Systems Medicine is an integrative approach that brings together biology, medicine, computation and biotechnology and uses mathematical modelling to study the complex interactions in systems. It understands diseases as complex events and considers them in their entirety. Systems Medicine is about looking at the big picture and how disease mechanisms are interrelated.
At the DZNE we aim to develop holistic approaches to gain new insights into diseases such as Alzheimer's, Parkinson's or amyotrophic lateral sclerosis (ALS). To achieve this aim, we leverage data derived from high-throughput methods and combine them with knowledge derived from clinical and population-based data as well as fundamental-science driven findings.
Such endeavor requires strong team efforts between many different disciplines including not only the life and medical sciences but also the computer and data sciences. In addition to the outstanding research on neurodegenerative diseases already performed at the DZNE, Systems Medicine intends to provide complementary approaches - mainly data-driven. That allows a more systemic or holistic view on the complex molecular mechanisms involved in neurodegenerative diseases, but also other chronic diseases of our aging population.
As biomedical research is becoming more and more data-driven, Systems Medicine will contribute important tools and approaches to foster a better understanding of neurodegenerative diseases but also to support novel treatment strategies through novel applications in drug discovery or drug repurposing.
Initially, the research area Systems Medicine consists of these domains:
Biotechnology and Biomedicine
The cell is the biological unit of life. Understanding the molecular mechanisms of each individual cell contributes to the understanding of life, but also the development and progression of diseases. In the past decade, technologies to comprehensively measure molecular phenotypes of thousands of cells have become a reality. We call this approach the “molecular microscope”, which is a collection of technologies allowing for an assessment of dozens to thousands of cell parameters in thousands of single cells simultaneously. The biotechnology and biomedicine domain at Systems Medicine will develop, establish and provide cutting-edge technologies. This includes single cell omics, and other high-throughput single cell technologies such as multi-color flow cytometry to scientists at DZNE.
In the future, medicine could make use of molecular and other data generated to describe molecular changes observed in diseases. The overall goal will be to make precise decisions based on large enough data derived from each individual patient. The concept of precision medicine would then allow to overcomes the usual “one-size-fits-all” treatments and use the large molecular data and other information, such as from magnetic resonance imaging, to develop drugs and treatments for subgroups of patients.
New developments in the computer sciences including artificial intelligence (AI) and machine learning (ML) are major tools for achieving the goals in precision medicine. At Systems Medicine we envision to leverage these approaches for the identification of disease-associated patterns derived from molecular phenotypes established on the single cell level. In other words, using the information derived from thousands of single cells combined with AI and machine learning, we want to develop novel data-driven approaches to precisely define the disease for each patient individually.
This is what we would call precision medicine.
Information and Data Science
Data generation, extraction and analysis is crucial for Systems Medicine. Within this domain, we will develop the computational foundation for Precision Medicine. First of all, this includes AI, ML and statistical methods development. But at DZNE, we do not stop here, rather we also invest in research developing revolutionizing computer architectures like Memory-Driven Computing or AI methods such as Swarm Learning that allow us to tailor solutions from the information and data sciences to medical research and clinical applications.