Novasign Studio | Industrial Biotech · Precision Fermentation · Cultured Food
Reach viable unit economics faster with less iteration and more confidence at scale
Novasign Studio helps industrial biotech, precision fermentation, and cultured food teams identify ideal operating conditions, reduce iteration cycles, and make better scale-up decisions – from campaign history to cost-down clarity.
THE CHALLENGE
Why cost-down decisions are harder than they should be
Strong teams already know their cost targets and process goals. The challenge is getting from campaign history to clear, actionable decisions fast enough – especially when:
A uncertain number of process variables drive the unit economics, but identifying them often requires more experimentation than time allows
Without the right tools campaign data is hard to compare consistently across runs, scales, and conditions – learning exists, but it is difficult to extract
Scale-up decisions must be made before confidence is fully established – often on compressed timelines and limited budget
Internal modelling capability is limited – most industrial biotech teams are not resourced like large, big pharma, and external consultants leave no reusable capability behind
The result is iterative experimentation that is slower and more expensive than the competitive economics of this sector can sustain.
NOVASIGN STUDIO
From campaign history to cost-down decisions - one connected workflow
Novasign Studio connects experimental planning, data preparation, and hybrid digital twins in one structured workflow. Teams can compare operating scenarios, trace the variables that drive unit economics, and make faster decisions -with the logic visible at every step.
Novasign Studio introduces a structured workflow layer across your development process.
Planning → Data preparation (ETL) → Hybrid modeling / digital twin → Decision support
Instead of disconnected analyses and local scripts, your team works within one structured environment:
01
Plan
Design smarter experiments around the specific decision you need to make – from screening to model-based Design of Experiments.
02
ETL
Transform campaign data from any source, bioreactors, PAT, LIMS, historian – into reproducible, comparable workflows your whole team can work from.
03
Digital Twin
Combine process knowledge and experimental data into hybrid models that are explainable, auditable, and built for your process, not a generic template.
04
Scenario
Run virtual digital twin scenarios to test operating strategies, de-risk scale-up, and evaluate trade-offs before committing to expensive lab or engineering runs.
05
Reuse
Generate clear, explainable outputs that can be shared with colleagues, clients, applied across programs, and built on over time – not rebuilt from scratch.
USE CASES
Where teams see the fastest return
The best starting points are where cost pressure, expensive trial and error, or scale-up uncertainty are already creating friction.
Cost-down decision support
Identify the process variables – yield / productivity / media composition / feeding strategy / Gassing / Stirring – that have the largest impact on unit economics. Focus experimental effort where it changes the cost equation, not just where it is easiest to measure.
Media and feed optimization
Model-guided media development reduces the number of experiments required to identify optimal feed concentrations, carbon sources, and nutrient profiles. Time-resolved models reveal the dynamic relationship between feeding behavior, nutrient uptake, and metabolic response -capturing what endpoint-only DoE misses. For precision fermentation and cultured food companies, where media can represent 50–80% of production cost, this is often the highest-ROI starting point.
Scale-up risk reduction
Predict process performance at target scale before committing to engineering runs. Novasign integrates CFD-derived parameters -kLa, mixing time, shear rate, P/V -into hybrid bioreactor models, enabling scale-up prediction from AMBR, DASGIP, or INFORS small-scale systems to pilot or production scale. Demonstrated: Arkeon continuous fermentation process scaled from small scale closed batch to pilot with 5× titer improvement, currently under Nature submission.
Variability and comparability insight
Turn campaign history into clearer root-cause understanding. When runs diverge unexpectedly, structured preprocessing workflows and reproducible comparison pipelines help teams identify the source of variability -instead of running more experiments to rediscover what was already learned.
Continuous manufacturing support
For teams moving toward continuous fermentation or integrated upstream/downstream processes, Novasign’s connected workflow framework supports coupling of unit operations in real time. Demonstrated in the ECOnti consortium: 30-day continuous run with digital twin-controlled column switching, achieving 32% reduced operational costs, 39% lower facility footprint and 45% energy savings compared to batch processing
WHO THIS IS FOR
Who Novasign Studio supports in industrial biotech
Studio supports both day-to-day process execution and longer-term capability building – depending on where your team is starting from.
Reduce Cost of Goods for your products
Reduce iteration cycles, improve scale-up confidence, and reach viable operating points faster – with structured decision support that connects your campaign data to your cost targets.
Make complex biology easier to learn from
Reproducible data pipelines, PAT-informed analysis, and scenario testing turn your campaign history into clear next-step recommendations -without rebuilding analysis from scratch every time.
Build team ownership, not tool dependency
Transparent workflow steps, training, and decision support that your scientists and operators understand and control. When Novasign leaves, the workflow stays.
WHY NOVASIGN STUDIO
What makes Novasign Studio the right fit for industrial biotech
01
Built around unit economics, not just titer
Novasign’s time-resolved models optimize for the economically ideal process endpoint – cost per gram, not just yield per liter. This is the distinction that matters for commercial viability in precision fermentation and industrial enzyme production.
02
Explainable from input to decision
Every modelling assumption, preprocessing step, and scenario comparison is visible and auditable. Your team can understand why a recommendation was made -and challenge it if the biology doesn’t match. No black box, no dependency on Novasign to interpret results.
