DataHow's Core Technologies

Achieving digitalization
in biopharma

DataHow’s key technologies provide actionable insights and tangible benefits for process development scientists, supporting the realization of Biopharma 4.0.

Hybrid Modeling

Combining AI with existing process knowledge

Combining your process data with hardwired knowledge

AI and machine learning (ML) are powerful, disruptive technologies. However, they require high volumes of data to be reliable — a process development challenge where datasets are scarce.

DataHow solves this by creating hybrid models that fuse a hard-coded “mechanistic” backbone, which contains known process dynamics, with machine learning models that continually learn from new process data.

These structured yet flexible models provide process scientists with unparalleled, reliable insight and prediction power. DataHow also dynamically uses hybrid models to support iterative experimental design and planning.

Key Benefits

Deep insights

Hybrid models use ML and pre-programmed bioprocess knowledge to understand complex process dynamics and patterns

Active model-based learning

Uses hybrid models to learn and support critical process development tasks and decisions iteratively

Accelerated development

Maximizes insights from each experiment and reduces experimental requirements

Transfer Learning

Leveraging existing data for new projects and scale-up

Many organizations repeat the same analyses with each new process development project. However, DataHow uses transfer learning to transfer historical data and insights between projects across an organization to give teams a head-start, allowing process scientists to accelerate learning and decrease development timelines.

Transfer learning also supports efficient scale-up as data and insights from smaller-scale processes are combined with data from initial large-scale runs to achieve higher performance.

Key Benefits

Leverage historical data on new projects

Knowledge and data from past projects support new developments

Support effective scale-up 

Transfer learnings from smaller-scale processes make rapid advancements at large scale

Accelerate learning & development

Reduce the need for extended experimental planning, saving time and resources

Digital Twins

Connecting virtual environments with physical processes

DataHow’s bioprocess models are trained on your process data to create virtual replicas, or digital twins, of your processes. Digital twins can be used offline for in silico simulations or real-time process monitoring and control when connected to live systems.

In silico simulations:
Run offline digital experiments to simulate a process’s performance under different conditions. This valuable technology allows process scientists to run “what-if” scenarios and build further process understanding and insight without wet lab experiments.

Real-time process monitoring and control:
A digital twin allows engineers to monitor and forecast the process dynamically in real time when connected to live systems. Engineers are alerted to minor process variations before they become critical, allowing them to take corrective action before it is too late.

Key Benefits

Real-time monitoring & forecasting

Understand how your process is performing and will perform

Real-time optimization

Be empowered to make adjustments before problems occur

In silico simulations

Run “what-if” process simulations to build process knowledge without costly and lengthy wet-lab work

Unlock digital bioprocessing with DataHowLab

DataHowLab is the only commercially available cloud-based bioprocess development solution that provides process scientists with a holistic and easy-to-use solution for data management, hybrid modeling, and digital twins. Learn more about how DataHowLab solves bioprocess development challenges.

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Meet with one of our bioprocess development experts for a demo of DataHowLab and see how holistic digital modeling, data analysis, and data management can transform your operation.

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DataHow’s Core Technologies

Achieving digitalization in biopharma

Hybrid Modeling

Combining AI with existing process knowledge

Combining your process data with hardwired knowledge

Key Benefits

Transfer Learning

Leveraging existing data for new projects and scale-up

Leveraging existing data for new projects and scale-up

Key Benefits

Digital Twins

Connecting virtual environments with physical processes

Connecting virtual environments with physical processes

Key Benefits

Achieving digitalization
in biopharma