SCIENTIFIC EVIDENCE

The science behind
better process decisions.

Explore independent research on hybrid models, digital twins and process optimization through visual study reports — with clear sources, findings and limits.

01 — RESEARCH SHOWCASE

Explore the science.
See the approach.

Explore five research reports with guided BioMedAna Assist workspaces: example requests, model calculations, charts and implementation details. Each report includes data and units, model equations, calculation steps, result tables, evaluation criteria and implementation downloads. The CHO intensification report also explores three operating modes and parameter sensitivity.

METHOD EXPLAINER / 2022From metabolism to decisionsA model-based feedback framework01Cell populations02Inhibitor balance03Exchange + bleed04Operating windowOriginal BioMedAna illustration · conceptual workflow · no plotted study results

PROCESS OPTIMIZATION · 2022

CHO intensification: from fed-batch to controlled perfusion

An executable 30-day CHO model report with operating-mode comparisons, controlled bleed, parameter variation, sensitivity tables and downloadable calculations.

Anne Richelle, Brandon Corbett, Piyush Agarwal, Anton Vernersson, Johan Trygg and Chris McCreadyExplore the study
METHOD EXPLAINER / 2024Knowledge + observationsA structured path to process prediction01Process knowledge02Culture data03Hybrid model04Culture outputsOriginal BioMedAna illustration · conceptual workflow · no plotted study results

HYBRID MODELING · 2024

Predicting cell growth, titer and glucose demand

A CHO study combines process knowledge and data-driven modeling to predict culture performance across selected conditions.

Bhagya S. Yatipanthalawa, Shauna E. Wallace Fitzsimons, Tizita Horning, Yih Yean Lee and Sally L. GrasExplore the study
METHOD EXPLAINER / 2025Reuse. Adapt. Evaluate.Learning across related biological systems01Source-strain data02Hybrid model03Transfer + new data04Adapted predictionOriginal BioMedAna illustration · conceptual workflow · no plotted study results

TRANSFER LEARNING · 2025

Adapting a bioprocess model to a new strain

A microalgal lutein-production study explores model adaptation when experimental data for a new strain are limited.

Luca Riezzo, Harry Kay, Yansong Feng, Keju Jing and Dongda ZhangExplore the study
METHOD EXPLAINER / 2023From metabolism to decisionsA model-based feedback framework01Metabolic targets02Reactor state03Feed optimization04Emulator feedbackOriginal BioMedAna illustration · conceptual workflow · no plotted study results

PROCESS OPTIMIZATION · 2023

Connecting cell metabolism to feeding decisions

A multiscale CHO model connects metabolic objectives with model-predictive feeding strategies evaluated in a digital-twin emulator.

Mariana Monteiro, Sarah Fadda and Cleo KontoravdiExplore the study
METHOD EXPLAINER / 2024Structure + learningPhysical constraints in a learned model01Physical structure02Process data03Parameter learning04Dynamic predictionOriginal BioMedAna illustration · conceptual workflow · no plotted study results

PHYSICS-INFORMED MODELING · 2024

Learning process dynamics while preserving physical structure

A CHO modeling study combines physical constraints, metabolic kinetics and machine learning to identify process dynamics.

Tianqi Cui, Tom Bertalan, Nelson Ndahiro, Pratik Khare, Michael Betenbaugh, Costas Maranas and Ioannis G. KevrekidisExplore the study
METHOD EXPLAINER / 2018Design. Test. Learn.Connect each result to the next question01Design candidates02Build + test03Learn response04Next experimentOriginal BioMedAna illustration · conceptual workflow · no plotted study results

EXPERIMENT PLANNING · 2018

Turning experiment results into the next design decision

An automated microbial-engineering pipeline connects computational design, experimental work and learning across iterations.

Pablo Carbonell, Adrian J. Jervis, Christopher J. Robinson and colleaguesExplore the study
Independent science. Clear attribution.

Each report separates the original research from BioMedAna's own educational implementation. Live charts use synthetic data or illustrative parameters, clearly labelled alongside the equations and code. They do not imply the researchers used BioMedAna or reproduce their experimental results.

02 — SCIENTIFIC METHODS IN BIOMEDANA

From process models to reviewable evidence.

These descriptions reflect methods and evidence structures in BioMedAna's codebase. They describe implementation, rather than measured customer outcomes or independent product validation.

CHO kinetics with a tested data-driven correction

The CHO hybrid model fits mechanistic growth, viability, nutrient and product relationships to completed project runs. A Gaussian-process correction is retained for selected outputs only where leave-one-run-out evaluation improves prediction error.

Interpretation & scope
Model assumptions include how feeding is represented. Held-out checks support the evaluated runs and outputs; they do not establish reliability outside the tested operating range.

Reaction networks, mass balances and temperature effects

Reaction modeling represents species and pathways, temperature-dependent kinetics, and batch or semi-batch mass and energy balances. Parameter estimation and held-out validation help assess how well a configured model describes the available observations.

Interpretation & scope
Predictions depend on the configured reaction network, parameter estimates and source data. Simplified engineering correlations and demo scenarios require process-specific calibration.

Trace a scientific value to its source

Source evidence links a mapped value to its original file, sheet, row and column, including units, mapping version and transformation steps. Reviewers can inspect the origin and transformation behind a scientific input.

Interpretation & scope
Complete lineage establishes traceability. Scientific correctness still depends on the selected data contract, mapping, validation rules and expert review.

Review the evidence behind a model version

Model evidence packs distinguish calibration fit, held-out evaluation, forecast backtests and prospective results where available. They bring together data versions, model fingerprints, assumptions, lifecycle records and control gaps for quality review.

Interpretation & scope
Evidence availability depends on the selected model and workflow. An evidence pack supports review; it does not certify the system or replace intended-use validation and customer procedures.

03 — PROCESS UNDERSTANDING & QUALITY

Guidance that informs the approach.

These documents provide scientific and regulatory context. They do not approve a particular AI platform or replace process-specific validation.

FDA · 2004

PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance

A framework for process understanding and measurement in pharmaceutical development, manufacturing and quality assurance.

Read the guidance

ICH guidance published by FDA · 2009

Q8(R2) Pharmaceutical Development

Describes pharmaceutical development and quality-by-design principles, providing context for a science-based approach to process understanding.

Read the guidance

04 — APPLYING THE SCIENCE

Make the evidence specific to your process.

Define the intended decision, compare predictions with held-out runs, assess uncertainty and operating limits, and document expert review before relying on model outputs.

Sources and implementation descriptions reviewed October 13, 2026. Publisher access may vary.