Computational Neurobiology


Our team is studying failures in multi-omics regulatory networks in neurological, neurodegenerative, and mental disorders. The focal omics level in our analyses is the metabolome that we use as intermediate readout for disease risk, state, stage in progression, and resilience. Using advanced computational approaches, we use this readout and interface it with genomic, transcriptomic, and proteomic markers to build multi-level frameworks that can be interrogated to identify functional hypotheses across all available molecular and regulatory layers.

Our work is embedded in several international collaborations and consortium efforts funded by the National Institutes of Health (see Third-party funding). Our current research focus is on Alzheimer’s Disease (AD), where we are partners in the Alzheimer’s Disease Metabolomics Consortium (ADMC), the Accelerating Medicines Partnership – Alzheimer’s Disease (AMP-AD) and Molecular Mechanisms of the Vascular Etiology of Alzheimer’s Disease (M2OVE-AD) consortia, and Major Depressive Disorder (MDD) within the Mood Disorders Precision Medicine consortium (MDPMC).

Research topics

  • Identification of metabolic network failures in AD and MDD
    Using data generated with a multitude of different metabolomics platforms, we are studying biochemical changes in AD and MDD to identify unique patterns across the trajectory of the diseases, as well as response to commonly prescribed medications.

  • Candidate target selection and prioritization in AMP-AD
    Using multi-omics techniques, we are developing multi-level frameworks for the identification and prioritization of novel targets for therapeutic intervention of AD. In this, we have started to build an integrated molecular atlas for AD that is anchored to the genome, and extend it by single-omics networks such as partial correlation and co-expression networks.

  • Identification of molecular risk factors and disease subtypes of AD in M2OVE-AD
    Using in-depth phenotypic measures, we are harnessing metabolic markers to identify common risk factors in AD, cardiovascular and metabolic disease (such as type 2 diabetes) that increase to vulnerability of brain health. Applying stratified analyses in different risk groups, as well as unbiased machine learning approaches, we are aiming at defining molecularly distinct subtypes of AD to enable precision medicine approaches.

More details are provided in the Projects section.

Tools and web services

  • SNiPA
    A versitale tool for annotating and browsing genetic variants (SNPs and SNVs). Contains extensive data on variant associations across omics, i.e. epigenetic marks, expression QTLs, protein QTLs, metabolite QTLs, as well as phenotype and disease associations.

  • Agora
    The AMP-AD candidate target nomination and prioritization platform. The ADMC led by Duke University (Durham, NC, USA) is one of seven contributing multi-site partners that deposit their annotations for potential novel targets of AD.

More details are provided in the Web services section.