The AI4B.io Lab centers its research on developing a deep understanding of how novel AI technology can strengthen the effectiveness and efficiency of research and business processes in the biotech industry.
To drive these advancements, the lab leverages key technical AI components, focusing primarily on:
With a commitment to multidisciplinary research, specifically prioritizing explainable AI.
The AI4B.io Lab is dedicated to bridging the gap between advanced artificial intelligence research and industrial biotechnology applications.
The lab’s mission and vision are to build the fundamental knowledge base, scientific theories, and AI tools necessary to develop biobased products and optimize large-scale biobased production technologies.
The impact of the lab lies in accelerating a comprehensive digital transformation across the biotech industry, achieved by validating AI breakthroughs directly in complex industrial settings alongside industry partners, fostering young scientific entrepreneurship, and embedding explainable AI frameworks to build trust across the bioscience ecosystem.
Digital twin and smart plant scheduling – Developing an AI-based framework/advisory tool for optimal batch scheduling to maximize plant output of an enzyme production line, and balance product performance and quality against operational costs.
Digital twin for large-scale fermentation – Evaluating how AI methods can be applied to develop an accurate real-time simulation of fermentation performance indicators in response to dynamic process inputs for industrial scale fed-batch operated fermentation units of 50-500 m3.
Digital twin of lab automation processes and self-learning platforms – Developing autonomous systems that evaluate and develop ‘self-learning’ experimental design algorithms, combine data-driven approaches with mechanistic modelling and other things.
Machine learning for genome-phenotype engineering – Evaluating and implementing so-called ‘representation learning’ methods on multi-omics data from strain development projects.
Machine learning for iterative metabolic engineering – Determining how AI/ML methods can support efficient exploration of a large solution space for (iterative) strain improvement.
Delft University of Technology (TU Delft) is the largest public technical university in the Netherlands, providing academic research expertise, faculty supervision, and educational resources across computer science and biotechnology.
DSM-firmenich is a global science-based company specializing in health, nutrition, and biosciences that provides industrial use cases, co-supervision, and real-world evaluation settings for the lab’s AI solutions.
