Industrial Biotechnology
Solutions
Scale bioproduction from lab to factory with AI-driven bioprocess optimization. Predict fermentation dynamics, optimize media composition, and reduce scale-up failures.
The Challenge of Scaling Bioproduction
Industrial biotechnology promises sustainable alternatives to petrochemical manufacturing, but the path from lab-scale success to commercial production is fraught with failures. A strain that performs well in a shake flask often behaves unpredictably in a 10,000-liter bioreactor, where mixing gradients, oxygen transfer limitations, and metabolic stress create conditions that are fundamentally different from the controlled laboratory environment.
Scale-up failures are costly and common. Industry estimates suggest that 70% of bioprocesses that work at bench scale fail to reach commercial viability, often due to problems that emerge only at larger volumes: foam formation, contamination susceptibility, inconsistent product quality, and declining productivity over extended fermentation runs. Each failed scale-up campaign wastes months of engineering effort and millions in capital investment.
Current bioprocess modeling tools struggle to bridge the gap between strain genetics and fermentation performance. Empirical approaches require extensive pilot-scale experimentation, while mechanistic models demand parameterization data that is expensive to generate. The industry needs predictive tools that can anticipate scale-up challenges and optimize process conditions before committing to large-scale production runs.
How Avitai Powers Industrial Biotech
Our foundation models connect strain genetics to process performance, enabling predictive scale-up and process optimization.
Scale-Up Prediction
Predict how fermentation performance will change at larger scales by modeling mass transfer, mixing dynamics, and metabolic responses to environmental gradients.
Process Optimization
Optimize feeding strategies, temperature profiles, and media composition to maximize productivity while minimizing raw material costs and batch variability.
Real-Time Monitoring
Deploy digital twin models that predict fermentation state from online sensor data, enabling early detection of process deviations and proactive intervention.
Strain-Process Co-Design
Simultaneously optimize strain genetics and process conditions to find combinations that deliver robust, reproducible production at industrial scale.
Foundation Models for Industrial Biotech
Three models bridge the gap from strain design to production scale.
Perturbation Model
Optimizes strain robustness under industrial conditions by predicting how genetic modifications affect performance across varying environmental stresses. Designs strains that maintain high productivity under the nutrient gradients, pH shifts, and oxygen limitations encountered at production scale.
Learn moreDynamics Model
Models fermentation dynamics with physics-informed neural networks that respect mass balance, thermodynamic constraints, and transport phenomena. Predicts time-course profiles of biomass, substrate consumption, and product formation across different bioreactor configurations and operating conditions.
Learn moreResearch Model
Aggregates knowledge from fermentation science literature, process patents, and industrial case studies. Identifies proven scale-up strategies, common failure modes for specific organism-product combinations, and process parameter ranges from prior commercial bioprocesses.
Learn moreOpen-Source Tools for Industrial Biotech
Build custom bioprocess modeling and optimization pipelines with our JAX ecosystem.
Physics-informed neural networks for fermentation dynamics and transport modeling
Benchmarking and profiling for bioprocess model performance evaluation
Data pipelines for integrating bioreactor sensor data and process analytics
Infrastructure for deploying and serving bioprocess prediction models
Scale Your Bioprocess with Confidence
Reduce scale-up risk and optimize production economics with AI-driven bioprocess intelligence.