Multi-scale organization of biological systems

 

Biological systems are strictly orchestrated through communications and interactions across the hierarchy of biological scales. With the vast amount of high-throughput biological data that have become increasingly available, these multi-scale interactions can be universally observed, from genetic and epigenetic lesions, sub- and intercellular interactions among proteins and metabolites, to physiological crosstalks on tissues and organs level. Furthermore, external influences such as social and environmental interactions, which can in turn affect the very basic genetic and epigenetic processes have also been extensively studied. These different levels of biological organization, cascades of effects that lead to multitudes of phenotypic effects, and information flows across the hierarchy can be captured via a multi-scale network. The concept has been formulated and successfully applied in different fields from urban organization to epidemiology, which can be benefited in understanding the complexity of health and diseases.

 

 

 

Our projects in this direction are currently aimed towards systematic understanding of rare diseases. Rare genetic diseases are often a result of just a single genetic lesion. Affected patients can however experience systemic, and often severe phenotypes. 

 

On the one hand, we are interested in basic understanding of disease phenomena such as:

- How does one genetic variant result in very systemic phenotypes and comparable to multifactorial & complex diseases? Can we track the flow of (mis)information across multi-scale networks that trigger disease state at the phenotypic level?

- How do different variants from the same gene lead to different phenotypic effects? Are there genetic modifiers?

- Are the topological neighbourhoods of disease genes, known as disease modules, observable across different scales?

 

On the other hand, we ultimately would like to leverage this understanding to facilitate clinical diagnostics of rare disease patients. Knowledge and resources around rare diseases are often scarce, leading to many rare disease patients living without diagnosis, and consequently without efficient treatments. Our network-based approaches and algorithms can power variant prioritization processes, to identify potential causal variants and accelerate therapeutic discoveries.