In Silico Simulations. Actionable Insights.
Cell Insights by Umetrics® Studio takes your experiments further by expanding upon information from in vitro experiments with in silico simulations. This provides actionable insights on optimizing the cell culture performance with streamlined, ready-to-use workflows accessible to more diverse user experience levels. It delivers intelligent cloud deployment options and self-service data analytics to reduce upstream process development timelines and costs with minimal cost-of-ownership.
On average, labs can spend up to $60,000 per month on cell culture development experiments*. By adding in silico you can run twice as many projects with your existing set-up, doubling your PD output.
*Assuming 48 bioreactor runs using 16 SU reactors
Cutting your timelines in half allows you to be that first-to-market player that enjoys a bigger market share even 10 years later – 6% higher than any rival**.
**Source: EvaluatePharma
Ensure optimal cell culture performance, regardless of the cell-based modality and independent of the bioreactor brand and model.
Enhance decision-making by coupling in vitro cell culture experiments with machine learning and mechanistic knowledge of cell behavior and pathways.
Pre-developed simulation workflows with easy-to-use interfaces enables access to advanced modeling beyond data scientist expertise levels.
Running digital operations in a cloud-native environment minimizes IT support by enabling fast and easy maintenance, scale-out and upgrades. Cell Insights by Umetrics® Studio is offered as a hosted cloud (SaaS) solution.
Cell Insights by Umetrics® Studio Use Cases
Cell Insights by Umetrics® Studio is an advanced data analytics application that uses hybrid models to generate bioreactor simulations. It supports a variety of cell-culture development use cases:
Choose the best cell line for intensified processes
Quickly forecast performance in fed-batch, perfusion and N-1 cell culture methods to choose the best cell line without having to perform multiple in vitro experiments.
Select optimal cell culture process parameters
Save time by using in silico simulations to understand how process parameter adjustments (e.g. feed additions, setpoint changes) impact cell growth, metabolism, accumulation of biomaterials, and productivity.
Optimize intensified feeding protocols
Rely on in silico simulations to know how media exchange rates impact cell growth to quickly design optimal intensified feeding protocols.
Set up your seed train configuration
Minimize experiments to learn how seed duration, number of passages, and culture methods impact cell growth to design optimal seed train configurations.
How to Optimize Cell Lines to Realize the Promise of PI
In silico simulations can reduce the development time of early-stage intensified upstream processes. In this webinar, Sartorius introduces an advanced data analytics application that minimizes experimental efforts.
FAQs
In silico modeling is a method of using computer simulations to study complex systems, such as cells, in a virtual environment.
Mechanistic modeling uses the fundamental laws of natural sciences to predict what will happen in the real world. Cell Insights® by Umetrics Studio utilizes mechanistic knowledge about kinetic cellular behavior to generate accurate predictions about cell growth, death, and productivity.
Yes, Cell Insights by Umetrics® Studio can be used to model a variety of cell-based processes so long as it follows Monod growth kinetics.
Monod kinetics refers to the mathematical model that is used to study cell growth and metabolism in silico. The Monod kinetics model describes the relationship between the growth rate of a microorganism population and the concentration of the limiting nutrient in the environment.
µ is the growth rate of a considered microorganism
µmax is the maximum specific growth rate coefficient
[S] is the substrate concentration
KS is the concentration where µ = ½ µmax
To perform basic simulations, at a minimum, the following data is needed:
- One dataset from a screening experimental design, containing sufficient variations in growth
- Data should show batch evolution (data over time)
- In .csv or Excel format
Required variables: Batch ID, time, viable cell density, viability, volume
Optional variables: Process inputs (e.g. DO, pH, temp), metabolites (e.g. glucose, lactate, glutamate), titer, feed, media composition
Using larger datasets will unlock more advanced simulations and is needed to adapt the models to other mammalian cell lines.
An advanced data analytics ecosystem is a single touchpoint for data analytics and management. The ecosystems contain all the necessary tools to ingest, prepare, and discover from data.
Cloud-native technologies empower organizations to build and run scalable tools in private, public, or hybrid cloud environments.
Software as a service (or SaaS) is a software distribution model in which a cloud provider hosts applications and makes them available to end users over the internet.
