechanisms for the derivation.
  • Cycle days (0.5-5.0 days) — hardcoded without source attribution
  • Plant factor (1.5-3.5×) — standard engineering multiplier range, not CM-specific
See the Technical Documentation for detailed parameter definitions and the expandable details under each slider for parameter-specific discussion.