AI-driven modelling for microbial growth
Company Name
Differential Bio
Founded
2023 June
Cohort
Berlin Cohort 1
City
Munich
Country
Germany
Founders & Roles
Chris Spier – CEO
Martin Patz – CTO
Web
https://www.differential.bio/
The Solution
Differential Bio combines automated experimentation with predictive AI to improve process development speed and accuracy.
Their robotic lab generates industry-relevant data through a closed-loop system of liquid handling, fermentation, and measurement. This data is then used to train specialised AI models that predict key outcomes such as biomass growth, protein expression, yield, and productivity.
This technology is utilised in their web-based platform that allows customers to run unlimited virtual experiments and define optimisation targets such as growth, yield, or cost. Differential Bio then simulates process conditions and recommends the most effective bioprocess parameters before physical scale-up begins.
The Problem
Scaling bioprocesses from lab to industrial production remains one of the biggest bottlenecks in biotechnology. Many novel bioproducts take 5–10 years to reach commercialisation, with each optimisation cycle often taking several weeks when run manually.
Results achieved at lab scale frequently fail to translate to manufacturing scale, where challenges such as oxygen transfer, pH gradients, heat distribution, contamination risk, and inconsistent mixing can reduce efficiency and yield.
Slow experimentation also means companies can spend years operating with suboptimal processes that consume excess water, energy, nutrients, raw materials, and cleaning cycles, increasing both costs and environmental impact.
Impact
By replacing slow manual iteration with rapid digital optimisation, Differential Bio helps customers reduce development time, lower risk, and improve commercial performance. Its platform enables processes to be tested 10x faster, accelerating the path to scalable production. This reduces the need for expensive fermentation trials, which results in a 3x reduction in R&D costs and an optimised bioprocess in as little as 4 weeks.
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