Systems Biology and Translation in Psychiatry Group

Research focus of the AG Systems Biology and Translation in Psychiatry

Advances in high throughput techniques have led to an increasing wealth of biological data, facilitating the description of human biology in health and disease in unprecedented detail. At the same time, the wealth of Omics data challenges our ways to treat, analyze, interpret, and translate data and insights into the clinical setting. The AG Systems Biology and Translation in Psychiatry works on new overarching analysis paradigms integrating deterministic modeling approaches with population statistics for translating insights from omics data into clinical practice in the realm of neuropsychiatric diseases. Currently, we are working on three levels of systems biological modeling.

Microbiome Modeling

The microbiome contributes crucially to human health and disease through its metabolic activity. We develop theoretical concepts allowing for the population statistics treatment of constraint-based community models and apply them to clinical data, enabling the functional characterization of the microbiome in detail. Statistical analyzes of personalized constraint-based community models additionally allow for novel insights into microbial ecology.

Integrative Whole Body Modeling

Based on the Virtual Metabolic Human database, Thiele et al. (2020) developed comprehensive organ-resolved, sex-specific whole body models, translating over 80000 metabolic reactions, human physiology, and the human microbiome into one mathematical formalism that enables personalized interrogation of metabolic phenotypes. It was successful in predicting known inborn errors of human metabolism and novel gene-metabolite associations discovered by genome wide association studies. We now explore the utility of whole body models for understanding the metabolic component of neurodegenerative diseases such as Alzheimer's disease or Parkinson's disease.

Cohort-based metabolic modeling of stress-related disorders and brain-aging

We develop methods to facilitate in silico modeling of large cohort data, aiming to generate virtual twins of the Study of Health in Pomerania and patient cohorts from the GANI_MED project via the utilization of personalized whole body models. Model parameters can then be utilized for predicting health outcomes and investigation of risk factors and etiological conditions for stress-related disorders and processes of brain-aging.

For more information please see AG Systems Biology and Translation in Psychiatry