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Develop your process on a model. Confirm it in the lab.

Make the right process decisions faster, with up to 70% fewer experiments. Novasign Studio guides experimental design, connects your data and builds hybrid models across your entire process chain, from upstream and filtration to chromatography. Predict how changes affect titer, purity and yield before running experiments, then use traceable results to guide scale-up and support regulatory submissions, all in one platform.

For biopharma, CDMOs and industrial biotech, including precision fermentation and cultured food.

Reactor · TFF · Chromatography

Novasign Studio / Fed-Batch Study / Model and Process Simulator

From broken handovers to one seamless, flexible workflow

Every bioprocess project runs through workflows that cross tools, units and teams: from the experimental plan to a decision, from upstream to downstream, and from early development to manufacturing. Each one breaks at its handovers. Novasign Studio turns them into one workflow that you adapt to how your team works.

Planning, execution, data, modelling and decisions usually live in separate tools: a planning tool, the control system, spreadsheets or databases to aggregate and inspect the data, a modelling tool and a meeting for the decision. Each tool needs its own expertise, and every handover is done by hand.

Switch to see what changes.
Experimental planning
manual setupdesign to equipment
Experiments
copy and pastedata importers
Spreadsheets and databases
aligned by handdata curation
Models
hard to retracedigital twin
Decisions
Next experimentsplanned from scratchmodel-based DoE
  1. Conventionally, experimental planning reaches the experiments through manual setup.
  2. The results reach spreadsheets and databases by copy and paste.
  3. The data reaches the models after being aligned by hand.
  4. The decision that follows is hard to retrace.

How Novasign Studio clears the way

  • From plan to execution

    Conventionally a design is set up on the equipment by hand, sometimes with the help of DoE software. Together with device vendors, Novasign is building the link that sends a design from Novasign Studio straight to the equipment, without a manual method setup.

  • From experiments to data

    In Novasign Studio every step is a node on one canvas. Data importer nodes read files and live process data, and curation nodes merge them into one dataset. In a biosimilar project with Repligen, Novasign Studio combined bioreactor data, offline viable cell density and titer, MAVEN glucose control and ZipChip CE-MS quality data in one workflow.

  • From data to model

    Extensive preprocessing nodes filter, align and validate the data. You build a reliable ETL pipeline for your process development once, and it runs the same way for every campaign that follows.

  • From model to decision

    The model behind the digital twin simulates the scenarios you have not run in the lab and ranks them by the objectives you choose, several at once. Novasign Studio is collaborative, so the whole team opens the same model, works on it together and can retrace the decision.

  • From decision to the next experiments

    Model-based DoE proposes the next experiments from the digital twin’s simulations. Tell it how many runs the next round holds, such as 4, 8 or 12 vessels of a parallel bioreactor system, and it recommends that many. Across customer programs this has meant 35–70% fewer experiments.

Backed by customer results and peer-reviewed science

  • Bespark*bio

    Experiment reduction and optimization

    A model trained on 8 runs instead of 22 made the same decisions for optimizing a viral-vector process as one trained on the full campaign.

    64% fewer experiments, about €40,000 of raw material and 12 weeks saved.

  • Bisy

    Industrial process optimization

    Two rounds of designed experiments optimized a Komagataella phaffii process for an industrial enzyme.

    Enzymatic activity increased 2.4-fold.

  • Arkeon

    Optimization and scale-up

    A designed campaign, model-proposed conditions and validation runs, followed by a scale-up prediction.

    5.3-fold specific yield at 150 L over the initial process, and a successful scale-up.

  • Repligen

    Matching CQAs for biosimilars

    A biosimilar process was designed for a target glycan pattern, with Repligen’s process analytics integrated.

    A design space for glycoform and glycation, mapped from five experiments and a hybrid model.

  • ECONTI consortium

    E2E process

    Hybrid models of four unit operations ran inside the automation platform of a continuous E. coli process and set the chromatography switching of the next cycle.

    Intensified DoE raised the upstream titer by 60% over the original process conditions, and the process ran end to end with model-based control.

From the beginning, bespark*bio has worked closely with Novasign… This transition is powered by Novasign Studio, which we use as our exclusive modeling environment.

Matthias Müllner, CEO, Bespark*bio

Why teams choose Novasign

Novasign was founded in August 2019 as a BOKU University spin-off, based in Vienna, Austria. It makes Novasign Studio, browser-based bioprocess modelling software.

