Links condivisione social

Networks for complex diseases

Christina Kiel, PI



The main aim of our research is to understand how complex behaviours in a biological or diseased state (phenotype) can be predicted and manipulated at a systems network level.

In contrast to classical reductionist science, that focuses on a detailed characterization of one protein, one organelle, or one process within one organelle, systems biology seeks to determine how biological systems work on a more holistic level.

System networks can be studied and represented on multiple levels, including signal transduction pathways, protein-protein interaction (PPI) networks, metabolism, or gene regulatory networks. Although it is pragmatic to consider networks in these different subcategories, the reality is that the delivery of a given phenotype is a function of many (likely all) of these, and therefore, studying one class of networks in isolation will always be incomplete.

The key ambition of our work is to understand how these different classes of networks interact in concert with one another to create specific phenotypes. As it is becoming increasingly possible to determine these networks in a quantitative manner and to avail of (patho)physiologically relevant model systems, the approach that we are taking is to combine bioinformatics, experimental, quantitative computational modelling, and protein engineering approaches to deliver a framework that describes networks in the context of complex biology (phenotypes) and disease.