Feature Release : AI-Powered Media Optimization

How BioReact Is Transforming Media Optimization With AI/ML

Optimizing media and nutrients for cell growth is one of the most critical—and time-consuming—challenges in bioprocessing. Traditional trial-and-error methods stretch timelines and budgets. BioReact’s new simulation model changes that by using AI to predict optimal conditions, helping teams increase yields, reduce experiments, and make faster, data-driven decisions.

The Challenge: Media Optimization Slows Progress

Cell growth and productivity are tightly linked to nutrient and media composition. Whether you’re culturing mammalian cells or fermenting microbes, the wrong inputs can lead to inconsistent results and high production costs. Yet optimizing these variables remains manual, repetitive, and slow.

BioReact’s solution automates this step, accelerating the DBTL (Design-Build-Test-Learn) cycle and improving outcomes at every stage.

How Our Simulation Model Works

Predictive Modeling Engine

Our simulation model uses AI/ML to predict outcomes based on data the user provides to train the model. Our model allows for up to 10,000 in-silico experiments per optimization. Here’s how it works:

Users upload data and/or background information such as:

• Organism (e.g., CHO, E. coli)

• Media composition

• Feeding strategy

• Temperature, pH, DO

The model outputs projected:

• Biomass (OD600)

• Cell viability

• Product titer

These insights help teams identify optimal conditions before running actual experiments.

Integrated Data Visualization

BioReact merges offline and online data from past experiments. All results are visualized in intuitive plots, making it easy to compare conditions and identify the best formulations quickly.

Key Benefits for Bioprocess Teams

Faster Results

Simulation cuts down on unnecessary experiments, speeding up process development.

Higher Reproducibility

AI standardizes inputs and decisions, reducing variability between runs.

Better Scalability

Formulations can be optimized for both bench-scale and production-scale volumes.

Lower Costs

Less trial-and-error means fewer resources and more efficient development.

Why This Matters Now

The biotech industry is growing rapidly—expected to reach $800 billion by 2030. Yet much of the R&D timeline is still wasted on data wrangling and manual experimentation. BioReact helps teams move faster with fewer mistakes, giving them a competitive edge in an increasingly crowded field.

Cross-Industry Applications

• Biopharma: Model feed strategies for CHO cell antibody production

• Industrial Biotech: Optimize microbial fermentation for enzymes and biofuels

• Cultured Meat: Simulate serum-free formulations for animal cell growth

Smarter Bioprocessing Starts Here

BioReact’s simulation model is a leap forward for modern biotech labs. It enables scientists to simulate and optimize bioprocesses before they start, reducing time, increasing yield, and helping bring life-changing products to market faster.