Novasign Studio for industrial biotech
Start with one cost question. Build a workflow. Make every run count. Lower your production cost to a competitive level, faster.
The overhead that keeps industrial biotech teams from moving faster on cost
The challenge is rarely a shortage of data, campaign history or technical expertise. It is that the production cost target is set externally, by a chemical synthesis route, an extraction process, or an established bio-based producer that has had years or decades to optimize. Closing that gap takes faster and better-justified process decisions than most teams are structured to make.
The cost benchmark is fixed by a competing technology
The production cost your process has to reach is not set by your development timeline. It is set by whatever already supplies the market: chemical synthesis, extraction from natural sources, animal-derived production, or a fermentation process that has run at industrial scale for years. Matching or undercutting that benchmark takes a systematic way to evaluate, optimize and justify process decisions, and to translate an innovative biological approach into a production cost that competes.
Process economics only close at scale, and scale-up carries real uncertainty
The cost targets that make a bio-based product viable require pilot or commercial-scale operation. How oxygen transfer, substrate gradients, mixing and shear change with scale can be described by engineering correlations, but what those physical changes mean for cell metabolism, productivity and product quality is much harder to predict. That gap is where scale-up risk lives, and it usually becomes visible only when the engineering run underperforms.
The cost-critical variables are hard to identify from campaign history alone
Energy, raw materials, media composition, aeration strategy and feeding profile all contribute to production cost differently by product and by scale. Teams naturally focus experimental effort on what they believe drives cost at the scale they currently operate, which may not be where the largest leverage sits at pilot or production scale. Without structured data processing and process modelling, the variables that genuinely drive cost of goods stay hard to separate from noise, and the same questions get answered repeatedly instead of built upon.
Process knowledge and modelling capability exist but do not reach the whole team
Where models have been built they typically live with one person and need specialist support to run. Everyone else decides on experience and prior campaign outcomes, with little ability to test the cost impact of a process change before committing to a run. External consultants deliver useful outputs, but the models built during an engagement rarely transfer into something the team can run, adapt and extend on its own.
The result is iterative experimentation that is slower and more expensive than the competitive economics of this sector can sustain.
Whether you are competing with chemical synthesis, plant extraction, animal-derived production or an established fermentation process, commercial success depends on delivering the right quality at a competitive cost, and on sustaining that performance at industrial scale.
Novasign Studio helps industrial biotech, precision fermentation and cultured food teams turn process data and knowledge into better development and scale-up decisions. Shorten development cycles, identify the variables that drive production costs and predict how your process will perform at scale, so you can focus your experiments and investment where they have the greatest impact on commercial viability.
One connected workflow, from campaign data to production cost decisions
Novasign Studio introduces a structured workflow layer across your development process and gives each step a structured home, open to extension where you need it. It does not replace your existing models, scripts or data infrastructure, it makes them reusable, reproducible and shareable across your organization. Each step is designed to fit how process teams already work rather than to require a new way of working.
1 · Plan
Design smarter experiments around the specific decision you need to make, from screening and process optimization with model-based design of experiments to intensified DoE for process characterization.
Where Novasign Studio fits in industrial biotech
From early-stage feasibility through pilot and commercial scale-up, across fermentation platforms, host organisms and downstream unit operations.
Production cost decision support
Once a model is established, the focus shifts to which process variables have the largest leverage on production economics. Yield, productivity, media composition, feeding strategy, aeration and agitation each contribute differently by product and scale. Novasign connects process conditions directly to cost-of-goods outcomes, so experimental effort goes to the runs that move the cost equation rather than the ones that are convenient to execute.
How hybrid modelling worksMedia and feed optimization
Model-guided media development reduces the number of experiments needed to identify optimal feed concentrations, carbon sources and nutrient profiles. Time-resolved models show the dynamic relationship between feeding behaviour, nutrient uptake and metabolic response, which is what endpoint-only DoE misses. For precision fermentation and cultured food teams in early development, where defined media with expensive components can dominate production cost, this is often the highest-return starting point.
Scale-up risk reduction
Scenario-based simulation lets the model predict performance at pilot or production scale before the engineering runs happen, so those runs validate expectations rather than explore them. Where bioreactor geometry matters, CFD-derived parameters such as kLa, mixing time, shear rate and power per volume (P/V), from Novasign’s technology partner SimVantage, are integrated directly into the process model, which puts a quantified basis under the prediction and captures how physical changes at scale translate into biological response. Direct import from small-scale systems works out of the box, including the Sartorius Ambr 15 and Ambr 250 and Eppendorf systems, so the path from small-scale screening to a scale-up model is as short as possible.