03
Consultancy-led start, team-owned outcomes
We begin with a guided feasibility. Novasign experts build the first workflow alongside your team, using your process data. Training and handover ensure your scientists and operators can run, adapt, and extend the workflow independently. The goal is capability transfer, not ongoing dependency.
04
Scale-up confidence before the expensive run
CFD-integrated hybrid models allow prediction of large-scale process behavior from small-scale data. Test operating windows, identify risk factors, and compare reactor geometries – before committing to pilot or production-scale runs that cost orders of magnitude more.
PROOF
Proof that the approach is practical
See how structured data, comparable runs, and explainable recommendations can help teams identify a stronger operating point and move toward lower process cost.
Arkeon
5× titer improvement and continuous scale-up
Novasign supported Arkeon’s continuous fermentation process -increasing titers 5-fold using model-based DoE and scaling from closed-batch experiments to Pilot-ScaleL. Results currently under second revision at Nature.
Bisy GmbH
50%+ timeline reduction
Bisy GmbH reduced development timelines by more than 50% in an initial proof-of-concept study, using Novasign to translate process potential into new products and processes.
Reduced Experments
Up to 70% experimental effort reduction
In selected applications across fermentation development, Novasign workflows have reduced experimental effort by 35–70% compared to conventional DoE approaches -demonstrated across E. coli, yeast, microalgae, and mammalian platforms.
Continuous manufacturing:
ECOnti A 30-day continuous manufacturing
A 30-day continuous upstream/downstream process -with digital twin-controlled chromatography and filtration – demonstrated 32% reduced operational costs, 39% lower facility footprint and 45% energy savings compared to batch processing.
Comparing Novasign with other solutions
Novasign Studio is built for teams that need a practical workflow from experimental data to modeling, simulation, and better process decisions.
Novasign VS internal scripts and local models
Move from person-dependent analyses to reusable workflows your team can review, reuse, and scale.
Novasign VS bioprocess cloud – based modeling platform
Keep flexibility in deployment, workflows, and model integration – including fully on-prem setups if desired.
Seamless integration with full white-label capabilities
Novasign Studio combines full integration and white-labeling flexibility with seamless compatibility across complex bioprocessing hardware and software workflows.
Full-Spectrum Modeling Across the Entire Bioprocess
Supports mechanistic and data-driven approaches across all unit operations, enabling full upstream-to-downstream process modeling within a single environment.
START WITH A GUIDED FEASIBILITY
Start with one cost-down decision - not a transformation program
Novasign typically begins with a scoped feasibility or pilot engagement. We build the first workflow together with your team, using your existing campaign data. You review the outcomes, decide whether to expand, and keep everything we build – the workflow, the model logic, and the recommendations.
WHAT PROCESS LOOKS LIKE
- Define one milestone decision tied to cost-down or scale-up
- Assess data suitability and predictive feasibility - free of charge
- Go/No-Go decision
- Build the first workflow with Novasign experts, inside Studio, using your data
- Review outcomes, recommended next steps, and adoption path
- Transition to internal use with training and full workflow handover
WHAT YOU RECIEVE
- Workflow built on your actual process data
- Hybrid digital twin model for the relevant process decision
- Scenario comparison tied to your unit economics
- Recommended next experiments or operating conditions
- Training and handover so your team runs it independently going forward
FAQ
Questions industrial biotech teams ask first
What kind of data do we need to get started?
The best starting point is one defined process question and relevant campaign history -even if the data is limited or inconsistently formatted. If available data is sparse, we can assess feasibility and recommend the most informative next experiments before building any model.
Is this a black box?
No. Novasign Studio is built around transparent workflow steps, explainable modelling logic, and decision support your team can review, challenge, and adapt. Every preprocessing step and model assumption is visible.
Do we need perfect data to begin?
No. The goal is not perfect data on day one - it is about structuring your existing campaign history well enough to support your optimization task or research question, and improving from there. We have worked with incomplete, inconsistent, and multi-format datasets across fermentation platforms.
Do we need an internal modelling team?
No. Studio is designed for process scientists and fermentation engineers, not data scientists. All core features are accessible through graphical, no-code interfaces. The consultancy-led engagement model means Novasign's experts build the first workflow with your team - no specialist resource required from day one.
Will our team still own the workflow after the engagement?
Yes. Training and workflow handover are built into every engagement. Scientists and operators are trained to run, adapt, and extend the workflow independently. When Novasign's involvement ends, the capability stays.
Can this handle continuous fermentation or precision fermentation platforms?
Yes. Novasign has direct experience with continuous fermentation processes, microalgae, precision fermentation, and cultured food platforms -alongside traditional industrial microbial and mammalian cell culture. The hybrid modelling framework adapts to the specific biology rather than requiring a pre-built template.
How quickly can we see results?
Initial results -data structuring, comparison, and first model outputs -are typically available within 4–8 weeks of starting a guided feasibility engagement. Operating point recommendations and scenario analysis follow within 2–3 months. Teams with established campaign datasets often move faster.
NEXT STEP
Start with one cost-down decision
Choose one decision tied to unit economics – feed strategy, harvest point, downstream optimization, or scale-up conditions – and find out whether your existing campaign data can support faster, better decisions.
No transformation program. No software commitment before value is proven. One milestone decision, one guided pilot, one clear outcome.