BioMedAna TWIN · BIOPROCESS PREDICTION AND SIMULATION
BioMedAna Twin combines Hub data with physics, domain science and modality-trained AI/ML. Explore process behavior and operating windows from bench to commercial scale, with evidence and uncertainty attached.
Predict and simulate — from bench to scale.
TWIN USE CASES / THE DECISION COMES FIRST
Explore scale-up, transfer and process recovery workflows, alongside scoped facility ramp-up and line-modeling engagements.
Editorial date
Compare candidate vessel conditions against oxygen transfer, mixing and process-performance evidence before committing an engineering run.
Engagement basis: Project-data process model and engineering assessment
Editorial date
Connect sending-site history, receiving-asset differences and bounded model comparisons to focus the next transfer study.
Engagement basis: Project-data comparison and process-model evaluation
Editorial date
Compare upstream performance, purification recovery and explicit cost assumptions to choose a better-supported process experiment.
Engagement basis: Process-train scenario evaluation
Editorial date
Use a staged evidence and modeling program to compare design assumptions, commissioning results and early operating constraints.
Engagement basis: Scoped engineering engagement with project-specific facility models
Editorial date
Frame the evidence and simulation needed to compare throughput losses, changeovers, buffers and product mix across a sterile line.
Engagement basis: Scoped line-modeling engagement and source-system integration
Editorial date
Connect the development baseline, vessel engineering and product-quality evidence to decide which large-scale scenario deserves physical confirmation.
Engagement basis: Project-specific CHO fed-batch process model, equipment assessment and staged scientific validation
01 — THE OPPORTUNITY
“Bioprocess development is still constrained by physical experimentation. Complex biology makes every run valuable, but much of what is learned remains difficult to reuse.”
Today, teams rely on run → analyze → learn → adjust — another physical run to validate the next change. That means more experiments, slower learning and knowledge fragmented across data, models and SME memory. The opportunity: learn more from every run, and predict before the next one.
02 — SCALE CHANGES MORE THAN THE REACTOR
From bench and development scale, through pilot, to commercial scale — the scale-up challenge is finding the operating conditions that preserve biology and product quality as the engineering environment changes.
High mixing homogeneity, short gradients and kLa headroom. The process behaves as one well-mixed volume.
Mixing takes longer and pCO₂ and DO gradients start to form. The same feed strategy sees a slightly different cell environment.
Oxygen transfer and CO₂ stripping become scale-limited; shear and gradient trade-offs dominate the operating window.
Same model, carried evidence. Predicted titer 5.11 g/L, inside the validated operating window.
03 — SCIENTIFICALLY CONSTRAINED
BioMedAna brings together physics, chemistry and biology data, AI/ML, agentic workflows and domain knowledge to explore the next process decision.
Bio Twin brings together mechanistic models, chemistry and biology data, AI/ML, agentic coordination and domain expertise to support higher-value experiments, operating-window exploration, scale-up assessment and earlier risk review.
04 — HUB + TWIN, THE CLOSED LOOP
BioMedAna Hub continuously connects, contextualizes and aligns process data so BioMedAna Twin can predict, simulate and improve with every run — governed end to end by BioMedAna OS.
Bioreactors, historians, PAT, ELN/LIMS, MES and lab data.
05 — MODALITY-SPECIFIC INTELLIGENCE
BioMedAna Twin keeps the platform core consistent while adapting scientific context, models and decision logic to each modality it predicts for.
Connect infection conditions to harvest and quality evidence. Data-driven predictions require held-out validation; mechanistic infection scenarios are not project-calibrated.
06 — PREDICTION WITH A REASON
BioMedAna Twin keeps predictions connected to experimental evidence, process constraints, model version and uncertainty—so teams can challenge the answer before acting on it.
07 — SEE THE WHOLE SPACE
Single-point predictions hide the trade-offs. Twin maps predicted performance across the full operating space — showing where the process is robust, where it is fragile, and where the next experiment would teach the most.
3D view · use the controls to explore the concept.
↔ DRAG TO EXPLORE08 — THE PIPELINE, RUNNING LIVE
The same node-to-node execution shown in the real Twin canvas — data, engineering calculations, simulation, scoring and optimization feeding forward continuously into a single reviewable decision.
Connected data, engineering, simulation and decision nodes — run continuously in a loop, one node at a time, exactly as the real canvas executes.
Starting a new run…
09 — REAL PRODUCT
Align batch trajectories, feed events and assay results before drawing a conclusion.
Association in illustrative data; not a root-cause conclusion.
Compare model scenarios, assumptions and uncertainty before committing material.
A study proposal, its assumptions and its evidence—ready for your experts.
Day 3, 3 g/L/day: predicted 5.3 ± 0.30 g/L (illustrative)
Confirm safe ranges, controls, randomization and statistical power.
10 — TRY IT YOURSELF
This sandbox mirrors how BioMedAna Twin frames a scenario — inputs on the left, predicted response on the right, and a confidence signal attached to every result. No data leaves this page and nothing writes back to a control system.
Explore the operating space. No control-system writeback occurs from this demo.
Feed +4% · DO ≥ 32% · agitation constrained
11 — HUMAN-GOVERNED
BioMedAna produces evidence-linked recommendations with uncertainty, review context and approval gates—not invisible automation.
Explore governance ↗12 — ONE PLATFORM, MANY DECISIONS
BioMedAna applies the same connected data, modality context and predictive intelligence across the full process lifecycle — and into manufacturing support for deviations, root-cause analysis and continuous improvement.
Design better experiments
Predict larger-scale behavior
Tune operating conditions
Anticipate CQAs and performance
Carry process knowledge forward
13 — THE EXPERIMENTAL DIVIDEND
A physical DoE across feed, DO and agitation can consume a quarter of experiments. Twin sweeps that space in silico against models, physics constraints and prior evidence — so the committed runs are chosen, not guessed.
Feed, DO, agitation, temperature and scale combinations simulated against models and prior evidence
Ranked by predicted outcome, uncertainty and engineering constraint headroom
High-value experiments approved through governed scientist review
Illustrative workflow — the point is the shape: explore widely in silico, commit narrowly in the lab.
14 — BUILT FOR PROCESS DECISION-MAKERS
Design fewer, higher-value experiments
De-risk scale-up, transfer and comparability
Anticipate batch behavior and investigate deviations
Govern data, models and integration with the existing stack
Review predicted quality outcomes with traceable evidence
Understand development risk, process maturity and capacity implications
It is a model of a process connected to relevant process evidence. Teams use it to test scenarios and estimate outcomes such as yield, quality or scale-up behavior within a defined use case.
BioMedAna combines process science and physics constraints with data-driven methods. Each model still needs validation on representative data and clear limits for its intended use.
BioMedAna positions Twin outputs as evidence-linked recommendations for human review. Any control integration or authorized action depends on deployment scope and governance.
Start with one decision
Define the evidence, physics, model and validation path needed to support your next scale-up, transfer or PPQ decision.
Design a Twin pilot ↗