How Arkeon scaled up from 2 L to 150 LProcess knowledge that reaches the whole team
Process knowledge built for one product or campaign does not have to stay with the person who developed it. Novasign Studio captures the data processing steps, the model structure and the decision logic behind a result in a reusable workflow. The next campaign starts from an established baseline, and the capability belongs to every team member rather than to the original author.
Continuous manufacturing support
For teams moving toward continuous fermentation or integrated upstream and downstream processes, the connected workflow supports coupling of unit operations. In the ECONTI research consortium, Novasign’s models predicted and the automation platform decided and acted, over continuous operation of up to 30 days. Genetic Engineering & Biotechnology News reported the run as 30 consecutive days of full process control.
The ECONTI study
Does this sound like your team?
- For heads of fermentationYou know the cost you have to hit, but not which lever gets you there.The target is not negotiable, because it is set by whoever already supplies the market. What is unclear is which part of the process moves you toward it: media, feed strategy, aeration, titer, or the scale you run at. Novasign Studio connects process conditions to the individual cost contributors, so the next experiment is aimed at the variable that actually moves the economics rather than the one that is easiest to test.
Who Novasign Studio supports
Studio supports day-to-day process execution and longer-term capability building, depending on where your team starts from.
Reduce cost of goods for your products
Fewer iteration cycles, better scale-up confidence, and viable operating points reached faster, with structured decision support connecting campaign data directly to your cost targets.
Turn complex process data into clear recommendations
Reproducible data pipelines, PAT-informed analysis and scenario testing turn campaign history into clear next-step recommendations, without rebuilding the analysis every time.
Build team ownership rather than tool dependency
Transparent workflow steps, training and decision support your scientists and operators understand and control. When Novasign leaves, the workflow stays.
Why industrial biotech teams choose Novasign Studio
Titer matters, production cost is what wins
In industrial biotech the process that survives is the one with the lowest cost per gram at scale. Novasign’s models connect process conditions to the individual cost contributors, media and substrate, energy, aeration and agitation, and make each contribution visible by source, so experimental effort goes to the variables that actually move the economics.
Explainable from input to decision
Every modelling assumption, preprocessing step and scenario comparison is visible and traceable. Your team can see why a recommendation was made and challenge it if the biology does not match. No black box, and no dependency on Novasign to interpret a result.
A consultancy-led start with team-owned outcomes
We begin with a guided feasibility. Novasign experts build the first workflow alongside your team on your process data, and training and handover make sure your scientists and operators can run, adapt and extend it. The goal is capability transfer rather than ongoing dependency.
Scale-up confidence before the expensive run
CFD-integrated hybrid models, with the fluid dynamics from Novasign’s technology partner SimVantage, predict large-scale behaviour from small-scale data. Test operating windows, identify risk factors, and compare reactor geometries before committing to runs that cost orders of magnitude more.
How Novasign Studio compares
Novasign Studio is built for teams that need a practical, end-to-end workflow, from experimental data through modelling and simulation to better process decisions.
Against internal scripts and local models
Move from person-dependent analyses to reusable, structured workflows your whole team can review, apply and build on.
Against cloud-based bioprocess modelling platforms
Keep full flexibility in deployment, workflows and model integration, including fully on-premises setups where data governance requires it.
Against standalone solutions
Use Novasign Studio as an integrated workflow platform.
Against pure mechanistic or pure ML modelling tools
Hybrid models are supported across multiple unit operations: the mechanistic structure you give, and the kinetics the network learns from the data you have. Connect unit operations so a change propagates through the whole workflow.
Relevant case studies
- 5.3×specific yield at 150 L, and continuous scale-upArkeon
Novasign supported Arkeon’s continuous fermentation process, raising the specific yield 5.3-fold at 150 L over Arkeon’s initial process through model-based DoE, with a successful scale-up transfer prediction for the same process.
- 2.4×enzymatic activity of the starting processBisy GmbH
Bisy GmbH increased the enzymatic activity of a Komagataella phaffii process for an industrial enzyme 2.4-fold.
Questions industrial biotech teams ask first
What you need to get started, how many experiments it takes, which hosts and process types are supported, whether to begin with a proof of concept or go straight to a license, and how deployment, IP and data ownership work, are all answered in the frequently asked questions.
Build digital capability, one workflow at a time
Choose one decision tied to your production economics: feed strategy, harvest point, downstream optimization, or scale-up conditions. Then find out whether your existing campaign data can support faster, better decisions. No transformation program, and no software commitment before the value is proven. One decision, one guided proof of concept, one clear outcome.