Start with an idea, and let the AI Assistant build it with you

Describe what you want to find out, and the optional AI Assistant sets up the workflow with you, from your data to a digital twin. You reach a first model faster, without knowing every node, and every suggestion stays yours to check and accept.

Novasign Studio / Fed-Batch Study / Model and Process Simulator

Why teams like working in Novasign Studio

One workflow, from the data to the model, built in the browser and shared by the whole team.

Novasign Studio / Fed-Batch Characterization / Model-Based Design of Experiments
  • Work together

    Colleagues edit the same workflow at the same time, and a node someone is working on is locked for everyone else.

  • Nothing to install

    Collaborators open the project in their browser and look at the same data, model and result.

  • Every result traceable

    Every change carries its author and time, and every training run is recorded with its data and parameters.

  • No code, or your own code

    Build a workflow by dragging nodes, or write your own Python in the same workflow.

Hybrid models learn from less data and respect the mechanistics

A hybrid model combines a given mechanistic structure (mass balances, stoichiometry) with a neural network that learns only the reaction kinetics. Because the mechanistics you know are already in the model, it has less left to learn, so the data from an affordable campaign is enough to train it, and it can often predict beyond the conditions it was trained on.

Hybrid Model for a Bioreactor
next time stepInside the Mass Balancect+1 Vt+1 = ct Vt + Σ cin Vin − ct Σ VoutProcess ParametersTemperature, pHFeedGlucoseSamplesVolume removedMass BalanceGiven · mechanisticMachine LearningLearned · from dataRates of changeKineticsGiven · mechanisticNew statesPredictedVCDTiterGlucose
  • Inside the Mass Balance. c t+1 V t+1 = c t V t + Σ c in V in − c t Σ V out
  • Process Parameters. Temperature, pH
  • Feed. Glucose
  • Samples. Volume removed
  • Mass Balance. Given · mechanistic
  • Machine Learning. Learned · from data. Rates of change
  • Kinetics. Given · mechanistic. New states
  • Predicted. VCD. Titer. Glucose

Every experiment improves the model

Each experiment adds to the model. Novasign Studio proposes the runs to perform, the scientist can adapt them, and the results are used to retrain the model. Fewer experiments are needed because the knowledge of the last campaign is carried over.

State the goal
Design the experiments
Run them
Prepare the data
Train the model
Simulate the rest

Novasign Studio proposes, the scientist adapts

The model proposes the runs to perform and shows how confident it is. Your team can adapt them before they go to the bioreactor.

8 runs instead of 22

to optimize a viral-vector process (Bespark*bio)

  1. State the goal, for example the titer, a quality attribute or the operating window the campaign has to reach.
  2. Design the experiments from what is already known.
  3. Run the experiments.
  4. Prepare the data. Alignment, units and derived quantities are nodes in the workflow, not a script on one person’s laptop.
  5. Train the model on the campaign. A person starts every training run.
  6. Simulate the runs that were not carried out and read a candidate optimum from the result. A scientist decides which experiments to run next, their results join the dataset, and the model is retrained. Later rounds often need only a confirming run rather than another campaign.

Start with one process question. See what your data can do.

The right starting point if you want concrete results. We work with you on a scoped proof of concept using your own process data, so the outcome is relevant to your process from the first step.

  1. 1

    Scoping call

    We sign an NDA with you. Then you define one process challenge and one unit operation or workflow to focus on.

  2. 2

    Data review

    Novasign reviews your existing data and defines what the proof of concept can produce, at no cost up to this point.

  3. 3

    Proof of concept

    We build and run the workflow in Novasign Studio together with you.

  4. 4

    Results

    Clear outputs, a defined scope, and your decision on how to proceed.

  5. 5

    Handover

    The project is handed over to you in Novasign Studio, so your team continues from where the proof of concept ended.

Studio runs where your data is

Novasign Studio runs on your own servers, in your own cloud account or on an appliance that Novasign builds and ships, and the on-premise and appliance options can run air-gapped. You decide where your process data is stored and what leaves your network.

On-premise
Your own data centreavailable today
Your cloud
Your own cloud accountavailable today

See how Novasign fits your process and your data

30 minutes on your own campaign data, no qualification step.