Malte Borggrewe PhD
My work sits at the intersection of biological insight and computational analysis, interpreting complex multi-omics datasets and translating them into conclusions that drive research strategy and discovery decisions.
With deep experience across multi-omics, I work with teams at both the analytical and strategic level: understanding cellular systems, regulatory mechanisms, and disease processes, and ensuring that data interpretation informs the decisions that matter. Whether leading a project or providing senior oversight, I bring the scientific rigour and strategic perspective needed to move from data to direction.
Omics technologies are playing an increasingly central role in early-stage discovery research. I work with biotech and pharma teams to analyse and interpret complex datasets to drive target identification, compound prioritisation, and mechanism-of-action studies.
Translating biological insight into clinical strategy demands both scientific rigour and the ability to align cross-functional teams around a shared data narrative. I have guided research teams through the design and interpretation of omics-driven studies, identifying biomarkers, characterising patient heterogeneity, and shaping the scientific rationale for clinical programmes.
Machine learning and AI are fundamentally changing how biological data is analysed at scale. I actively follow this space and integrate AI-driven approaches into my work, advising teams on where these methods add genuine value, how to evaluate emerging tools, and how to build data workflows that are fit for purpose as the field evolves.
Much of my work has centred on biological systems where multi-omics data is essential to understand complex cellular behaviour and disease mechanisms. The domains below represent areas of particular depth, though my experience extends across a broader range of biological contexts.
Understanding immune cell states, differentiation processes, and immune responses across tissues and disease contexts, from foundational mechanistic studies to translational applications in drug discovery and patient stratification.
Studying the interactions between immune and neural systems, including cellular mechanisms underlying neuroinflammation and brain disease, where multi-omics approaches are increasingly critical to resolving disease complexity.
Identifying regulatory programmes and biological pathways underlying complex disease phenotypes, with a focus on generating interpretable, decision-relevant insights that inform research strategy and target prioritisation.
Analysis and infrastructure tools I work with regularly across projects.
Seurat, DESeq2, edgeR, limma, Bioconductor, CATALYST, Seumetry, Monocle, Slingshot, SCENIC, Shiny
Scanpy, Squidpy, Scirpy, Muon, scVI, decoupler, GSEA, AnnData,Pandas, NumPy
Nextflow, Docker, Git, Bash, Jupyter, Quarto
LLM engineering, AI-assisted coding
scikit-learn, TensorFlow, PyTorch
Computational analysis is only valuable if it drives better decisions. Across all projects, my focus is on understanding what the data means for the biological system under study and ensuring those findings are translated into clear, actionable conclusions.
This means identifying meaningful signals within complex datasets, connecting results across technologies and omics layers, and delivering interpretations that inform experimental direction, research strategy, and discovery decisions.
