Culture development
Compare growth, viability, feed, nutrients and product accumulation across clones and culture conditions.
← All modalities
CHO · MAB
Monoclonal antibodies
Bring cell-culture records, process conditions, downstream recovery and analytical outcomes into one scientific context. Compare runs, investigate changes and evaluate the next process scenario using the evidence that supports it.
MODALITY-SPECIFIC PROCESS INTELLIGENCE
Connect culture performance to final product quality.
- 01Cell culture
- 02Harvest
- 03Purification
- 04Quality
Downstream recovery
Follow harvest, purification stages and chromatography evidence to understand where product or quality changes.
Scale and transfer
Assess mixing, oxygen demand, equipment differences and operating windows with process-specific models.
Quality investigation
Connect process events and batch comparisons to product-quality assays and evidence-linked hypotheses.
EVIDENCE · MODELS · DECISIONS
Keep every conclusion connected.
Evidence in Bio Hub
- Cell growth and viability
- Feed, metabolites and reactor conditions
- Chromatography, recovery and quality assays
Models in Bio Twin
- Culture kinetics and hybrid prediction
- Feed and operating-window comparisons
- Scale-dependent process assessment
Scientific decisions
- Compare media and feed strategies
- Investigate titer or quality shifts
- Assess transfer and scale-up scenarios
Process outcomes to evaluate: Titer · recovery · product quality
EXAMPLE INVESTIGATION
Why did titer decline while the nominal setpoints stayed the same?
Connect evidence
Connect campaign runs with feed, material lots, metabolites, viability and reactor events.
Compare context
Compare the affected cohort with representative historical runs and aligned process phases.
Evaluate explanations
Evaluate plausible growth, nutrient and transport explanations; use calibrated model scenarios where appropriate.
Review next steps
Prepare a traceable comparison and recommend the experiment or measurement that would distinguish the hypotheses.
Bio Agents can coordinate the scientific work across evidence and models, while Bio OS preserves governance and human review. The configured workflow determines which tasks and models are available.
Bring us this process challenge ↗EVALUATION & SCOPE
Make the demonstration specific.
Evaluate model fitness against independent runs, the intended operating range and the specific output. A model of titer does not automatically validate predictions of glycosylation or other quality attributes.
What evidence should we bring?
Cell growth and viability. Feed, metabolites and reactor conditions. Chromatography, recovery and quality assays. Include the process question, relevant run context and the analytical methods used to assess the outcome.
How do models support the workflow?
Culture kinetics and hybrid prediction. Feed and operating-window comparisons. Scale-dependent process assessment. Scope and evaluation criteria are defined for your process, data and intended decision.
What should we validate before using the results?
Evaluate model fitness against independent runs, the intended operating range and the specific output. A model of titer does not automatically validate predictions of glycosylation or other quality attributes.
EXPLORE OTHER MODALITIES