Biosystems & Data Science Team
Deploying advanced data analytics to elucidate complex biological systems.

Research Focus
We harness the power of data science to address intricate biological questions, by combining machine learning and mechanistic models with information-rich datasets from omics and process analytical technologies.
We leverage and integrate state-of-the art tools and algorithms to assemble a quick, powerful approach tailored specifically to each research question and biological system. We empower our partners by creating intuitive graphical interfaces and applications where they can become independent users of sophisticated approaches to explore their data.
We aim to champion digitalization and data-centric approaches to accelerate the path to innovation in iBET’s research.
Areas of Activity
Bioprocess Optimization for Cell and Gene Therapy
Bioprocessing 4.0 is set to revolutionize bioprocess development, propelling it into a new era of efficiency and speed. Using digital bioprocessing, with the creation of digital bioprocess replicas, we drastically reduce experimental burden and expedite process optimization. We take advantage of omics technologies and real-time monitoring to provide a wealth of bioprocess insights.
Precision Medicine
We exploit machine learning to decode data from patient samples, advanced cell models, and clinical information, to identify biomarkers that determine different responses to therapy. This informs clinical strategies and paves the way for the discovery of novel targets in precision drug development.
Advanced Analytics for Drug Discovery
Combining automated processing and advanced visualizations of high-throughput datasets with automatic retrieval of relevant information from public databases, we can place critical insights at the fingertips of experts, thus streamlining decision-making at different steps of the drug discovery pipeline.




Inês Isidro
Head of Biosystems and Data Science
My group uses data science to empower research in complex biological systems. We combine machine learning, mechanistic modelling, systems biology, and other advanced data analytics concepts and approaches to design data-driven strategies tailored to each specific research question, often integrating data from omics and process analytical technologies.